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Intelligent Systems

Publications

2016
Emerencia, A. C., Krieke, L. V. D., Bos, E. H., de Jonge, P., Petkov, N., & Aiello, M. (2016). Automating Vector Autoregression on Electronic Patient Diary Data. IEEE Journal of Biomedical and Health Informatics, 20(2), 631-643. DOI: 10.1109/JBHI.2015.2402280
Strisciuglio, N., Azzopardi, G., Vento, M., & Petkov, N. (2016). Unsupervised delineation of the vessel tree in retinal fundus images. In Computational Vision and Medical Image Processing VIPIMAGE 2015. (pp. 149-155). Taylor & Francis Group.
Foggia, P., Petkov, N., Saggese, A., Strisciuglio, N., & Vento, M. (2016). Audio Surveillance of Roads: A System for Detecting Anomalous Sounds. Ieee transactions on intelligent transportation systems, 17(1), 279-288. DOI: 10.1109/TITS.2015.2470216
Schiza, E. C., Fakas, G. J., Pattichis, C. S., Petkov, N., & Schizas, C. N. (2016). Data Protection Issues of Integrated Electronic Health Records (EHR). In E. Kyriacou, S. Christofides, & C. S. Pattichis (Eds.), XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. (pp. 781-784). (IFMBE Proceedings; Vol. 57). Springer International Publishing.
Neocleous, A. C., Neocleous, C. K., Petkov, N., Nicolaides, K. H., & Schizas, C. N. (2016). Prenatal Diagnosis of Aneuploidy Using Artificial Neural Networks in Relation to Health Economics. In E. Kyriacou, S. Christofides, & C. S. Pattichis (Eds.), XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016: MEDICON 2016, March 31st–April 2nd 2016, Paphos, Cyprus. (pp. 930-934). (IFMBE Proceedings; Vol. 57). Springer International Publishing. DOI: 10.1007/978-3-319-32703-7_181
Strisciuglio, N., Azzopardi, G., Vento, M., & Petkov, N. (2016). Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters. Machine Vision and Applications, 27(8), 1137. DOI: 10.1007/s00138-016-0781-7
Guo, J., Shi, C., Azzopardi, G., & Petkov, N. (2016). Inhibition-augmented trainable COSFIRE filters for keypoint detection and object recognition. Machine Vision and Applications, 1-15. DOI: 10.1007/s00138-016-0777-3
Strisciuglio, N., Vento, M., & Petkov, N. (2016). Bio-Inspired Filters for Audio Analysis. In K. Amunts, L. Grandinetti, T. Lippert, & N. Petkov (Eds.), BrainComp 2015: Brain-Inspired Computing. (pp. 101). ( Lecture Notes in Computer Science (LNCS); Vol. 10087). Springer. DOI: 10.1007/978-3-319-50862-7_8
Fernandez Robles, L. (2016). Object recognition techniques in real applications [Groningen]: University of Groningen
Strisciuglio, N. (2016). Bio-inspired algorithms for pattern recognition in audio and image processing [Groningen]: University of Groningen
Melchert, F., Seiffert, U., & Biehl, M. (2016). Functional Representation of Prototypes in LVQ and Relevance Learning. In E. Merényi, M. J. Mendenhall, & P. O'Driscoll (Eds.), Advances in Self-Organizing Maps and Learning Vector Quantization. (Vol. 428, pp. 317-327). (Advances in Intelligent Systems and Computing). Springer. DOI: 10.1007/978-3-319-28518-4_28
Melchert, F., Seiffert, U., & Biehl, M. (2016). Funktionale Approximation von Spektraldaten zur Steigerung der Klassifikationleistung in GMLVQ. In M. Schenk (Ed.), 17. Forschungskolloquium am Fraunhofer IFF 2015. (pp. 49-54). Magdeburg, Germany: Fraunhofer-Institut für Fabrikbetrieb und Automatisierung IFF.
Melchert, F., Matros, A., Biehl, M., & Seiffert, U. (2016). The sugar dataset: A multimodal hyperspectral dataset for classification and research. In F-M. Schleif, & T. Villmann (Eds.), Machine Learning Reports: MIWOCI Workshop 2016. (Vol. 03, pp. 15). Bielefeld: Univ. of Bielefeld.
Melchert, F., Seiffert, U., & Biehl, M. (2016). Functional approximation for the classification of smooth time series. In B. Hammer, T. Martinetz, & T. Villmann (Eds.), Workshop New Challenges in Neural Computation 2016. (pp. 24-31). (Machine Learning Reports; Vol. 04/2016). Bielefeld: Univ. of Bielefeld.
Melchert, F., Seiffert, U., & Biehl, M. (2016). Functional Representation of Prototypes in LVQ and Relevance Learning. 165-166. Abstract from BNAIC 2016, Amsterdam, Netherlands.
Teeninga, P., Moschini, U., Trager, S. C., & Wilkinson, M. H. F. (2016). Statistical attribute filtering to detect faint extended astronomical sources. Mathematical Morphology - Theory and Applications, 1(1), 100–115. DOI: 10.1515/mathm-2016-0006
Moschini, U. (2016). Efficient morphological tools for astronomical image processing [Groningen]: University of Groningen
Talavera Martínez, E. (2016). Towards Emotion Retrieval in Egocentric Photo Stream. Abstract from Sixteenth International Conference on Computer Aided Systems Theory, Las Palmas, Spain.
Kiwanuka, F. N., & Wilkinson, M. H. F. (2016). Automatic attribute threshold selection for morphological connected attribute filters. Pattern recognition, 53, 59-72. DOI: 10.1016/j.patcog.2015.11.012
Wilkinson, M., Pesaresi, M., & Ouzounis, G. (2016). An Efficient Parallel Algorithm for Multi-Scale Analysis of Connected Components in Gigapixel Images. ISPRS International Journal of Geo-Information, 5(3), 22. DOI: 10.3390/ijgi5030022
Kiwanuka, F. N., & Wilkinson, M. H. F. (2016). Cluster Based Vector Attribute Filtering. Mathematical Morphology - Theory and Applications, 1(1), 116–135. DOI: 10.1515/mathm-2016-0007
Babai, M., Kalantar-Nayestanaki, N., Messchendorp, J. G., & Wilkinson, M. H. F. (2016). Tracking sub-atomic particles through the Attribute Space. Mathematical Morphology - Theory and Applications, 1(1), 175–188. DOI: 10.1515/mathm-2016-0009
Bosilj, P., Wilkinson, M. H. F., Kijak, E., & Lefèvre, S. (2016). Local 2D Pattern Spectra as Connected Region Descriptors. Mathematical Morphology - Theory and Applications, 1(1), 203–215. DOI: 10.1515/mathm-2016-0011
Gay, M., Kaden, M., Biehl, M., Lampe, A., & Villmann, T. (2016). Complex Variants of GLVQ Based on Wirtinger’s Calculus. In E. Merényi, M. J. Mendenhall, & P. O'Driscoll (Eds.), Advances in Self-Organizing Maps and Learning Vector Quantization. (Vol. 428, pp. 293-303). (Advances in Intelligent Systems and Computing). Springer. DOI: 10.1007/978-3-319-28518-4_26
Mudali, D., Biehl, M., Leenders, K., & Roerdink, J. (2016). LVQ and SVM Classification of FDG-PET Brain Data. In E. Merényi, M. J. Mendenhall, & P. O'Driscoll (Eds.), Advances in Self-Organizing Maps and Learning Vector Quantization. (Vol. 428, pp. 205-215). (Advances in Intelligent Systems and Computing). Springer. DOI: 10.1007/978-3-319-28518-4_18
Mwebaze, E., & Biehl, M. (2016). Prototype-Based Classification for Image Analysis and Its Application to Crop Disease Diagnosis. In E. Merényi, M. J. Mendenhall, & P. O'Driscoll (Eds.), Advances in Self-Organizing Maps and Learning Vector Quantization. (Vol. 428, pp. 329-339). (Advances in Intelligent Systems and Computing). Springer. DOI: 10.1007/978-3-319-28518-4_29
Biehl, M., Hammer, B., & Villmann, T. (2016). Prototype-based models in machine learning. Wiley Interdisciplinary Reviews. Cognitive Science, 7(2), 92-111. DOI: 10.1002/wcs.1378
Smedinga, R., Biehl, M., & Kramer, F. (Eds.) (2016). 13th SC@RUG 2016 proceedings 2015-2016. Groningen: Rijksuniversiteit Groningen.
Villmann, T., Kaden, M., Hermann, W., & Biehl, M. (2016). Learning vector quantization classifiers for ROC-optimization. Computational Statistics, 1-22. DOI: 10.1007/s00180-016-0678-y
Mudali, D., Biehl, M., Meles, S. K., Renken, R. J., García-García, D., Clavero, P., ... Roerdink, J. B. T. M. (2016). Differentiating Early and Late Stage Parkinson’s Disease Patients from Healthy Controls. Journal of Biomedical Engineering and Medical Imaging, 3(6), 33-43.
Moolla, A., Amin, A., Hughes, B. A., Arlt, W., Hassan-Smith, Z., Armstrong, M., ... Tomlinson, J. (2016). The urinary steroid metabolome as a non-invasive tool to stage non-alcoholic fatty liver disease. DOI: 10.1530/endoabs.44.OC1.4
Moolla, A., Amin, A., Hughes, B. A., Arlt, W., Hassan-Smith, Z., Armstrong, M., ... Tomlinson, J. (2016). The changing ‘steroid metabolome’ across the spectrum of non-alcoholic fatty liver disease. DOI: 10.1530/endoabs.41.GP173
Baranowski, E. S., Bunte, K., Shackleton, C., Taylor, A., Hughes, B. A., Biehl, M., ... Arlt, W. (2016). Steroid metabolomics for diagnosis of inborn steroidogenic disorders – bridging the gap between clinician and scientist through computational approaches. DOI: 10.1530/endoabs.44.P40
de Vries, G-J., Lemmens, P., Brokken, D., Pauws, S. C. . S., & Biehl, M. (2016). Towards Emotion Classification Using Appraisal Modeling. In Psychology and Mental Health: Concepts, Methodologies, Tools, and Applications. (pp. 552-572). IGI Global. DOI: 10.4018/978-1-5225-0159-6.ch023
Bhanot, G., Biehl, M., Villmann, T., & Zuehlke, D. (2016). Integration of Expert Knowledge for Interpretable Models in Biomedical Data Analysis (Dagstuhl Seminar 16261): Dagstuhl Reports. (Dagstuhl Reports; Vol. 6, No. 6). Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany. DOI: 10.4230/DagRep.6.6.88
Mukherjee, G., Bhanot, G., Raines, K., Sastry, S., Doniach, S., & Biehl, M. (2016). Predicting recurrence in clear cell Renal Cell Carcinoma: Analysis of TCGA data using outlier analysis and generalized matrix LVQ. In 2016 IEEE Congress on Evolutionary Computation (CEC). (pp. 656-661). IEEE. DOI: 10.1109/CEC.2016.7743855
Biehl, M., Mudali, D., Leenders, K. L., & Roerdink, J. B. T. M. (2016). Classification of FDG-PET Brain Data by Generalized Matrix Relevance LVQ. In K. Amunts, L. Grandinetti, T. Lippert, & N. Petkov (Eds.), Brain-Inspired Computing: Second International Workshop, BrainComp 2015, Cetraro, Italy, July 6-10, 2015, Revised Selected Papers. (pp. 131-141). (Lecture Notes in Computer Science; Vol. 10087). Cham: Springer International Publishing. DOI: 10.1007/978-3-319-50862-7_10
2015
Azzopardi, G., Strisciuglio, N., Vento, M., & Petkov, N. (2015). Trainable COSFIRE filters for vessel delineation with application to retinal images. Medical image analysis, 19(1), 46-57. DOI: 10.1016/j.media.2014.08.002
Schleif, F-M., Hammer, B., Monroy, J. G., Jimenez, J. G., Blanco-Claraco, J-L., Biehl, M., & Petkov, N. (2015). Odor recognition in robotics applications by discriminative time-series modeling. Pattern Analysis and Applications, 18(1). DOI: 10.1007/s10044-014-0442-2
Giotis, I., Molders, N., Land, S., Biehl, M., Jonkman, M. F., & Petkov, N. (2015). MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images. Expert systems with applications, 42(19), 6578-6585. DOI: 10.1016/j.eswa.2015.04.034
Strisciuglio, N., Foggia, P., Petkov, N., Saggese, A., & Vento, M. (2015). Reliable Detection of Audio Events in Highly Noisy Environments. Pattern Recognition Letters, 65(1), 22-28. DOI: 10.1016/j.patrec.2015.06.026
Strisciuglio, N., Azzopardi, G., Vento, M., & Petkov, N. (2015). Multiscale Blood Vessel Delineation Using B-COSFIRE Filters. In G. Azzopardi, & N. Petkov (Eds.), Computer Analysis of Images and Patterns. (Vol. 9257, pp. 300-12). (Lecture Notes in Computer Science; Vol. 9257).
Neocleous, A., Azzopardi, G., Schizas, C., & Petkov, N. (2015). Filter-Based Approach for Ornamentation Detection and Recognition in Singing Folk Music. In Computer Analysis of Images and Patterns. (Vol. 9256, pp. 558-569)
Fernandez Robles, L., Azzopardi, G., Alegre, E., & Petkov, N. (2015). Cutting Edge Localisation in an Edge Profile Milling Head. In Computer Analysis of Images and Patterns: Lecture Notes in Computer Science. (Vol. 9257, pp. 336-347). (Lecture notes in computer science; Vol. 9257). Springer.
Foggia, P., Petkov, N., Saggese, A., Strisciuglio, N., & Vento, M. (2015). Car crashes detection by audio analysis in crowded roads. In Advanced Video and Signal Based Surveillance (AVSS), 2015 12th IEEE International Conference on. (pp. 1-6). DOI: 10.1109/AVSS.2015.7301731
Guo, J., Shi, C., Azzopardi, G., & Petkov, N. (2015). Recognition of architectural and electrical symbols by COSFIRE filters with inhibition. In Computer Analysis of Images and Patterns. (Vol. 9257, pp. 348-358). (Lecture Notes in Computer Science). Springer.
Shi, C., Guo, J., Azzopardi, G., Meijer, J., Jonkman, M. F., & Petkov, N. (2015). Automatic differentiation of u- and n-serrated patterns in direct immunofluorescence images. In Computer Analysis of Images and Patterns. (Vol. 9256, pp. 513-521). (Lecture Notes in Computer Science). Springer.
Shi, C., Meijer, J., Guo, J., Azzopardi, G., Jonkman, M. F., & Petkov, N. (2015). Automatic Classification of Serrated Patterns in Direct Immunofluorescence Images. In Autonomous Systems 2015 - Proceedings of the 8th GI Conference. (pp. 61-69). (Fortschritt-Berichte VDI. Informatik/Kommunikationstechnik; Vol. 842). VDI Verlag.
Guo, J., Shi, C., Jansonius, N. M., & Petkov, N. (2015). Automatic Optic Disk Localization and Diameter Estimation in Retinal Fundus Images. In Autonomous Systems 2015 - Proceedings of the 8th GI Conference. (pp. 70-79). (Fortschritt-Berichte VDI. Informatik/Kommunikationstechnik; Vol. 842). VDI Verlag.
Gomez-Ojeda, R., Lopez-Antequera, M., Petkov, N., & Gonzalez-Jimenez, J. (2015). Training a Convolutional Neural Network for Appearance-Invariant Place Recognition.
Petkov, N., & Azzopardi, G. (Eds.) (2015). Computer Analysis of Images and Patterns: 16th International Conference, CAIP 2015, Valletta, Malta, September 2-4, 2015 Proceedings, Part I. (Lecture Notes in Computer Science; Vol. 9256). Springer International Publishing. DOI: 10.1007/978-3-319-23192-1
Schiza, E. C., Panos, G., David, C., Petkov, N., & Schizas, C. N. (2015). Integrated Electronic Health Record Database Management System: A Proposal. Studies in health technology and informatics, 213, 187-190. DOI: 10.3233/978-1-61499-538-8-187
Petkov, N. (2015). Predicting the erosion of the cathode material in PVD systems. Bulgarian Chemical Communications, 47, 177-181.
van Hateren, J. H. (2015). Active causation and the origin of meaning. Biological Cybernetics, 109(1), 33-46. DOI: 10.1007/s00422-014-0622-6
van Hateren, J. H. (2015). Extensive fitness and human cooperation. Theory in Biosciences, 134(3-4), 127-142. DOI: 10.1007/s12064-015-0214-6
van Hateren, J. H. (2015). The natural emergence of (bio)semiosic phenomena. Biosemiotics, 8(3), 403-419. DOI: 10.1007/s12304-015-9241-4
van Hateren, J. H. (2015). Causal non-locality can arise from constrained replication. EPL (Europhysics Letters), 112(2), [20004]. DOI: 10.1209/0295-5075/112/20004
Melchert, F., Seiffert, U., & Biehl, M. (2015). Polynomial Approximation of Spectral Data in LVQ and Relevance Learning. In B. Hammer, T. Martinetz, & T. Villmann (Eds.), Workshop on New Challenges in Neural Computation 2015. (pp. 25-32). (Machine Learning Reports; Vol. 03-2015).
Teeninga, P., Moschini, U., Trager, S., & Wilkinson, M. (2015). Improved detection of faint extended astronomical objects through statistical attribute filtering. In Mathematical Morphology and Its Applications to Signal and Image Processing. (LNCS ed., Vol. 9082, pp. 157-168). Springer. DOI: 10.1007/978-3-319-18720-4_14
Moschini, U., & Wilkinson, M. (2015). Viscous-hyperconnected attribute filters: A first algorithm. In Mathematical Morphology and Its Applications to Signal and Image Processing. (Vol. 9082, pp. 669-680). (LNCS), (Lecture Notes in Computer Science; Vol. 9082). Springer. DOI: 10.1007/978-3-319-18720-4_56
Teeninga, P., Moschini, U., Trager, S. C., & Wilkinson, M. H. F. (2015). Improving background estimation for faint astronomical object detection. In Image Processing (ICIP), 2015 IEEE International Conference on. (pp. 1046-1050). IEEE (The Institute of Electrical and Electronics Engineers). DOI: 10.1109/ICIP.2015.7350959
Moschini, U., Teeninga, P., Trager, S. C., & Wilkinson, M. H. F. (2015). Parallel 2D Local Pattern Spectra of Invariant Moments for Galaxy Classification. In G. Azzopardi, & N. Petkov (Eds.), Computer Analysis of Images and Patterns: Proceedings, 2015, Part 2. (pp. 121-133). (Lecture Notes in Computer Science; Vol. 9257, No. I). Springer. DOI: 10.1007/978-3-319-23117-4_11
Bolanos, M., Dimiccoli, M., Talavera Martínez, E., Aghaei, M., Nikolov, S. G., & Radeva, P. (2015). SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation. Computer Vision and Image Understanding, 155, 55-69. DOI: 10.1016/j.cviu.2016.10.005
Talavera Martínez, E. (2015). Towards Unsupervised Familiar Scene Recognition in Egocentric Videos. In Autonomous Systems 2015 - Proceedings of the 8th GI Conference. (pp. 80-91). (Fortschritt-Berichte VDI. Informatik/Kommunikationstechnik; Vol. 842, No. 0178-9627). VDI Verlag.
Talavera Martínez, E. (2015). R-clustering for egocentric video segmentation. 327-336. Paper presented at 8th Iberian Conference on Pattern Recognition and Image Analysis, Spain.
Babai, M., Kalantar-Nayestanaki, N., Messchendorp, J., & Wilkinson, M. H. F. (2015). Tracking Sub-atomic Particles Through the Attribute Space. In Mathematical Morphology and Its Applications to Signal and Image Processing: Lecture Notes in Computer Science. (LNCS ed., Vol. 9082, pp. 86-97). [Chapter 8] (Mathematical Morphology and Its Applications to Signal and Image Processing; Vol. 9082). Springer. DOI: 10.1007/978-3-319-18720-4_8
Bosilj, P., Wilkinson, M., Kijak, E., & Lefevre, S. (2015). Local 2D Pattern Spectra as Connected Region Descriptors. In Mathematical Morphology and Its Applications to Signal and Image Processing: Lecture Notes in Computer Science. (LNCS ed., Vol. 9082, pp. 182-193)
Bosilj, P., Kijak, E., Wilkinson, M. H. F., & Lefèvre, S. (2015). Short local descriptors from 2D connected pattern spectra. In Image Processing (ICIP), 2015 IEEE International Conference on. (pp. 1548-1552). IEEE (The Institute of Electrical and Electronics Engineers). DOI: 10.1109/ICIP.2015.7351060
Kiwanuka, F. N., & Wilkinson, M. H. F. (2015). Cluster Based Vector Attribute Filtering. In Mathematical Morphology and Its Applications to Signal and Image Processing. (pp. 277-288). (Lecture Notes in Computer Science; Vol. 9082). SPRINGER. DOI: 10.1007/978-3-319-18720-4_24
Lange, M., Biehl, M., & Villmann, T. (2015). Non-Euclidean principal component analysis by Hebbian learning. Neurocomputing, 147, 107-119. DOI: 10.1016/j.neucom.2013.11.049
Vries, J. J. G. . G-J. D., Pauws, S. C., & Biehl, M. (2015). Insightful stress detection from physiology modalities using Learning Vector Quantization. Neurocomputing, 151(Part 2), 873-882. DOI: 10.1016/j.neucom.2014.10.008
Yeo, L., Adlard, N., Biehl, M., Juarez, M., Smallie, T., Snow, M., ... Scheel-Toellner, D. (2015). Expression of chemokines CXCL4 and CXCL7 by synovial macrophages defines an early stage of rheumatoid athritis. Annals of the Rheumatic Diseases, 1-11. DOI: 10.1136/annrheumdis-2014-206921
Mwebaze, E., Bearda, G., Biehl, M., & Zuehlke, D. (2015). Combining dissimilarity measures for prototype-based classification. In M. Verleysen (Ed.), European Symposium on Artificial Neural Networks (ESANN) 2015. (Vol. 23, pp. 31-36). d-side publishing.
Smedinga, R., Biehl, M., & Kramer, F. (Eds.) (2015). 12th SC@RUG 2015 proceedings: Student Colloquium 2014-2015. Rijksuniversiteit Groningen. Universiteitsbibliotheek.
de Wiljes, O. O., van Elburg, R. A., Biehl, M., & Keijzer, F. (2015). Modeling spontaneous activity across an excitable epithelium: Support for a coordination scenario of early neural evolution. Frontiers in Computational Neuroscience, 9. DOI: 10.3389/fncom.2015.00110
de Vries, J. J. G., Lemmens, P. M. C., Brokken, D., Pauws, S. C., & Biehl, M. (2015). Towards Emotion Classification Using Appraisal Modeling. International Journal of Synthetic Emotions, 6(1), 40-59. DOI: 10.4018/IJSE.2015010103
Biehl, M., Hammer, B., Schleif, F-M., Schneider, P., & Villmann, T. (2015). Stationarity of Matrix Relevance LVQ. In Neural Networks (IJCNN), 2015 International Joint Conference on. (pp. 1-8). IEEE (The Institute of Electrical and Electronics Engineers). DOI: 10.1109/IJCNN.2015.7280441
Lang, K., Beuschlein, F., Biehl, M., Dietz, A., Riester, A., Hughes, B. A., ... Arlt, W. (2015). Urine steroid metabolomics as a diagnostic tool in primary aldosteroinism. Abstract from Society for Endocrinology BES 2015, Edinburgh, United Kingdom.DOI: 10.1530/endoabs.38.OC1.6
Taylor, A. E., Bancos, I., Chortis, V., Lang, K., O'Neil, D. M., Hughes, B. A., ... Arlt, W. (2015). Further advances in diagnosis of adrenal cancer: a high-throughput urinary steroid profiling method using liquid chromatography tandem mass spectrometry (LC-MS/MS). Abstract from Society for Endocrinology BES 2015, Edinburgh, United Kingdom.DOI: 10.1530/endoabs.38.OC2.3
Chortis, V., Bancos, I., Lang, K., Hughes, B. A., O'Neil, D. M., Taylor, A. E., ... Arlt, W. (2015). Urine steroid metabolomics as a novel diagnostic tool for early detection of recurrence in adrenocortical carcinoma. Abstract from Society for Endocrinology BES 2015, Edinburgh, United Kingdom.DOI: 10.1530/endoabs.38.OC3.4
Biehl, M., Ghio, A., & Schleif, F-M. (2015). Developments in computational intelligence and machine learning. Neurocomputing, 169, 185-186. DOI: 10.1016/j.neucom.2015.03.062
Schulz, A., Mokbel, B., Biehl, M., & Hammer, B. (2015). Inferring Feature Relevances From Metric Learning. In Computational Intelligence, 2015 IEEE Symposium Series on. (pp. 1599-1606). IEEE (The Institute of Electrical and Electronics Engineers). DOI: 10.1109/SSCI.2015.225
Villmann, T., Kaden, M., Nebel, D., & Biehl, M. (2015). Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection. In G. Azzopardi, & N. Petkov (Eds.), Proc. Computer Analysis of Images and Patterns. (pp. 772-782). (Lecture Notes in Computer Science; Vol. 9257). Springer. DOI: 10.1007/978-3-319-23117-4_66
Vries, G-J., Pauws, S., & Biehl, M. (2015). Facial Expression Recognition Using Learning Vector Quantization. In G. Azzopardi, & N. Petkov (Eds.), Computer Analysis of Images and Patterns: 16th International Conference, CAIP 2015, Valletta, Malta, September 2-4, 2015, Proceedings, Part II. (pp. 760-771). (Lecture Notes in Computer Science; Vol. 9257). Cham: Springer. DOI: 10.1007/978-3-319-23117-4_65
2014
Azzopardi, G., Rodriguez-Sanchez, A., Piater, J., & Petkov, N. (2014). A Push-Pull CORF Model of a Simple Cell with Antiphase Inhibition Improves SNR and Contour Detection. Plos one, 9(7), [98424]. DOI: 10.1371/journal.pone.0098424
Azzopardi, G., & Petkov, N. (2014). Ventral-stream-like shape representation: from pixel intensity values to trainable object-selective COSFIRE models. Frontiers in Computational Neuroscience, 8, [80]. DOI: 10.3389/fncom.2014.00080
Gheorghiu, E., Kingdom , F. A. A., & Petkov, N. (2014). Contextual modulation as de-texturizer. Vision Research, 104, 12-23. DOI: 10.1016/j.visres.2014.08.013
Grandinetti, L., Lippert, T. A., & Petkov, N. (2014). Brain-Inspired Computing: International Workshop, BrainComp 2013, Cetraro, Italy, July 8-11, 2013, Revised Selected Papers. (Lecture Notes in Computer Science; Vol. 8603). Springer.
Azzopardi, G., & Petkov, N. (2014). COSFIRE: A Brain-Inspired Approach to Visual Pattern Recognition. In L. Granidinetti, T. A. Lippert, & N. Petkov (Eds.), Brain-Inspired Computing: Revised Selected Papers. (Vol. 8603, pp. 76-87). (Lecture Notes in Computer Science; Vol. 8306). Springer.
Sun, Z., Wiering, M., & Petkov, N. (2014). Classification System for Mortgage Arrear Management. In IEEE Computational Intelligence for Financial Engineering and Economics . IEEE (The Institute of Electrical and Electronics Engineers).
Giotis, I., Bunte, K., Petkov, N., & Biehl, M. (2014). Erratum to: Adaptive Matrices and Filters for Color Texture Classification. Journal of Mathematical Imaging and Vision, 48(1), 202. DOI: 10.1007/s10851-013-0472-1
Azzopardi, G., Sanchez, A. R., Piater, J., & Petkov, N. (2014). A computational model of push-pull inhibition of simple cells with application to contour detection. 163-163. Poster session presented at European Conference on Visual Perception 2014, .
Azzopardi, G., Strisciuglio, N., Vento, M., & Petkov, N. (2014). Vessels delineation in retinal images using COSFIRE filters. In Netherlands Conference on Computer Vision, NCCV 2014. Ermelo, Netherlands.
Cholakova, T., Chitanov, V., Chaliampalias, D., Kolaklieva, L., Kakanakov, R., Bahchedjiev, C., ... Meletis, E. I. (2014). Study of the Structural and Mechanical Properties of Nanocrystalline TiAlSiN Gradient Coatings. Journal of Nano Research, 27, 15-24. DOI: 10.4028/www.scientific.net/JNanoR.27.15
van Hateren, J. H. (2014). Intrinsic estimates of fitness affect the causal structure of evolutionary change. Biology & Philosophy, 30(5), 729-746. DOI: 10.1007/s10539-014-9463-x
van Hateren, J. H. (2014). The origin of agency, consciousness, and free will. Phenomenology and the Cognitive Sciences, 14(4), 979-1000. DOI: 10.1007/s11097-014-9396-5
Azzopardi, G., de Vries, H., Knobbe, A., & Koelewijn, A. (2014). Parametric nonlinear regression models for dike monitoring systems. In Parametric nonlinear regression models for dike monitoring systems. (Vol. 8819, pp. 345-355)
Moschini, U., Teeninga, P., Wilkinson, M., Giese, N., Punzo, D., van der Hulst, J. M., & Trager, S. (2014). Towards better segmentation of large floating point 3D astronomical data sets: first results. In Proceedings of the 2014 conference on Big Data from Space BiDS14. (pp. 232-235). Publications Office of the European Union .
Smedinga, R., Biehl, M., & Kramer, F. (Eds.) (2014). 11th SC@RUG 2014 proceedings: Student Colloquium 2013-2014. Groningen: Rijksuniversiteit Groningen. Universiteitsbibliotheek.
Biehl, M., Sadowski, P., Bhanot, G., Bilal, E., Dayarian, A., Meyer, P., ... Hormoz, S. (2014). Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge. Bioinformatics. DOI: 10.1093/bioinformatics/btu407
Dayarian, A., Romero, R., Wang, Z., Biehl, M., Bilal, E., Hormoz, S., ... Tarca, A. L. (2014). Predicting protein phosphorylation from gene expression: Top methods from the IMPROVER Species Translation Challenge. Bioinformatics. DOI: 10.1093/bioinformatics/btu490
Kruitbosch, H. T., Giotis, I., & Biehl, M. (2014). Segmented Shape-Symbolic Time Series Representation. In M. Verleysen (Ed.), Proceedings of the 22. European Symposium on Artificial Neural Networks ESANN. d-side publishing.
Biehl, M. (2014). Prototype-Based Classifiers and Their Application in the Life Sciences (abstract). In T. Villmann, F-M. Schleif, M. Kaden, & M. Lange (Eds.), Advances in Self-Organizing Maps and Learning Vector Quantization: Proc. of the 10th International Workshop, WSOM 2014. (pp. 121-121). (Advances in Intelligent Systems and Computing; Vol. 295). Springer. DOI: 10.1007/978-3-319-07695-9_11
Schulz, A., Hofmann, D., Biehl, M., & Hammer, B. (2014). Interpretation of linear mappings employing L1 regularization. In MiWoCI Workshop-2014. (pp. 5). (Machine Learning Reports; Vol. MLR-01/2014). Univ. of Bielefeld.
Biehl, M., Kaden, M., Stürmer, P., & Villmann, T. (2014). ROC-Optimization and Statistical Quality Measures in Learning Vector Quantization Classifiers. In MIWOCI Workshop 2014. (pp. 23-34). (Machine Learning Reports; Vol. MLR-01/2014). Univ. of Bielefeld.
Davis, C. F., Ricketts, C. J., Wang, M., Yang, L., Cherniack, A. D., Shen, H., ... The Cancer Genome Atlas Research Network (2014). The Somatic Genomic Landscape of Chromophobe Renal Cell Carcinoma. Cancer cell, 26(3), 319-330. DOI: 10.1016/j.ccr.2014.07.014
Hormoz, S., Bhanot, G., Biehl, M., Bilal, E., Meyer, P., Norel, R., ... Dayarian, A. (2014). Inter-species Inference of Gene Set Enrichment in Lung Epithelial Cells from Proteomic and Large Transcriptomic Data Sets. Bioinformatics (Oxford, England). DOI: 10.1093/bioinformatics/btu569
Bilal, E., Sakellaropoulos, T., Challenge Participants, Melas, I. N., Messinis, D. E., Belcastro, V., ... Poussin, C. (2014). A crowd-sourcing approach for the construction of species-specific cell signaling networks. Bioinformatics, [btu659]. DOI: 10.1093/bioinformatics/btu659
Frenay, B., Hofmann, D., Schulz, A., Biehl, M., & Hammer, B. (2014). Valid interpretation of feature relevance for linear data mappings. In Computational Intelligence and Data Mining (CIDM), 2014 IEEE Symposium on. (pp. 149-156). IEEE (The Institute of Electrical and Electronics Engineers). DOI: 10.1109/CIDM.2014.7008661
Biehl, M., Hammer, B., & Villmann, T. (2014). Distance measures for prototype based classification. In L. Grandinetti, T. A. Lippert, & N. Petkov (Eds.), Brain-Inspired Computing, International Workshop, Cetraro/Italy, July 2013: Revised Selected Papers. (Vol. 8603, pp. 100-116). (Lecture Notes in Computer Science). Springer. DOI: 10.1007/978-3-319-12084-3_9
Biehl, M., Kaden, M., & Villmann, T. (2014). Statistical Quality Measures and ROC-Optimization by Learning Vector Quantization Classifiers. In H. A. Kestler, M. Schmid, L. Lausser, & J. M. Krauss (Eds.), Statistical Computing 2014: Abstracts der 46. Arbeitstagung. (pp. 2-6). (Ulmer Informatik-Berichte; Vol. 2014-04). UULM (Ulm University).
2013
Azzopardi, G., & Petkov, N. (2013). Automatic detection of vascular bifurcations in segmented retinal images using trainable COSFIRE filters. Pattern Recognition Letters, 34(8), 922-933. DOI: 10.1016/j.patrec.2012.11.002
Emerencia, A., van der Krieke, L., Sytema, S., Petkov, N., & Aiello, M. (2013). Generating personalized advice for schizophrenia patients. Artificial Intelligence in Medicine, 58(1), 23-36. DOI: 10.1016/j.artmed.2013.01.002
Giotis, I., Visser, M., Jonkman, M., & Petkov, N. (2013). Discriminative power of visual attributes in dermatology. Skin research and technology, 19(1), E123-E131. DOI: 10.1111/j.1600-0846.2012.00618.x
Azzopardi, G., & Petkov, N. (2013). Trainable COSFIRE Filters for Keypoint Detection and Pattern Recognition. Ieee transactions on pattern analysis and machine intelligence, 35(2), 490-503. DOI: 10.1109/TPAMI.2012.106
Alegre, E., Biehl, M., Petkov, N., & Sanchez, L. (2013). Assessment of acrosome state in boar spermatozoa heads using n-contours descriptor and RLVQ. Computer Methods and Programs in Biomedicine, 111(3), 525-536. DOI: 10.1016/j.cmpb.2013.05.003
Giotis, I., Bunte, K., Petkov, N., & Biehl, M. (2013). Adaptive Matrices and Filters for Color Texture Classification. Journal of Mathematical Imaging and Vision, 47(1), 79-92. DOI: 10.1007/s10851-012-0356-9
Azzopardi, G., & Petkov, N. (2013). A shape descriptor based on trainable COSFIRE filters for the recognition of handwritten digits. In Lecture Notes in Computer Science. (Vol. 8048, pp. 9-16). Springer.
Chitanov, V., Cholakova, T., Kolaklieva, L., Kakanakov, R., Bahchedjiev, C., & Petkov, N. (2013). Influence of thermal treatment on the mechanical properties of TiAlSiN-based coatings. Journal of Chemical Technology and Metallurgy, 48(6), 567-570.
Andreas, N., Maria, P., Ioannou, R., Petkov, N., & Schizas, C. N. (2013). A machine learning approach for clustering western and non-western folk music using low-level and mid-level. In Proceedings 6th International Workshop on Machine Learning and Music. (pp. 55-58)
Azzopardi, G., & Petkov, N. (2013). Trainable COSFIRE filters for keypoint detection object localization, and pattern recognition. Ieee transactions on pattern analysis and machine intelligence, 35(2), 490-503.
Neocleous, A., Panteli, M., Petkov, N., & Schizas, C. N. (2013). TIMBRE AND TONAL SIMILARITIES BETWEEN THE TURKISH, WESTERN AND CYPRIOT MONOPHONIC SONGS USING MACHINE LEARNING TECHNIQUES. In P. van Kranenburg, C. Anagnostopoulou, & A. Volk (Eds.), Proceedings of The 3rd International Workshop on Folk Music Analysis will take place in Amsterdam, Netherlands, June 6 and 7, 2013. (pp. 95). Meertens Instituut.
Hateren, J. H. V. (2013). A New Criterion for Demarcating Life from Non-Life. Origins of Life and Evolution of Biospheres, 43(6), 491-500. DOI: 10.1007/s11084-013-9352-3
Hateren, J. H. V. (2013). A fractal climate response function can simulate global average temperature trends of the modern era and the past millennium. Climate dynamics, 40(11-12), 2651-2670. DOI: 10.1007/s00382-012-1375-3
Azzopardi, G. (2013). COSFIRE (Combination of Shifted Filter Responses): A trainable filter approach to visual pattern recognition Groningen: s.n.
Moschini, U., Trager, S. C., & Wilkinson, M. H. F. (2013). Mask Connectivity by Viscous Closings: Linking Merging Galaxies without Merging Double Stars. In C. L. Hendriks, G. Borgefors, & R. Strand (Eds.), Mathematical Morphology and Its Applications to Signal and Image Processing. (Vol. 7883, pp. 484-495). (Lecture Notes in Computer Science). Springer. DOI: 10.1007/978-3-642-38294-9_41
Evans, D., & Moschini, U. (2013). Ten Times more information in your Real-Time Telemetry (extended). In C. A. C. Michael Schmidhuber, & J. Kehr (Eds.), Space Operations: Experience, Mission Systems and Advanced Concepts. (Progress in Astronautics and Aeronautics). American Institute of Aeronautics Astronautics. DOI: 10.2514/4.102080
Teeninga, P., Moschini, U., Trager, S. C., & Wilkinson, M. H. F. (2013). Bi-variate statistical attribute filtering: A tool for robust detection of faint objects. In 11th International Conference "Pattern Recognition and Image Analysis: New Information Technologies" (PRIA-11-2013). (pp. 746-749)
Sardjono, T. A., Wilkinson, M. H. F., Veldhuizen, A. G., van Ooijen, P. M. A., Purnama, K. E., & Verkerke, G. J. (2013). Automatic Cobb Angle Determination From Radiographic Images. SPINE, 38(20), E1256-E1262. DOI: 10.1097/BRS.0b013e3182a0c7c3
van de Gronde, J. J., Wilkinson, M. H. F., & Roerdink, J. B. T. M. (2013). Rotation-invariant morphological filtering of tensor fields using frames.
Strickert, M., Hammer, B., Villmann, T., & Biehl, M. (2013). Regularization and improved interpretation of linear data mappings and adaptive distance measures. In Computational Intelligence and Data Mining (CIDM), 2013 IEEE Symposium on. (pp. 10-17). IEEE (The Institute of Electrical and Electronics Engineers). DOI: 10.1109/CIDM.2013.6597211
Lange, M., Biehl, M., & Villmann, T. (2013). Non-Euclidean Independent Component Analysis and Oja's Learning. In M. Verleysen (Ed.), Proc. 21st Europ. Symp. Artificial Neural Networks (ESANN). (pp. 125-130). d-side publishing.
Biehl, M., Bunte, K., & Schneider, P. (2013). Analysis of flow cytometry data by matrix relevance learning vector quantization. PLoS ONE, 8(3), [e59401]. DOI: 10.1371/journal.pone.0059401
Kästner, M., Nebel, D., Riedel, M., Biehl, M., & Villmann, T. (2013). Differentiable Kernels in Generalized Matrix Learning Vector Quantization. In Machine Learning and Applications (ICMLA), 2012 11th Conference on. (pp. 132-137). IEEE (The Institute of Electrical and Electronics Engineers). DOI: 10.1109/ICMLA.2012.231
Biehl, M., Kästner, M., Lange, M., & Villmann, T. (2013). Non-Euclidean principal component analysis and Oja’s learning rule-theoretical aspects. In P. A. Estevez (Ed.), Advances in Self-Organizing Maps: Proc. of the 9th Workshop on Self-Organizing Maps (WSOM 2012). (pp. 23-33). (Advances in Intelligent Systems and Computing; Vol. 198). Springer. DOI: 10.1007/978-3-642-35230-0_3
Biehl, M. (2013). Two or three things we know about LVQ. In MiWoCI Workshop-2013. (pp. 71-73). (Machine Learning Reports; Vol. MLR-04/2013). Univ. of Bielefeld.
2012
Papari, G., Campisi, P., & Petkov, N. (2012). New Families of Fourier Eigenfunctions for Steerable Filtering. Ieee transactions on image processing, 21(6), 2931-2943. DOI: 10.1109/TIP.2011.2179060
Giotis, I., & Petkov, N. (2012). Cluster-based adaptive metric classification. Neurocomputing, 81, 33-40. DOI: 10.1016/j.neucom.2011.10.018
Azzopardi, G., & Petkov, N. (2012). Detection of retinal vascular bifurcations by rotation-, scale- and reflection-invariant COSFIRE filters. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Azzopardi, G., & Petkov, N. (2012). A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model. Biological Cybernetics, 106(3), 177-189. DOI: 10.1007/s00422-012-0486-6
Giotis, I., & Petkov, N. (2012). Cluster-based adaptive metric classification (vol 81, pg 33, 2012). Neurocomputing, 91, 90-90. DOI: 10.1016/j.neucom.2012.04.001
Azzopardi, G., & Petkov, N. (2012). A CORF computational model of a simple cell with application to contour detection. Perception, 41(10), 1269-1270.
Azzopardi, G., & Petkov, N. (2012). Contour Detection by CORF Operator. In Lecture Notes in Computer Science. (pp. 395-402). Springer.
Azzopardi, G., & Petkov, N. (2012). V4-like filters applied to the detection of retinal vascular bifurcations. Perception, 41(3), 365-366.
Azzopardi, G., & Petkov, N. (2012). CORE: A computational model of a simple cell with application to contour detection. Perception, 41, 99-99.
Azzopardi, G., & Petkov, N. (2012). Detection of Retinal Vascular Bifurcations by Rotation- and Scale-Invariant COSFIRE Filters. In EPRINTS-BOOK-TITLE. Springer.
Kakanakov, R., Polychroniadis, E. K., Cholakova, T., Vourlias, G., Kolaklieva, L., Chaliampalias, D., ... Petkov, N. (2012). Mechanical, Structural and Thermal Properties of Multilayered Gradient Nanocomposite Coatings. Journal of Nano Research, 17, 193-202. DOI: 10.4028/www.scientific.net/JNanoR.17.193
Petkov, N. (Author), & Wieling, M. (Author). (2012). Gabor filter及参数.
Wilkinson, M., Moschini, U., Ouzounis, G. K., & Pesaresi, M. (2012). Concurrent computation of connected pattern spectra for very large image information mining. In ESA-EUSC-JRC 8th Conference on Image Information Mining. (pp. 21-25). DOI: 10.2788/49465
Evans, D., & Moschini, U. (2012). Ten Times More Information in Your Real-Time TM. In SpaceOps 2012 Conference. (SpaceOps Conferences). American Institute of Aeronautics and Astronautics. DOI: 10.2514/6.2012-1275117
Tushabe, F., & Wilkinson, M. H. F. (2012). Color Processing using Max-trees: A Comparison on Image Compression. In Systems and Informatics (ICSAI), 2012 International Conference on. (pp. 1374-1380). IEEE (The Institute of Electrical and Electronics Engineers).
Wilkinson, M. H. F., & Oosterbroek, J. (2012). Mask-Edge Connectivity: Theory, Computation, and Application to Historical Document Analysis. In Proceedings of the 21st International Conference on Pattern Recognition, ICPR 2012. (pp. 1334-1337). IEEE (The Institute of Electrical and Electronics Engineers).
Kiwanuka, F. N., & Wilkinson, M. H. F. (2012). Cluster-Based Vector-Attribute Filtering for CT and MRI Enhancement. In Proceedings of the 21st International Conference on Pattern Recognition, ICPR 2012. (pp. 3112-3115). IEEE (The Institute of Electrical and Electronics Engineers).
Kiwanuka, F. N., & Wilkinson, M. H. F. (2012). Radial Moment Invariants for Attribute Filtering in 3D. In Applications of Discrete Geometry and Mathematical Morphology: First International Workshop, WADGMM 2010. (pp. 68-81). (Lecture Notes in Computer Science; Vol. 7346). Heidelberg: Springer.
van de Gronde, J. J., Wilkinson, M. H. F., & Roerdink, J. B. T. M. (2012). Making computers see tensors using mathematical morphology.
Biehl, M. (2012). Admire LVQ—adaptive distance measures in Relevance Learning Vector quantization. Künstliche Intelligenz, 26(4), 391-395. DOI: 10.1007/s13218-012-0188-1
Biehl, M., Bunte, K., Schleif, F-M., Schneider, P., & Villmann, T. (2012). Large Margin Linear Discriminative Visualization by Matrix Relevance Learning. In 2012 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN). (IEEE International Joint Conference on Neural Networks (IJCNN)). NEW YORK: IEEE (The Institute of Electrical and Electronics Engineers). DOI: 10.1109/IJCNN.2012.6252627
Bunte, K., Schneider, P., Hammer, B., Schleif, F-M., Villmann, T., & Biehl, M. (2012). Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks, 26, 159-173. DOI: 10.1016/j.neunet.2011.10.001
Huber, M. B., Bunte, K., Nagarajan, M. B., Biehl, M., Ray, L. A., & Wismueller, A. (2012). Texture feature ranking with relevance learning to classify interstitial lung disease patterns. Artificial Intelligence in Medicine, 56(2), 91-97. DOI: 10.1016/j.artmed.2012.07.001
Peters, G., Bunte, K., Strickert, M., Biehl, M., & Villmann, T. (2012). Visualization of Processes in Self-Learning Systems. In Privacy, Security and Trust (PST), 2012 Tenth Annual International Conference on. (pp. 244-249). IEEE (The Institute of Electrical and Electronics Engineers). DOI: 10.1109/PST.2012.6297953
Kästner, M., Hammer, B., Biehl, M., & Villmann, T. (2012). Functional relevance learning in generalized learning vector quantization. Neurocomputing, 90, 85-95. DOI: 10.1016/j.neucom.2011.11.029
Bunte, K., Haase, S., Biehl, M., & Villmann, T. (2012). Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences. Neurocomputing, 90, 23-45. DOI: 10.1016/j.neucom.2012.02.034
Bunte, K., Biehl, M., & Hammer, B. (2012). A General Framework for Dimensionality-Reducing Data Visualization Mapping. Neural computation, 24(3), 771-804. DOI: 10.1162/NECO_a_00250
Smedinga, R., Biehl, M., & Kramer, F. (Eds.) (2012). 9th SC@RUG 2012 proceedings: Student Colloquium 2011-2012. Rijksuniversiteit Groningen. Universiteitsbibliotheek.
Mokbel, B., Lueks, W., Gisbrecht, A., Biehl, M., & Hammer, B. (2012). Visualizing the quality of dimensionality reduction. In M. Verleysen (Ed.), 20th European Symposium on Artificial Neural Networks ESANN 2012. (pp. 179-184). d-side publishing.
Bunte, K., Schleif, F-M., & Biehl, M. (2012). Adaptive learning for complex valued data. In M. Verleysen (Ed.), 20th European Symposium on Artificial Neural Networks, ESANN 2012. (pp. 387-392). d-side publishing.
Biehl, M., Schneider, P., Smith, D., Stiekema, H., Taylor, A., Hughes, B., ... Arlt, W. (2012). Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors. In M. Verleysen (Ed.), Proc. 20th European Symposium on Artificial Neural Networks: ESANN 2012. (pp. 423-428). d-side publishing.
2011
Emerencia, A., van der Krieke, L., Petkov, N., & Aiello, M. (2011). Assessing Schizophrenia with an Interoperable Architecture. In M-M. Bouamrane, & C. Tao (Eds.), Proceedings of the first International Workshop on Managing Interoperability and Complexity in Health Systems, MIXHS'11. (pp. 79-82). New York, NY, USA: ACM Press. DOI: 10.1145/2064747.2064766
Papari, G., & Petkov, N. (2011). An improved model for surround suppression by steerable filters and multilevel inhibition with application to contour detection. Pattern recognition, 44(9), 1999-2007. DOI: 10.1016/j.patcog.2010.08.013
Petkov, N., & Jiang, X. (2011). CAIP—Computer Analysis of Images and Patterns. Pattern recognition, 44(9), 1841-1841. DOI: 10.1016/j.patcog.2011.03.017
Bunte, K., Biehl, M., Jonkman, M. F., & Petkov, N. (2011). Learning effective color features for content based image retrieval in dermatology. Pattern recognition, 44(9), 1892-1902. DOI: 10.1016/j.patcog.2010.10.024
Papari, G., & Petkov, N. (2011). Edge and line oriented contour detection: State of the art. Image and vision computing, 29(2-3), 79-103. DOI: 10.1016/j.imavis.2010.08.009
van der Krieke, J. A. J., Emerencia, A. C., Sytema, S., Aiello, M., Petkov, N., & Wiersma, D. (2011). AN ONLINE SELF-MANAGEMENT TOOL FOR PEOPLE SUFFERING FROM PSYCHOSIS. Schizophrenia Bulletin, 37, 283-283.
Azzopardi, G., & Petkov, N. (2011). Detection of Retinal Vascular Bifurcations by Trainable V4-Like Filters. In P. Real, D. DiazPernil, H. MolinaAbril, A. Berciano, & W. Kropatsch (Eds.), COMPUTER ANALYSIS OF IMAGES AND PATTERNS: 14TH INTERNATIONAL CONFERENCE, CAIP 2011, PT I. (pp. 451-459). (Lecture Notes in Computer Science; Vol. 6854). BERLIN: Springer.
Bunte, K., Giotis, I., Petkov, N., & Biehl, M. (2011). Adaptive Matrices for Color Texture Classification. In P. Real, D. DiazPernil, H. MolinaAbril, A. Berciano, & W. Kropatsch (Eds.), COMPUTER ANALYSIS OF IMAGES AND PATTERNS: 14TH INTERNATIONAL CONFERENCE, CAIP 2011, PT 2. (pp. 489-497). (Lecture Notes in Computer Science; Vol. 6855). BERLIN: Springer. DOI: 10.1007/978-3-642-23678-5_58
Azzopardi, G., & Petkov, N. (2011). Detection of retinal vascular bifurcations by rotation-, scale- and reflection-invariant COSFIRE filters. In P. Soda, & F. Tortorella (Eds.), 2012 25TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS). NEW YORK: IEEE (The Institute of Electrical and Electronics Engineers).
Ouzounis, G. K., & Wilkinson, M. H. F. (2011). Hyperconnected Attribute Filters Based on k-Flat Zones. Ieee transactions on pattern analysis and machine intelligence, 33(2), 224-239. DOI: 10.1109/TPAMI.2010.74
Ferdosi, B. J., Buddelmeijer, H., Trager, S. C., Wilkinson, M. H. F., & Roerdink, J. B. T. M. (2011). Comparison of density estimation methods for astronomical datasets. Astronomy & astrophysics, 531, [114]. DOI: 10.1051/0004-6361/201116878
Wilkinson, M. H. F. (2011). A Fast Component-Tree Algorithm for High Dynamic-Range Images and Second Generation Connectivity. In 2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP). (pp. 1021-1024). (IEEE International Conference on Image Processing ICIP). New York: IEEE (The Institute of Electrical and Electronics Engineers).
Neerbos, J. V., Najman, L., & Wilkinson, M. H. F. (2011). Towards a Parallel Topological Watershed: First Results. In EPRINTS-BOOK-TITLE. Springer.
Wilkinson, M. H. F., Soille, P., Pesaresi, M., & Ouzounis, G. K. (2011). Concurrent Computation of Differential Morphological Profiles on Giga-Pixel Images. In EPRINTS-BOOK-TITLE. Springer.
Wilkinson, M. H. F. (2011). Hyperconnections and Openings on Complete Lattices. In EPRINTS-BOOK-TITLE. Springer.
Mwebaze, E., Schneider, P., Schleif, F. -M., Aduwo, J. R., Quinn, J. A., Haase, S., ... Biehl, M. (2011). Divergence-based classification in learning vector quantization. Neurocomputing, 74(9), 1429-1435. DOI: 10.1016/j.neucom.2010.10.016
Bunte, K., Biehl, M., & Hammer, B. (2011). Dimensionality Reduction Mappings. In Proc. IEEE Symp. on Computational Intelligence and Data Mining SSCI 2011 CDIM. (pp. 349-356). IEEE (The Institute of Electrical and Electronics Engineers). DOI: 10.1109/CIDM.2011.5949443
Arlt, W., Biehl, M., Taylor, A. E., Hahner, S., Libe, R., Hughes, B. A., ... Stewart, P. M. (2011). Urine Steroid Metabolomics as a Biomarker Tool for Detecting Malignancy in Adrenal Tumors. JOURNAL OF CLINICAL ENDOCRINOLOGY AND METABOLISM, 96(12), 3775-3784. DOI: 10.1210/jc.2011-1565
Bunte, K., Hammer, B., Villmann, T., Biehl, M., & Wismueller, A. (2011). Neighbor embedding XOM for dimension reduction and visualization. Neurocomputing, 74(9), 1340-1350. DOI: 10.1016/j.neucom.2010.11.027
Smedinga, R., Biehl, M., & Kramer, F. (Eds.) (2011). 8th SC@RUG 2011 proceedings: Student Colloquium 2010-2011. Rijksuniversiteit Groningen. Universiteitsbibliotheek.
Mwebaze, E., Quinn, J., & Biehl, M. (2011). Causal relevance learning for robust classification under inventions. In M. Verleysen (Ed.), 19th European Symposium on Artificial Neural Networks (ESANN 2011). (pp. 315-320). d-side publishing.
Quinn, J., Mooij, J., Heskes, T., & Biehl, M. (2011). Learning of Causal Relations. In M. Verleysen (Ed.), 19th European Symposium on Artificial Neural Networks (ESANN 2011) . (pp. 287-296). d-side publishing.
Bunte, K., Biehl, M., & Hammer, B. (2011). Supervised dimension reduction mappings. In M. Verleysen (Ed.), 19th European Symposium on Artificial Neural Networks (ESANN 2011). (pp. 281-286). d-side publishing.
Schneider, P., Geweniger, T., Schleif, F-M., Biehl, M., & Villmann, T. (2011). Multivariate class labeling in Robust Soft LVQ. In M. Verleysen (Ed.), 19th European Symposium on Artificial Neural Networks (ESANN 2011). (pp. 17-22). d-side publishing.
Kästner, M., Hammer, B., Biehl, M., & Villmann, T. (2011). Generalized functional relevance Learning Vector Quantization. In M. Verleysen (Ed.), 19th European Symposium on Artificial Neural Networks (ESANN 2011). (pp. 93-98). d-side publishing.
Huber, M. B., Bunte, K., Nagajaran, M. B., Biehl, M., Ray, L. A., & [No Value], W. (2011). Texture feature selection with relevance learning to classify Interstitial lung disease patterns. In Medical Imaging 2011: Computer Aided Diagnostics. (Vol. 7963 (43)). (SPIE Conference Proceedings). DOI: doi:10.1117/12.877894
Hammer, B., Biehl, M., Bunte, K., & Mokbel, B. (2011). A general framework for dimensionality reduction for large data sets. In J. Laaksonen, & T. Honkela (Eds.), Advances in Self-Organizing Maps, Proc. 8th Intl. Workshop on Selforganizing Maps (WSOM 2011): WSOM 2011. (pp. 277-287). (Lecture Notes in Computer Science; Vol. 6731). Springer. DOI: 10.1007/978-3-642-21566-7_28
Kästner, M., Villmann, T., & Biehl, M. About Sparsity in Functional Relevance Learning in Generalized Learning Vector Quantization
Papari, G., Bunte, K., & Biehl, M. (2011). Waypoint averaging and step size control in learning by gradient descent (technical report). In F-M. Schleif, & T. Villmann (Eds.), MIWOCI 2011, Mittweida Workshop on Computational Intelligence. (Vol. MLR-2011-06, pp. 16-26). (Machine Learning Reports). Univ. of Bielefeld.
Lueks, W., Mokbel, B., Biehl, M., & Hammer, B. (2011). How to evaluate Dimensionality Reduction (technical report). In B. Hammer, & T. Villmann (Eds.), Workshop New Challenges in Neural Computation. (Vol. MLR-2011-05, pp. 29-37). (Machine Learning Reports). Univ. of Bielefeld.
Biehl, M., Hammer, B., Merényi, E., Sperduti, A., & Villman, T. (Eds.) Learning in the context of very high dimensional data (Dagstuhl Seminar 11341) DOI: http://dx.doi.org/10.4230/DagRep.1.8.67
2010
Papari, G., Campisi, P., & Petkov, N. (2010). CLOSED FORM OF THE STEERED ELONGATED HERMITE-GAUSS WAVELETS. In 2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING. (pp. 377-380). (IEEE International Conference on Image Processing ICIP). NEW YORK: University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Bosman, H. H. W. J., Petkov, N., & Jonkman, M. F. (2010). Comparison of color representations for content-based image retrieval in dermatology. Skin research and technology, 16(1), 109-113. DOI: 10.1111/j.1600-0846.2009.00405.x
Papari, G., Campisi, P., & Petkov, N. (2010). Steerable filtering using novel circular harmonic functions with application to edge detection. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Papari, G., & Petkov, N. (2010). Painterly Rendering. In Computational Photography: Methods and Applications. (pp. 367-394). CRC Press.
Purnama, K. E., Wilkinson, M. H. F., Veldhuizen, A. G., van Ooijen, P. M. A., Lubbers, J., Burgerhof, J. G. M., ... Verkerke, G. J. (2010). A framework for human spine imaging using a freehand 3D ultrasound system. Technology and Health Care, 18(1), 1-17. DOI: 10.3233/THC-2010-0565
Ouzounis, G. K., & Wilkinson, M. H. F. (2010). Partition-induced connections and operators for pattern analysis. Pattern recognition, 43(10), 3193-3207. DOI: 10.1016/j.patcog.2009.10.002
Ferdosi, B. J., Buddelmeijer, H., Trager, S., Wilkinson, M. H. F., & Roerdink, J. B. T. M. (2010). Finding and Visualizing Relevant Subspaces for Clustering High-Dimensional Astronomical Data Using Connected Morphological Operators. In EPRINTS-BOOK-TITLE. (pp. 35-42). University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Dewi, D. E. O., Veldhuizen, A. G., Burgerhof, J. G. M., Purnama, I. K. E., Ooijen, P. M. A. V., Wilkinson, M. H. F., ... Verkerke, G. J. (2010). Reproducibility of Standing Posture for X-Ray Radiography: A Feasibility Study of the BalancAid with Healthy Young Subjects. Annals of Biomedical Engineering, 38(10), 3237-3245. DOI: 10.1007/s10439-010-0062-y
Wilkinson, M. H. F., & Ouzounis, G. K. (2010). Advances in Connectivity and Connected Attribute Filters. In P. W. Hawkes (Ed.), ADVANCES IN IMAGING AND ELECTRON PHYSICS, VOL 161. (pp. 211-275). (Advances in Imaging and Electron Physics; Vol. 161). SAN DIEGO: Academic Press. DOI: 10.1016/S1076-5670(10)61005-1
Kiwanuka, F. N., & Wilkinson, M. H. F. (2010). Automatic Attribute Threshold Selection for Blood Vessel Enhancement. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Wilkinson, M., Urbach, E., Jalba, A., & Roerdink, J. (2010). Diatom Identification with Mathematical Morphology. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Dewi, D. E. O., Mengko, T. L. R., Purnama, I. K. E., Veldhuizen, A. G., & Wilkinson, M. H. F. (2010). An Improved Olympic Hole-Filling Method for Ultrasound Volume Reconstruction of Human Spine. International Journal of E-Health and Medical Communications, 1(3), 28-40. DOI: 10.4018/jehmc.2010070103
Purnama, I. K. E., Aryanto, K. Y. E., & Wilkinson, M. H. F. (2010). Non-Compactness Attribute Filtering to Extract Retinal Blood Vessels in Fundus Images. International Journal of E-Health and Medical Communications, 1(3), 16-27.
Wilkinson, M., Urbach, E., Jalba, A., & Roerdink, J. (2010). Identification de diatomées par morphologie mathématique. In H. Talbot, & L. Najman (Eds.), Morphologie Mathématique 2: estimation, choix et mise en oeuvre. (pp. 183-192). Hermes Science.
De Wiljes, O., van Elburg, R. A. J., Biehl, M., & Keijzer, F. (2010). Early nervous systems: theoretical background and a preliminary model of neuronal processes. In H. Fellermann, M. Dörr, M. M. Hanczyc, L. Ladegaard Laursen, S. Maurer, D. Merkle, P. A. Monnard, K. Stoy, ... S. Rasmussen (Eds.), Artificial Life XII:proceedings of the twelfth international conference on the synthesis and simulation of living systems.. (pp. 239 - 240). Cambridge: MIT Press.
Witoelar, A. W., Ghosh, A., de Vries, J. J. G., Hammer, B., & Biehl, M. (2010). Window-Based Example Selection in Learning Vector Quantization. Neural computation, 22(11), 2924-2961. DOI: 10.1162/NECO_a_00030
Villmann, T., Haase, S., Schleif, F-M., Hammer, B., & Biehl, M. (2010). The Mathematics of Divergence Based Online Learning in Vector Quantization. In Artificial Neural Networks In Pattern Recognition: Proc. ANNPR 2010. (pp. 108-119). (Lecture Notes in Computer Science; Vol. 5998). Springer. DOI: 10.1007/978-3-642-12159-3_10
Schneider, P., Biehl, M., & Hammer, B. (2010). Hyperparameter learning in probabilistic prototype-based models. Neurocomputing, 73(7-9), 1117-1124. DOI: 10.1016/j.neucom.2009.11.021
Bunte, K., Hammer, B., Wismueller, A., & Biehl, M. (2010). Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data. Neurocomputing, 73(7-9), 1074-1092. DOI: 10.1016/j.neucom.2009.11.017
Schneider, P., Bunte, K., Stiekema, H., Hammer, B., Villmann, T., & Biehl, M. (2010). Regularization in Matrix Relevance Learning. Ieee transactions on neural networks, 21(5), 831-840. DOI: 10.1109/TNN.2010.2042729
Schleif, F-M., Villmann, T., Hammer, B., Schneider, P., & Biehl, M. (2010). Generalized Derivative Based Kernelized Learning Vector Quantization. In C. Fyfe, P. Tino, D. Charles, C. Garcia-Osoro, & H. Yin (Eds.), Proc. Intelligent Data Engineering and Automated Learning - IDEAL 2010. (pp. 21-28). (Lecture Notes in Computer Science; Vol. 6283). Springer. DOI: 10.1007/978-3-642-15381-5_3
Offringa, A. R., Bruyn, A. G. D., Biehl, M., Zaroubi, S., Bernardi, G., & Pandey, V. N. (2010). Post-correlation radio frequency interference classification methods. Monthly Notices of the Royal Astronomical Society, 405(1), 155-167. DOI: 10.1111/j.1365-2966.2010.16471.x
Taylor, A. E., Biehl, M., Hahner, S., Libe, R., Hughes, B. A., Stiekema, H., ... Arlt, W. (2010). Urinary Steroid Profiling as a High-Throughput Screening Tool for the Detection of Malignancy in Patients with Adrenal Tumors. Endocrine reviews, 31(3).
Smedinga, R., Biehl, M., & Kramer, F. (Eds.) (2010). 7th SC@RUG 2010 proceedings: Student Colloquium 2009-2010. Rijksuniversiteit Groningen. Universiteitsbibliotheek.
Mwebaze, E., Schneider, P., Schleif, F-M., Haase, S., Villmann, T., & Biehl, M. (2010). Divergence Based Learning Vector Quantization. In M. Verleysen (Ed.), 18th European Symposium on Artificial Neural Networks (ESANN 2010). (pp. 247-252). d-side publishing.
Bunte, K., Hammer, B., Villmann, T., Biehl, M., & Wismüller, A. (2010). Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization. In M. Verleysen (Ed.), 18th European Symposium on Artificial Neural Networks (ESANN 2010) . (pp. 87-92). d-side publishing.
Offringa, A. R., Bruyn, A. G. D., Zaroubi, S., & Biehl, M. (2010). A LOFAR RFI detection pipeline and its first results. In U. O. Groningen (Ed.), RFI mitigation workshop - RFI 2010, Groningen . (Vol. POS(RFI2010)036). (Proceedings of Science). SISSA.
Hammer, B., Bunte, K., & Biehl, M. (2010). Some steps towards a general principle for dimensionality reduction mappings. In B. Hammer, P. Hitzler, W. Maas, & M. Toussaint (Eds.), Learning paradigms in dynamic environments. (Vol. 10302, pp. 15). (Dagstuhl Seminar Proceedings). Dagstuhl Research Online Publication Server.
Taylor, A., Biehl, M., Hughes, B., Stiekema, H., Schneider, P., Smith, D., ... Arlt, W. (2010). Development of urinary steroid profiling as a high-throughput screening tool for the detection of malignancy in patients with adrenal tumours.
Bunte, K., Haase, S., Biehl, M., & Villmann, T. (2010). Mathematical Foundations of Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization. 52-75: University of Leipzig.
Offringa, A. R., de Bruyn, A. G., Zaroubi, S., & Biehl, M. (2010). Post-correlation RFI detection. In Proceedings of the RFI Mitigation Workshop. 29-31 March 2010. Groningen, the Netherlands. (Vol. 36, pp. 36)
2009
Papari, G., & Petkov, N. (2009). Glass Patterns and Artistic Imaging. In T. Wada, F. Huang, & S. Lin (Eds.), ADVANCES IN IMAGE AND VIDEO TECHNOLOGY, PROCEEDINGS. (pp. 1034-1045). (Lecture Notes in Computer Science; Vol. 5414). BERLIN: Springer.
Papari, G., Campisi, P., Callet, P. L., & Petkov, N. (2009). Artistic Stereo Imaging by Edge Preserving Smoothing. In 2009 IEEE 13TH DIGITAL SIGNAL PROCESSING WORKSHOP & 5TH IEEE PROCESSING EDUCATION WORKSHOP, VOLS 1 AND 2, PROCEEDINGS. (pp. 639-642). NEW YORK: IEEE (The Institute of Electrical and Electronics Engineers).
Papari, G., & Petkov, N. (2009). Continuous Glass Patterns for Painterly Rendering. Ieee transactions on image processing, 18(3), 652-664. DOI: 10.1109/TIP.2008.2009800
Papari, G., & Petkov, N. (2009). Reduced Inverse Distance Weighting Interpolation for Painterly Rendering. In Jiang, & N. Petkov (Eds.), COMPUTER ANALYSIS OF IMAGES AND PATTERNS, PROCEEDINGS. (pp. 509-516). (Lecture Notes in Computer Science; Vol. 5702). BERLIN: Springer.
Bunte, K., Biehl, M., Petkov, N., & Jonkman, M. F. (2009). Adaptive Metrics for Content Based Image Retrieval in Dermatology. In M. Verleysen (Ed.), 17th European Symposium on Artificial Neural Networks (ESANN 2009) . (pp. 129-134). d-side publishing.
Jiang, X., & Petkov, N. (2009). Computer Analysis of Images and Patterns CAIP 2009: Proc. 13th Int. Conf., CAIP 2009, Muenster, Germany, Sept. 2-4, 2009. (Lecture Notes in Computer Science; Vol. 8603). Springer.
Sanchez, L., & Petkov, N. (2009). Estimation of Boar Sperm Status Using Intracellular Density Distribution in Grey Level Images. In EPRINTS-BOOK-TITLE. Springer.
Azzopardi, G., & Smeraldi, F. (2009). Variance Ranklets: Orientation-selective rank features for contrast modulations. In British Machine Vision Conference.
Wilkinson, M. H. F., & Roerdink, J. B. T. M. (2009). Mathematical Morphology and Its Application to Signal and Image Processing. In EPRINTS-BOOK-TITLE. Springer.
Salembier, P., & Wilkinson, M. H. F. (2009). Connected Operators: A review of region-based morphological image processing techniques. Ieee signal processing magazine, 26(6), 136-157. DOI: 10.1109/MSP.2009.934154
Dewi, D. E. O., Wilkinson, M. H. F., Mengko, T. L. R., Purnama, I. K. E., Ooijen, P. M. A. V., Veldhuizen, A. G., ... Verkerke, G. J. (2009). 3D Ultrasound Reconstruction of Spinal Images using an Improved Olympic Hole-Filling Method. In ICICI-BME: 2009 INTERNATIONAL CONFERENCE ON INSTRUMENTATION, COMMUNICATION, INFORMATION TECHNOLOGY, AND BIOMEDICAL ENGINEERING. (pp. 350-354). NEW YORK: IEEE (The Institute of Electrical and Electronics Engineers).
Wilkinson, M. H. F. (2009). An Axiomatic Approach to Hyperconnectivity. In M. H. F. Wilkinson, & J. B. T. M. Roerdink (Eds.), MATHEMATICAL MORPHOLOGY AND ITS APPLICATION TO SIGNAL AND IMAGE PROCESSING. (pp. 35-46). (Lecture Notes in Computer Science; Vol. 5720). BERLIN: Springer.
Wilkinson, M. H. F. (2009). Hyperconnectivity, Attribute-Space Connectivity and Path Openings: Theoretical Relationships. In M. H. F. Wilkinson, & J. B. T. M. Roerdink (Eds.), MATHEMATICAL MORPHOLOGY AND ITS APPLICATION TO SIGNAL AND IMAGE PROCESSING. (pp. 47-58). (Lecture Notes in Computer Science; Vol. 5720). BERLIN: Springer.
Kiwanuka, F. N., Ouzounis, G. K., & Wilkinson, M. H. F. (2009). Surface-Area-Based Attribute Filtering in 3D. In M. H. F. Wilkinson, & J. B. T. M. Roerdink (Eds.), MATHEMATICAL MORPHOLOGY AND ITS APPLICATION TO SIGNAL AND IMAGE PROCESSING. (pp. 70-81). (Lecture Notes in Computer Science; Vol. 5720). BERLIN: Springer.
Land, S., & Wilkinson, M. H. F. (2009). A Comparison of Spatial Pattern Spectra. In M. H. F. Wilkinson, & J. B. T. M. Roerdink (Eds.), MATHEMATICAL MORPHOLOGY AND ITS APPLICATION TO SIGNAL AND IMAGE PROCESSING. (pp. 92-103). (Lecture Notes in Computer Science; Vol. 5720). BERLIN: Springer.
Purnama, K., Wilkinson, M. H. F., Veldhuizen, A. G., Ooijen, P. M. A. V., Sardjono, T. A., Lubbers, J., & Verkerke, G. J. (2009). Following Scoliosis Progression in the Spine using Ultrasound Imaging. In O. Dossel, & W. C. Schlegel (Eds.), WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING: DIAGNOSTIC IMAGING. (2 ed., Vol. 25, pp. 600-602). (IFMBE Proceedings; Vol. 25). NEW YORK: Springer.
Ouzounis, G. K., Giannakopoulos, S., Simopoulos, C. E., & Wilkinson, M. H. F. (2009). Robust extraction of urinary stones from CT data using attribute filters. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Land, S., & Wilkinson, M. H. F. (2009). A Comparison of Spatial Pattern Spectra. In EPRINTS-BOOK-TITLE. Springer.
Ferdosi, B. J., Buddelmeijer, H., Helmi, A., Trager, S. C., Valentijn, E. A., Wilkinson, M. H. F., ... Roerdink, J. B. T. M. (2009). Comparison of Density Estimation Methods for Astronomical Datasets.
Witoelar, A., & Biehl, M. (2009). Phase transitions in vector quantization and neural gas. Neurocomputing, 72(7-9), 1390-1397. DOI: 10.1016/j.neucom.2008.10.023
Schleif, F-M., Biehl, M., & Vellido, A. (2009). Advances in machine learning and computational intelligence. Neurocomputing, 72(7-9), 1377-1378. DOI: 10.1016/j.neucom.2008.12.013
Schneider, P., Biehl, M., & Hammer, B. (2009). Adaptive Relevance Matrices in Learning Vector Quantization. Neural computation, 21(12), 3532-3561. DOI: 10.1162/neco.2009.11-08-908
Schneider, P., Biehl, M., & Hammer, B. (2009). Distance Learning in Discriminative Vector Quantization. Neural computation, 21(10), 2942-2969. DOI: 10.1162/neco.2009.10-08-892
Bunte, K., Hammer, B., & Biehl, M. (2009). Nonlinear Dimension Reduction and Visualization of Labeled Data. In Jiang, & N. Petkov (Eds.), COMPUTER ANALYSIS OF IMAGES AND PATTERNS, PROCEEDINGS. (pp. 1162-1170). (Lecture Notes in Computer Science; Vol. 5702). BERLIN: Springer. DOI: 10.1007/978-3-642-03767-2_141
Biehl, M., Hammer, B., Verleysen, M., & Villmann, T. (Eds.) (2009). Similarity-Based Clustering: Recent Developments and Biomedical Applications. (Lecture Notes in Artificial Intelligence; Vol. 5400). Springer. DOI: 10.1007/978-3-642-01805-3
Bunte, K., Hammer, B., Schneider, P., & Biehl, M. (2009). Nonlinear Discriminative Data Visualization. In M. Verleysen (Ed.), 17th European Symposium on Artificial Neural Networks (ESANN 2009). (pp. 65-70). d-side publishing.
Schneider, P., Biehl, M., & Hammer, B. (2009). Hyperparameter Learning in Robust Soft LVQ. In M. Verleysen (Ed.), 17th European Symposium on Artificial Neural Networks (ESANN 2009). (pp. 517-522). d-side publishing.
Witoelar, A., Biehl, M., & Hammer, B. (2009). Equilibrium Properties of Offline LVQ. In M. Verleysen (Ed.), 17th European Symposium on Artificial Neural Networks (ESANN 2009). (pp. 535-540). d-side publishing.
Strickert, M., Keilwagen, J., Schleif, F-M., Villmann, T., & Biehl, M. (2009). Matrix Metric Adaptation for Improved Linear Discriminant Analysis of Biomedical Data. In J. C. E. al. (Ed.), 10th Int. Work-Conference on Artificial Neural Networks (IWANN 2009). (Vol. 5517, pp. 933-940). (Lecture Notes in Computer Science). Springer. DOI: 10.1007/978-3-642-02478-8_117
Villmann, T., Hammer, B., & Biehl, M. (2009). Some theoretical aspects of the Neural Gas Vector Quantizer. In M. Biehl, B. Hammer, T. Villmann, & M. Verleysen (Eds.), Similarity Based Clustering. (Vol. 5400, pp. 23-34). (Lecture Notes in Artificial Intelligence). Springer. DOI: 10.1007/978-3-642-01805-3_2
Biehl, M., Hammer, B., Schneider, P., & Villmann, T. (2009). Metric Learning for Prototype-based classification. In M. Bianchini, M. Maggini, F. Scarselli, & L. Jain (Eds.), Advances in Neural Information Paradigms. (Vol. 247, pp. 183-199). (Springer Studies in Computational Intelligence). Springer. DOI: 10.1007/978-3-642-04003-0_8
Geweniger, T., Schneider, P., Schleif, F-M., Biehl, M., & Villmann, T. Extending RSLVQ to handle data points with uncertain class assignments
Biehl, M., Hammer, B., Schleif, F-M., Schneider, P., & Villmann, T. Stationarity of Matrix Relevance Learning Vector Quantization
Arlt, W., Hahner, S., Libe, R., Hughes, B. A., Biehl, M., Stiekema, H., ... Stewart, P. M. (2009). Urinary steroid profiling as a biomarker tool for the detection of adrenal malignancy - results of the EURINE ACC study.
Biehl, M., Hammer, B., Hochreiter, S., Kremer, S. C., & Villmann, T. (Eds.) (2009). Similarity-based learning on structures.
Biehl, M., Caticha, N., & Riegler, P. (2009). Statistical Mechanics of On-line Learning. In M. Biehl, B. Hammer, M. Verleysen, & T. Villmann (Eds.), Similarity-Based Clustering: Recent Developments and Biomedical Applications: Lecture Notes in Computer Science. (Vol. 5400, pp. 1). Springer. DOI: 10.1007/978-3-642-01805-3_1
Vries, G-J. D., & Biehl, M. (2009). Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition. In M. Biehl, B. Hammer, S. Hochreiter, S. C. Kremer, & T. Villmann (Eds.), Similarity-based learning on structures. (Dagstuhl Seminar Proceedings). Dagstuhl, Germany: Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany.
Biehl, M., Hammer, B., Hochreiter, S., Kremer, S. C., & Villmann, T. (2009). 09081 Abstracts Collection -- Similarity-based learning on structures. In M. Biehl, B. Hammer, S. Hochreiter, S. C. Kremer, & T. Villmann (Eds.), Similarity-based learning on structures. (Dagstuhl Seminar Proceedings). Dagstuhl, Germany: Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany.
Biehl, M., Hammer, B., Hochreiter, S., Kremer, S. C., & Villmann, T. (2009). 09081 Summary -- Similarity-based learning on structures. In M. Biehl, B. Hammer, S. Hochreiter, S. C. Kremer, & T. Villmann (Eds.), Similarity-based learning on structures. (Dagstuhl Seminar Proceedings). Dagstuhl, Germany: Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany.
2008
Papari, G., & Petkov, N. (2008). Adaptive Pseudo Dilation for Gestalt Edge Grouping and Contour Detection. Ieee transactions on image processing, 17(10), 1950-1962. DOI: 10.1109/TIP.2008.2002306
Alegre, E., Biehl, M., Petkov, N., & Sanchez, L. (2008). Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ. Computers in biology and medicine, 38(4), 461-468. DOI: 10.1016/j.compbiomed.2008.01.005
Papari, G., & Petkov, N. (2008). Fast multiresolution contour completion. In J. T. Astola, K. O. Egiazarian, & E. R. Dougherty (Eds.), IMAGE PROCESSING: ALGORITHMS AND SYSTEMS VI. (Proceedings of SPIE; Vol. 6812). BELLINGHAM: SPIE - INT SOC OPTICAL ENGINEERING.
Papari, G., & Petkov, N. (2008). Artistic imaging by edge enhancing smoothing. Perception, 37, 64-64.
Petkov, N. (Author), & Wieling, M. (Author). (2008). Gabor filter for image processing and computer vision.
Hateren, J. H. V. (2008). Fast Recursive Filters for Simulating Nonlinear Dynamic Systems. Neural computation, 20(7), 1821-1846.
Snippe, H. P., & van Hateren, J. H. (2008). Optimal nonlinear signal transmission: a comparison of mutual information and estimation error. Perception, 37, 105-105.
Tushabe, F., & Wilkinson, M. H. F. (2008). Content-Based Image Retrieval Using Combined 2D Attribute Pattern Spectra. In C. Peters, Jikoun, T. Mandl, H. Muller, D. W. Oard, A. Penas, Petras, ... D. Santos (Eds.), ADVANCES IN MULTILINGUAL AND MULTIMODAL INFORMATION RETRIEVAL. (pp. 554-561). (LECTURE NOTES IN COMPUTER SCIENCE; Vol. 5152). BERLIN: University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Wilkinson, M. H. F. (2008). Connected Filtering by Reconstruction: Basis and New Advances. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Wilkinson, M. H. F., Gao, H., Hesselink, W. H., Jonker, J-E., & Meijster, A. (2008). Concurrent computation of attribute filters on shared memory parallel machines. Ieee transactions on pattern analysis and machine intelligence, 30(10), 1800-1813. DOI: 10.1109/TPAMI.2007.70836
Urbach, E. R., & Wilkinson, M. H. F. (2008). Efficient 2-D Grayscale Morphological Transformations With Arbitrary Flat Structuring Elements. Ieee transactions on image processing, 17(1), 1-8. DOI: 10.1109/TIP.2007.912582
Wilkinson, M. H. F. (2008). CONNECTED FILTERING BY RECONSTRUCTION: BASIS AND NEW ADVANCES. In 2008 15TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-5. (pp. 2180-2183). (IEEE International Conference on Image Processing (ICIP)). NEW YORK: IEEE (The Institute of Electrical and Electronics Engineers).
Ferdosi, B. J., Buddelmeijer, H., Helmi, A., Trager, S. C., Valentijn, E. A., Wilkinson, M. H. F., ... Roerdink, J. B. T. M. (2008). Visualizing Astronomical Data. Poster session presented at SIREN: Scientific ICT Research Event Netherlands, 29 September 2008, VU Amsterdam, 2008. Poster., Amsterdam, Netherlands.
Strickert, M., Witzel, K., Keilwagen, J., Mock, H-P., Schneider, P., Biehl, M., & Villmann, T. (2008). Adaptive Matrix Metrics for Attribute Dependence Analysis in Differential High-Throughput Data. In Proc. 5th International Workshop on Computational Systems Biology: WCSB 2008. (Vol. 41, pp. 181-184). (Tampere International Center for Signal Processing TICSP; Vol. 41). University of Tampere.
Witoelar, A., Ghosh, A., & Biehl, M. (2008). Phase transitions in Vector Quantization. In M. Verleysen (Ed.), Proc. European Symposium on Artificial Neural Networks: ESANN 2008. (pp. 221-226). d-side publishing.
Schneider, P., Schleif, F-M., Villmann, T., & Biehl, M. (2008). Generalized Matrix Learning Vector Quantizer for the Analysis of Spectral Data. In M. Verleysen (Ed.), Proc. European Symposium on Artificial Neural Networks: ESANN 2008. (pp. 451-456). d-side publishing.
Witoelar, A., Biehl, M., Ghosh, A., & Hammer, B. (2008). Learning dynamics and robustness of vector quantization and neural gas. Neurocomputing, 71(7-9), 1210-1219. DOI: 10.1016/j.neucom.2007.11.022
Weber, S., Biehl, M., Kotrla, M., & Kinzel, W. (2008). Simulation of self-assembled nanopatterns in strained 2D alloys on the face centered cubic (111) surface. Journal of Physics-Condensed Matter, 20(26), [265004]. DOI: 10.1088/0953-8984/20/26/265004
Strickert, M., Schneider, P., Keilwagen, J., Villmann, T., Biehl, M., & Hammer, B. (2008). Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics. In Artificial Neural Networks in Pattern Recognition: ANNPR 2008. (pp. 78-89). (Lecture Notes in Artificial Intelligence; Vol. 5064). Springer. DOI: 10.1007/978-3-540-69939-2_8
Rossi, F., Biehl, M., & Bahón, C. A. (2008). Progress in modeling, theory, and application of computational intelligence. Neurocomputing, 71(7-9), 1117-1119. DOI: 10.1016/j.neucom.2007.12.019
Schneider, P., Bunte, K., Stiekema, H., Hammer, B., Villmann, T., & Biehl, M. Regularization in Matrix Relevance Learning
Bunte, K., Schneider, P., Hammer, B., Schleif, F. M., Villmann, T., & Biehl, M. Discriminative visualization by limited rank matrix learning
Schneider, P., Biehl, M., & Hammer, B. (2008). Matrix adaptation in discriminative vector quantization. (IFL Technical Report Series). Technical University Clausthal.
2007
Petkov, N., & Subramanian, E. (2007). Motion detection, noise reduction, texture suppression, and contour enhancement by spatiotemporal Gabor filters with surround inhibition. Biological Cybernetics, 97(5-6), 423-439. DOI: 10.1007/s00422-007-0182-0
Petkov, N., Alegre, E., Biehl, M., & Sánchez, L. (2007). LVQ acrosome integrity assessment of boar sperm cells. In J. M. R. S. Tavares, & R. M. N. Jorge (Eds.), COMPUTATIONAL MODELLING OF OBJECTS REPRESENTED IN IMAGES: FUNDAMENTALS, METHODS AND APPLICATIONS. (pp. 337-342). (Proceedings and Monographs in Engineering, Water and Earth Sciences). LONDON: Taylor & Francis Group.
Papari, G., Petkov, N., & Campisi, P. (2007). Artistic edge and corner enhancing smoothing. Ieee transactions on image processing, 16(10), 2449-2462. DOI: 10.1109/TIP.2007.903912
Papari, G., Campisi, P., Petkov, N., & Neri, A. (2007). A biologically motivated multiresolution approach to contour detection. Eurasip journal on advances in signal processing, [71828]. DOI: 10.1155/2007/71828
Papari, G., Campisi, P., & Petkov, N. (2007). Multilevel surround inhibition a biologically inspired contour detector - art. no. 649702. In J. T. Astola, K. O. Egiazarian, & E. R. Dougherty (Eds.), Image Processing: Algorithms and Systems V. (pp. 49702-49702). (PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE); Vol. 6497). BELLINGHAM: SPIE - INT SOC OPTICAL ENGINEERING.
Papari, G., Petkov, N., & Campisi, P. (2007). Edge and corner preserving smoothing for artistic imaging. In J. T. Astola, K. O. Egiazarian, & E. R. Dougherty (Eds.), IMAGE PROCESSING: ALGORITHMS AND SYSTEMS V. (Proceedings of SPIE; Vol. 6497). BELLINGHAM: SPIE - INT SOC OPTICAL ENGINEERING.
Petkov, N. (2007). Contour detection by surround suppression of texture. In J. M. R. S. Tavares, & R. M. N. Jorge (Eds.), COMPUTATIONAL MODELLING OF OBJECTS REPRESENTED IN IMAGES: FUNDAMENTALS, METHODS AND APPLICATIONS. (pp. 29-32). (Proceedings and Monographs in Engineering, Water and Earth Sciences). LONDON: Taylor & Francis Group.
Snippe, H. P., & Hateren, J. H. V. (2007). Dynamics of nonlinear feedback control. Neural computation, 19(5), 1179-1214.
Hateren, J. H. V., & Snippe, H. P. (2007). Simulating human cones from mid-mesopic up to high-photopic luminances. JOURNAL OF VISION, 7(4), [1]. DOI: 10.1167/7.4.1
Hateren, J. H. V. (2007). A model of spatiotemporal signal processing by primate cones and horizontal cells. JOURNAL OF VISION, 7(3), [3]. DOI: 10.1167/7.3.3
Purnama, K. E., Wilkinson, M. H. F., Veldhuizen, A. G., Ooijen, P. M. A. V., Sardjono, T. A., & Verkerke, G. J. (2007). Ultrasound for human spine: vertebral features enhancement using length attribute filter. Default journal.
Purnama, K. E., Wilkinson, M. H. F., Veldhuizen, A. G., Ooijen, P. M. A. V., Sardjono, T. A., & Verkerke, G. J. (2007). Ultrasound imaging for human spine: imaging and analysis. Default journal.
Wilkinson, M. H. F. (2007). Attribute-space connectivity and connected filters. Image and vision computing, 25(4), 426-435. DOI: 10.1016/j.imavis.2006.04.015
Urbach, E. R., Roerdink, J. B. T. M., & Wilkinson, M. H. F. (2007). Connected shape-size pattern spectra for rotation and scale-invariant classification of gray-scale images. Ieee transactions on pattern analysis and machine intelligence, 29(2), 272-285.
Ouzounis, G. K., & Wilkinson, M. H. F. (2007). A parallel implementation of the dual-input Max-Tree algorithm for attribute filtering. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Westenberg, M. A., Roerdink, J. B. T. M., & Wilkinson, M. H. F. (2007). Volumetric attribute filtering and interactive visualization using the max-tree representation. Ieee transactions on image processing, 16(12), 2943-2952. DOI: 10.1109/TIP.2007.909317
Sardjono, T. A., Wilkinson, M. H. F., Ooijen, P. M. A. V., Veldhuizen, A. G., Purnama, K. E., & Verkerke, G. J. (2007). Spinal curvature determination from an X-ray image using a deformable model. In F. Ibrahim, N. A. A. Osman, J. Usman, & N. A. Kadri (Eds.), 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006. (pp. 291-295). (IFMBE Proceedings; Vol. 15). NEW YORK: Springer.
Ouzounis, G. K., & Wilkinson, M. H. F. (2007). Mask-based second-generation connectivity and attribute filters. Ieee transactions on pattern analysis and machine intelligence, 29(6), 990-1004. DOI: 10.1109/TPAMI.2007.1045
Urbach, E., Roerdink, J., & Wilkinson, M. (2007). Connected morphological operators improve image classification. SPIE Newsroom. DOI: 10.1117/2.1200706.0699
Sardjono, T. A., Wilkinson, M., Ooyen, P., Veldhuizen, A. G., Purnama, K., & Verkerke, G. J. (2007). A new approach for automatic curvature determination from a frontal X-ray image of a scoliotic patient. International Journal of Computer Assisted Radiology and Surgery, 2 suppl 1, S445.
Walther, M., Biehl, M., & Kinzel, W. (2007). Formation and consequences of misfit dislocations in heteroepitaxial growth. Physica Status Solidi (C), 4(9). DOI: 10.1002/pssc.200775414
Biehl, M., Breitling, R., & Li, Y. (2007). Analysis of tiling microarray data by learning vector quantization and relevance learning. In H. Yin, P. Tino, E. Corchado, W. Byrne, & Yao (Eds.), INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2007. (pp. 880-889). (LECTURE NOTES IN COMPUTER SCIENCE; Vol. 4881). BERLIN: Springer. DOI: 10.1007/978-3-540-77226-2_88
Biehl, M., Ghosh, A., & Hammer, B. (2007). Dynamics and generalization ability of LVQ algorithms. Journal of Machine Learning Research, 8, 323-360.
Witoelar, A., Biehl, M., & Hammer, B. (2007). Learning Vector Quantization: generalization ability and dynamics of competing prototypes. In Proc. International Workshop on Self-Organizing Maps: WSOM 2007. Univ. of Bielefeld.
Schneider, P., Biehl, M., Schleif, F-M., & Hammer, B. (2007). Advanced metric adaptation in Generalized LVQ for classification of mass spectrometry data. In Proc. 6th International Workshop on Self-Organizing Maps: WSOM 2007. Univ. of Bielefeld.
Witoelar, A., Biehl, M., Ghosh, A., & Hammer, B. (2007). On the Dynamics of Vector Quantization and Neural Gas. In M. Verleysen (Ed.), Proc. European Symposium on Artificial Neural Networks: ESANN 2007. (pp. 127-132). d-side publishing.
Schneider, P., Biehl, M., & Hammer, B. (2007). Relevance Matrices in LVQ. In M. Verleysen (Ed.), Proc. European Symposium on Artificial Neural Networks: ESANN 2007. (pp. 37-42). d-side publishing.
Kotrla, M., Weber, S., Much, F., Biehl, M., & Kinzel, W. (2007). Self-assembled nano-patterns in strained 2D metallic alloys: Droplets vs. stripes. In Proc. NANO'07 conference in Brno/Cz. (Vol. 13, pp. 70-75). (Acta Metallurgica Slovaca).
Witoelar, A., Biehl, M., & Hammer, B. (2007). Learning Vector Quantization: generalization ability and dynamics of competing prototypes. In M. Biehl, B. Hammer, M. Verleysen, & T. Villmann (Eds.), Similarity-based Clustering and its Application to Medicine and Biology. (Dagstuhl Seminar Proceedings). Dagstuhl, Germany: Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany.
Biehl, M., Hammer, B., Verleysen, M., & Villmann, T. (2007). 07131 Summary -- Similarity-based Clustering and its Application to Medicine and Biology. In M. Biehl, B. Hammer, M. Verleysen, & T. Villmann (Eds.), Similarity-based Clustering and its Application to Medicine and Biology. (Dagstuhl Seminar Proceedings). Dagstuhl, Germany: Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany.
Biehl, M., Merényi, E., & Rossi, F. (2007). Advances in computational intelligence and learning. Neurocomputing, 70(7-9), 1117-1119. DOI: 10.1016/j.neucom.2006.12.001
2006
Petkov, N., & Kalfsbeek, F. Web-enabled biologically motivated image processing algorithms
Biehl, M., Pasma, P., Pijl, M., Sánchez, L., & Petkov, N. (2006). Classification of Boar Sperm Head Images using Learning Vector Quantization. In M. Verleysen (Ed.), Proc. European Symposium on Artificial Neural Networks: ESANN 2006. d-side publishing.
Papari, G., Campisi, P., Petkov, N., & Neri, A. (2006). A Multiscale Approach to Contour Detection by Texture Suppression. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Papari, G., Campisi, P., Petkov, N., & Neri, A. (2006). Contour Detection by Multiresolution Surround Inhibition. Default journal.
Ghosh, A., & Petkov, N. (2006). Effect of high curvature point deletion on the performance of two contour based shape recognition algorithms. International Journal of Pattern Recognition and Artificial Intelligence, 20(6), 913-924.
Papari, G., Campisi, P., Petkov, N., & Neri, A. (2006). Contour Detection by Surround Inhibition in the Circular Harmonic Functions Domain. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Sánchez, L., Petkov, N., & Alegre, E. (2006). Statistical Approach to Boar Semen Evaluation Using Intracellular Intensity Distribution of Head Images. Cellular and molecular biology, 52(6), 38-43. DOI: 10.1170/T736
Papari, G., Campisi, P., Petkov, N., & Neri, A. (2006). A multiscale approach to contour detection by texture suppression - art. no. 60640D. In E. R. Dougherty, J. T. Astola, K. O. Egiazarian, N. M. Nasrabadi, & S. A. Rizvi (Eds.), Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning. (pp. D640-D640). (PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE); Vol. 6064). BELLINGHAM: SPIE - INT SOC OPTICAL ENGINEERING.
Papari, G., Campisi, P., Petkov, N., & Neri, A. (2006). Contour detection by multiresolution surround inhibition. In 2006 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP 2006, PROCEEDINGS. (pp. 749-752). (IEEE International Conference on Image Processing (ICIP)). NEW YORK: IEEE (The Institute of Electrical and Electronics Engineers).
Petkov, N., Alegre, E., Biehl, M., & Sánchez, L. (2006). LVQ acrosome integrity assessment of boar sperm cells. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
PETKOV, N. (2006). Tuning and auto-tuning of controllers : a survey. Fundamental Sciences and Applications, 13(4), 273.
Papari, G., Campisi, P., Neri, A., & Petkov, N. (2006). Multiresolution Contour Detection Using Two-level Surround Inhibition. In 2nd International Conference on Computers and Devices for Communication (CODEC06).
Karmeier, K., Hateren, J. H. V., Kern, R., & Egelhaaf, M. (2006). Encoding of Naturalistic Optic Flow by a Population of Blowfly Motion-Sensitive Neurons. Journal of Neurophysiology, 96(3), 1602-1614. DOI: 10.1152/jn.00023.2006
Kern, R., Hateren, J. H. V., & Egelhaaf, M. (2006). Representation of behaviourally relevant information by blowfly motion-sensitive visual interneurons requires precise compensatory head movements. Journal of Experimental Biology, 209(7), 1251-1260. DOI: 10.1242/jeb.02127
Hateren, J. H. V., & Snippe, H. P. (2006). Phototransduction in primate cones and blowfly photoreceptors: different mechanisms, different algorithms, similar response. Journal of comparative physiology a-Neuroethology sensory neural and behavioral physiology, 192(2), 187-197. DOI: 10.1007/s00359-005-0060-y
Hateren, J. H. V., & Lamb, T. D. (2006). The photocurrent response of human cones is fast and monophasic. BMC Neuroscience, 7, [34]. DOI: 10.1186/1471-2202-7-34
Hateren, J. H. V. (2006). Encoding of High Dynamic Range Video With a Model of Human Cones. Acm transactions on graphics, 25(4), 1380-1399.
Wilkinson, M. H. F. (2006). Fifth Quinquennial Review 2001-2006. In Fifth Quinquennial Review 2001-2006. Dutch Society for Pattern Recognition and Image Processing.
Ouzounis, G. K., & Wilkinson, M. H. F. Filament enhancement by non-linear volumetric filtering using clustering-based connectivity
Wilkinson, M. H. F., Veldhuizen, A. G., & Verkerke, B. Automatic extraction of vertebral parts from ultrasound images
Wilkinson, M. H. F., Veldhuizen, A. G., & Verkerke, B. Imaging the whole spine using a free-hand 3-d ultrasound system
Wilkinson, M. H. F. Effiecient 2-D gray-scale dilations and erosions with arbitrary flat structuring elements
Jalba, A. C., Wilkinson, M. H. F., & Roerdink, J. B. T. M. (2006). Shape representation and recognition through morphological curvature scale spaces. Ieee transactions on image processing, 15(2), 331-341. DOI: 10.1109/TIP.2005.860606
Ouzounis, G. K., & Wilkinson, M. H. F. (2006). Filament Enhancement by Non-linear Volumetric Filtering Using Clustering-Based Connectivity. In N. Zheng, Jiang, & lan (Eds.), ADVANCES IN MACHINE VISION, IMAGE PROCESSING, AND PATTERN ANALYSIS. (pp. 317-327). (Lecture Notes in Computer Science; Vol. 4153). BERLIN: Springer.
Wilkinson, M. H. F. (2006). Mathematical Modelling of Predatory Prokaryotes. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Urbach, E. R., & Wilkinson, M. H. F. (2006). Efficient 2-D Gray-Scale Dilations and Erosions with Arbitrary Flat Structuring Elements. In 2006 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP 2006, PROCEEDINGS. (pp. 1573-1576). (IEEE International Conference on Image Processing (ICIP)). NEW YORK: IEEE (The Institute of Electrical and Electronics Engineers).
Halliday, C., Carter, D., Bridges, T. J., Jackson, Z. C., Wilkinson, M. I., Quinn, D. P., ... Irwin, M. J. (2006). Planetary nebula velocities in the disc and bulge of M31. Monthly Notices of the Royal Astronomical Society, 369(1), 97-119. DOI: 10.1111/j.1365-2966.2006.10364.x
Merrett, H. R., Merrifield, M. R., Douglas, N. G., Kuijken, K., Romanowsky, A. J., Napolitano, N. R., ... Bridges, T. J. (2006). A deep kinematic survey of planetary nebulae in the Andromeda galaxy using the Planetary Nebula Spectrograph. Monthly Notices of the Royal Astronomical Society, 369(1), 120-142. DOI: 10.1111/j.1365-2966.2006.10268.x
Biehl, M., Hammer, B., & Schneider, P. Matrix Learning in Learning Vector Quantization
Biehl, M., Ghosh, A., & Hammer, B. (2006). Learning vector quantization: The dynamics of winner-takes-all algorithms. Neurocomputing, 69(7-9), 660-670. DOI: 10.1016/j.neucom.2005.12.007
Ghosh, A., Biehl, M., & Hammer, B. (2006). Performance analysis of LVQ algorithms: A statistical physics approach. Neural Networks, 19(6-7), 817-829. DOI: 10.1016/j.neunet.2006.05.010
Biehl, M., Hammer, B., & Schneider, P. (2006). Matrix Learning in Learning Vector Quantization. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
2005
Petkov, N., & Kalfsbeek, F. (2005). Web-enabled biologically motivated image processing algorithms. Unknown Journal.
Petkov, N. (2005). Grid Computing. Euclides, 80(4), 249 - 253.
Ghosh, A., & Petkov, N. (2005). Incomplete contour representations and shape descriptors: ICR test studies. In M. D. Gregorio, DiMaio, M. Frucci, & C. Musio (Eds.), BRAIN, VISION, AND ARTIFICIAL INTELLIGENCE, PROCEEDINGS. (pp. 416-425). (LECTURE NOTES IN COMPUTER SCIENCE; Vol. 3704). BERLIN: Springer.
Ghosh, A., & Petkov, N. (2005). Robustness of shape descriptors to incomplete contour representations. Ieee transactions on pattern analysis and machine intelligence, 27(11), 1793-1804.
Ghosh, A., & Petkov, N. (2005). A cognitive evaluation procedure for contour based shape descriptors. Default journal, 2(4), 237 - 252.
Petkov, N., & Visser, W. T. (2005). Modifications of center-surround, spot detection and dot-pattern selective operators. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Sánchez, L., Petkov, N., & Alegre, E. (2005). Classification of boar spermatozoid head images using a model intracellular density distribution. In A. Sanfeliu, & M. L. Cortes (Eds.), PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS AND APPLICATIONS, PROCEEDINGS. (pp. 154-160). (LECTURE NOTES IN COMPUTER SCIENCE; Vol. 3773). BERLIN: Springer.
Sánchez, L., Petkov, N., & Alegre, E. (2005). Statistical approach to boar semen head classification based on intracellular intensity distribution. In A. Gagalowicz, & W. Philips (Eds.), COMPUTER ANALYSIS OF IMAGES AND PATTERNS, PROCEEDINGS. (pp. 88-95). (LECTURE NOTES IN COMPUTER SCIENCE; Vol. 3691). BERLIN: Springer.
Papari, G., & Petkov, N. (2005). Algorithm that mimics human perceptual grouping of dot patterns. In M. D. Gregorio, DiMaio, M. Frucci, & C. Musio (Eds.), BRAIN, VISION, AND ARTIFICIAL INTELLIGENCE, PROCEEDINGS. (pp. 497-506). (LECTURE NOTES IN COMPUTER SCIENCE; Vol. 3704). BERLIN: Springer.
Ghosh, A., & Petkov, N. (2005). GAP test: A cognitive evaluation procedure for shape descriptors. In M. Ishikawa, S. Hashimoto, M. Paprzycki, E. Barakova, K. Yoshida, M. Koppen, D. W. Corne, ... A. Abraham (Eds.), HIS'04: Fourth International Conference on Hybrid Intelligent Systems, Proceedings. (pp. 334-339). LOS ALAMITOS: IEEE (The Institute of Electrical and Electronics Engineers).
Lindemann, J. P., Kern, R., Hateren, J. H. V., Ritter, H., & Egelhaaf, M. (2005). On the computations analyzing natural optic flow: Quantitative model analysis of the blowfly motion vision pathway. The Journal of Neuroscience, 25(27), 6435-6448. DOI: 10.1523/JNEUROSCI.1132-05.2005
Heitwerth, J., Kern, R., Hateren, J. H. V., & Egelhaaf, M. (2005). Motion adaptation leads to parsimonious encoding of natural optic flow by blowfly motion vision system. Journal of Neurophysiology, 94(3), 1761-1769. DOI: 10.1152/jn.00308.2005
Kern, R., Hateren, J. H. V., Michaelis, C., Lindemann, J. P., & Egelhaaf, M. (2005). Function of a fly motion-sensitive neuron matches eye movements during free flight. PLOS BIOLOGY, 3(6), 1130-1138. [171]. DOI: 10.1371/journal.pbio.0030171
Hateren, J. H. V., Kern, R., Schwerdtfeger, G., & Egelhaaf, M. (2005). Function and coding in the blowfly H1 neuron during naturalistic optic flow. The Journal of Neuroscience, 25(17), 4343-4352. DOI: 10.1523/JNEUROSCI.0616-05.2005
Jalba, A. C., Wilkinson, M. H. F., Roerdink, J. B. T. M., Bayer, M. M., & Juggins, S. (2005). Automatic diatom identification using contour analysis by morphological curvature scale spaces. Machine Vision and Applications, 16(4), 217 - 228. DOI: 10.1007/s00138-005-0175-8
Ouzounis, G. K., & Wilkinson, M. H. F. Generalized Connected Morphological Operators for Robust Shape Extraction
Ouzounis, G. K., & Wilkinson, M. H. F. (2005). Second-Order Connectivity and Oversegmentation. Unknown Journal, 366 - 371.
urbach, E. R., & Wilkinson, M. H. F. Image processing and analysis using shape filters with vector attributes
urbach, E. R., Boersma, N. J., & Wilkinson, M. H. F. Vector-Attribute Filters
Wilkinson, M. H. F. (2005). Kvantove mechanicka interpretace homeopatie. Unknown Journal.
Wilkinson, M. H. F., Ouzounis, G. K., & urbach, E. R. Connected Morphological Image Analysis
Wilkinson, M. H. F. (2005). Attribute-space connected filters. In C. Ronse, L. Najman, & E. Decenciere (Eds.), Mathematical Morphology: 40 years on. (pp. 85-94). (COMPUTATIONAL IMAGING AND VISION; Vol. 30). DORDRECHT: Springer.
Ouzounis, G. K., & Wilkinson, M. H. F. (2005). Countering oversegmentation in partitioning-based connectivities. In 2005 International Conference on Image Processing (ICIP), Vols 1-5. (pp. 3637-3640). (IEEE International Conference on Image Processing (ICIP)). NEW YORK: IEEE (The Institute of Electrical and Electronics Engineers).
Urbach, E. R., Boersma, N. J., & Wilkinson, M. H. F. (2005). Vector-attribute filters. In C. Ronse, L. Najman, & E. Decenciere (Eds.), MATHEMATICAL MORPHOLOGY: 40 YEARS ON. (pp. 95-104). (Computational Imaging and Vision; Vol. 30). DORDRECHT: Springer.
Ouzounis, G. K., & Wilkinson, M. H. F. (2005). Second-order connected attribute filters using max-trees. In C. Ronse, L. Najman, & E. Decenciere (Eds.), Mathematical Morphology: 40 years on. (pp. 65-74). (COMPUTATIONAL IMAGING AND VISION; Vol. 30). DORDRECHT: Springer.
Wilkinson, M. I., Vallenari, A., Turon, C., Munari, U., Katz, D., Bono, G., ... Viala, Y. (2005). Spectroscopic survey of the Galaxy with Gaia - II. The expected science yield from the Radial Velocity Spectrometer. Monthly Notices of the Royal Astronomical Society, 359(4), 1306-1335. DOI: 10.1111/j.1365-2966.2005.09012.x
Diepgen, T. L., Coenraads, P. J., Wilkinson, M., Basketter, D. A., & Lepoittevin, J. P. (2005). Para-phenylendiamine (PPD) 1% pet. is an important allergen in the standard series. CONTACT DERMATITIS, 53(3), 185-185.
Biehl, M. (2005). Lattice gas models and kinetic Monte Carlo simulations of epitaxial growth. In A. Voigt (Ed.), Multiscale Modeling in Epitaxial Growth. (pp. 3-18). (INTERNATIONAL SERIES OF NUMERICAL MATHEMATICS; Vol. 149). BASEL: Birkhauser. DOI: 10.1007/3-7643-7343-1_1
Volkmann, T., Much, F., Biehl, M., & Kotrla, M. (2005). Interplay of strain relaxation and chemically induced diffusion barriers: Nanostructure formation in 2D alloys. Surface Science, 586(1-3), 157-173. DOI: 10.1016/j.susc.2005.05.010
Ghosh, A., Biehl, M., & Hammer, B. (2005). Dynamical analysis of LVQ type learning rules. In M. Cottrell (Ed.), Proc. 5th Intl. Workshop on Self-Organising Maps (WSOM 2005) . (pp. 587-594). Univ. Paris I.
Biehl, M., Ghosh, A., & Hammer, B. (2005). The dynamics of Learning Vector Quantization. In M. Verleysen (Ed.), Proc. 13th Symposium on Artificial Neural Networks (ESANN 2005). d-side publishing.
Bunzmann, C., Biehl, M., & Urbanczik, R. (2005). Efficient training of multilayer perceptrons using principal component analysis. Physical Review E, 72(2), [026117]. DOI: 10.1103/PhysRevE.72.026117
Biehl, M., & Much, F. (2005). Off-Lattice Kinetic Monte Carlo Simulations of Stranski-Krastanov-like Growth. In B. Joyce, P. Kelires, A. Naumovets, & D. D. Vvedensky (Eds.), Quantum Dots: Fundamentals, Applications, and Frontiers: NATO Advanced Research Workshop 2003. (pp. 89-102). (NATO Science Series II: Mathematics, Physics and Chemistry; Vol. 190). Springer. DOI: 10.1007/1-4020-3315-X_6
Biehl, M., Much, F., & Vey, C. (2005). Off-lattice kinetic Monte Carlo simulations of strained heteroepitaxial growth. In A. Voigt (Ed.), Multiscale Modeling in Epitaxial Growth. (pp. 41-56). (INTERNATIONAL SERIES OF NUMERICAL MATHEMATICS; Vol. 149). BASEL: Birkhauser. DOI: 10.1007/3-7643-7343-1_4
2004
Ghosh, A., & Petkov, N. (2004). GAP Test: A Cognitive Evaluation Procedure for Shape Descriptors. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Grigorescu, C., Petkov, N., & Westenberg, M. A. (2004). Contour and boundary detection improved by surround suppression of texture edges. Image and vision computing, 22(8), 609-622. DOI: 10.1016/j.imavis.2003.12.004
Petkov, N., & Westenberg, M. A. (2004). Non-classical receptive-field inhibition and its relation to orientation-contrast pop-out and line and contour salience: A computational approach. Perception, 33, 68-68.
Petkov, N., & Wieling, M. B. (2004). Gabor filtering augmented with surround inhibition for improved contour detection by texture suppression. Perception, 33, 68-68. DOI: 10.1068/ecvp04a
Petkov, N. (2004). Framework for testing biological-utility hypotheses. Perception, 33, 69-69.
Petkov, N., & Westenberg, M. A. (2004). Band-limited noise suppresses contour perception not only of letters but of any objects. Perception, 33, 150-150.
Petkov, N., & Wieling, M. (2004). Gabor filtering augmented with surround inhibition for improved contour detection by texture suppression. Poster session presented at 27th Annual Meeting of the European Conference on Visual Perception. Budapest, Hungary, Budapest, Hungary.
Snippe, H. P., Poot, L., & Hateren, J. H. V. (2004). Asymmetric dynamics of adaptation after onset and offset of flicker. JOURNAL OF VISION, 4(1), 1-12. DOI: 10.1167/4.1.1
Blaj, G., & Hateren, J. H. V. (2004). Saccadic head and thorax movements in freely walking blowflies. Journal of comparative physiology a-Neuroethology sensory neural and behavioral physiology, 190(11), 861-868. DOI: 10.1007/s00359-004-0541-4
Snippe, H., & van Hateren, J. H. (2004). Dynamics of nonlinear feedback control. Perception, 33, 182-182.
Wilkinson, M. H. F., & Wink, A. M. LaTeX Scientific poster class sciposter.cls version 1.15
Urbach, E. R., Roerdink, J. B. T. M., & Wilkinson, M. H. F. (2004). Connected Rotation-invariant Size-Shape Granulometries. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Jalba, A. C., Wilkinson, M. H. F., & Roerdink, J. B. T. M. (2004). Automatic image segmentation using a deformable model based on charged particles. In A. Campilho, & M. Kamel (Eds.), IMAGE ANALYSIS AND RECOGNITION, PT 1, PROCEEDINGS. (pp. 1-8). (LECTURE NOTES IN COMPUTER SCIENCE; Vol. 3211). BERLIN: University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Jalba, A. C., Wilkinson, M. H. F., & Roerdink, J. B. T. M. (2004). Morphological hat-transform scale spaces and their use in pattern classification. Pattern recognition, 37(5), 901-915. DOI: 10.1016/j.patcog.2003.09.009
Jalba, A. C., Wilkinson, M. H. F., & Roerdink, J. B. T. M. (2004). Automatic segmentation of diatom images for classification. Microscopy Research and Technique, 65(1-2), 72-85. DOI: 10.1002/jemt.20111
Jalba, A. C., Wilkinson, M. H. F., & Roerdink, J. B. T. M. (2004). CPM: A Deformable Model for Shape Recovery and Segmentation Based on Charged Particles. Ieee transactions on pattern analysis and machine intelligence, 26(10), 1320-1335.
Urbach, E. R., Roerdink, J. B. T. M., & Wilkinson, M. H. F. (2004). Connected rotation-invariant size-shape granutometries. In J. Kittler, M. Petrou, & M. Nixon (Eds.), PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 1. (pp. 688-691). (INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION). LOS ALAMITOS: IEEE (The Institute of Electrical and Electronics Engineers).
Volkmann, T., Ahr, M., & Biehl, M. (2004). Kinetic model of II-VI(001) semiconductor surfaces: Growth rates in atomic layer epitaxy. Physical Review. B: Condensed Matter and Materials Physics, 69(16), [165303]. DOI: 10.1103/PhysRevB.69.165303
Biehl, M. (2004). Off-lattice Kinetic Monte Carlo Simulation of strained hetero-epitaxial growth (abstract).
Biehl, M. (2004). Lattice gas models and kinetic Monte Carlo simulations of epitaxial crystal growth (abstract).
Ghosh, A., Biehl, M., Freking, A., & Reents, G. A theoretical framework for analysing the dynamics of LVQ
2003
Petkov, N., & Westenberg, M. A. (2003). Computer Analysis of Images and Patterns. (2756 ed.) Berlin-New York, etc.: Springer.
Grigorescu, S. E., & Petkov, N. (2003). Texture Analysis Using Rényi’s Generalized Entropies. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Grigorescu, C., Petkov, N., & Westenberg, M. A. (2003). Contour Detection Operators Based on Surround Inhibition. In 2003 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL 3, PROCEEDINGS. (pp. 437-440). NEW YORK: University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Grigorescu, C., & Petkov, N. (2003). Distance sets for shape filters and shape recognition. Ieee transactions on image processing, 12(10), 1274-1286. DOI: 10.1109/TIP.2003.816010
Grigorescu, C., Petkov, N., & Westenberg, M. A. (2003). The role of non-CRF inhibition in contour detection. In Skala (Ed.), WSCG'2003, VOL 11, NO 2, CONFERENCE PROCEEDINGS. (pp. 197-204). PLZEN: UNIV WEST BOHEMIA.
Grigorescu, C., Petkov, N., & Westenberg, M. A. (2003). Contour detection based on nonclassical receptive field inhibition. Ieee transactions on image processing, 12(7), 729-739. DOI: 10.1109/TIP.2003.814250
Petkov, N., & Westenberg, M. A. (2003). Suppression of contour perception by band-limited noise and its relation to nonclassical receptive field inhibition. Biological Cybernetics, 88(3), 236-246. DOI: 10.1007/s00422-002-0378-2
Petkov, N. (2003). Grid computing en e-science. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Grigorescu, S. E., & Petkov, N. (2003). A dynamical system approach to texel identification in regular textures. In S. Loncaric, A. Neri, & H. Babic (Eds.), ISPA 2003: PROCEEDINGS OF THE 3RD INTERNATIONAL SYMPOSIUM ON IMAGE AND SIGNAL PROCESSING AND ANALYSIS, PTS 1 AND 2. (pp. 66-71). ZAGREB: University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Petkov, N., Westenberg, M. A., & Grigorescu, C. (2003). Non-classical receptive field inhibition and contour detection. Perception, 32, 1-2.
Grigorescu, S. E., & Petkov, N. (2003). Texture analysis using Renyi's generalized entropies. In 2003 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL 1, PROCEEDINGS. (pp. 241-244). (IEEE International Conference on Image Processing (ICIP)). NEW YORK: IEEE (The Institute of Electrical and Electronics Engineers).
Petkov, N. (2003). Algorithm for the cost of an optimal assignment of two sets of real numbers. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Petkov, N., & Westenberg, M. (Eds.) (2003). Computer Analysis of Images and Patterns: 10th International Conference. (Lecture Notes in Computer Science; Vol. 27562003). Springer Berlin Heidelberg. DOI: 10.1007/b13427
Lindemann, J. P., Kern, R., Michaelis, C., Meyer, P., Hateren, J. H. V., & Egelhaaf, M. (2003). FliMax, a novel stimulus device for panoramic and highspeed presentation of behaviourally generated optic flow. Vision Research, 43(7), 779-791. DOI: 10.1016/S0042-6989(03)00039-7
Snippe, H. P., & Hateren, J. H. V. (2003). Recovery from contrast adaptation matches ideal-observer predictions. Journal of the optical society of america a-Optics image science and vision, 20(7), 1321-1330.
Jalba, A. C., Roerdink, J. B. T. M., & Wilkinson, M. H. F. (2003). Morphological hat-transform scale spaces and their use in texture classification. In 2003 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL 1, PROCEEDINGS. (pp. 329-332). (IEEE International Conference on Image Processing (ICIP)). NEW YORK: University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Wilkinson, M. H. F., Wijbenga, T., Vries, G. D., & Westenberg, M. A. (2003). Blood Vessel Segmentation Using Moving-Window Robust Automatic Threshold Selection. In 2003 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL 2, PROCEEDINGS. (pp. 1093-1096). NEW YORK: University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Wilkinson, M. H. F. (2003). Gaussian-weighted moving-window robust automatic threshold selection. In N. Petkov, & M. A. Westenberg (Eds.), COMPUTER ANALYSIS OF IMAGES AND PATTERNS, PROCEEDINGS. (pp. 369-376). (LECTURE NOTES IN COMPUTER SCIENCE; Vol. 2756). BERLIN: University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Wilkinson, M. H. F., Jansen, G. J., & Waaij, D. V. D. (2003). Groningen Reduction of Image Data: A Microbiological Image Processing System with Applications in Immunofluorescence and Morphometry. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Jansen, G. J., & Wilkinson, M. H. F. (2003). Fluoromorphometrics: A New Approach in Characterising Faecal Flora. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Merrett, H. R., Kuijken, K., Merrifield, M. R., Romanowsky, A. J., Douglas, N. G., Napolitano, N. R., ... Carter, D. (2003). Tracing the star stream through M31 using planetary nebula kinematics. Monthly Notices of the Royal Astronomical Society, 346(4), L62-L66.
Wilkinson, M. H. F. (2003). Decoys in Predation and Parasitism. Default journal.
Biehl, M., Ahr, M., Kinzel, W., & Much, F. (2003). Kinetic Monte Carlo simulations of heteroepitaxial growth. Thin Solid Films, 428. DOI: 10.1016/S0040-6090(02)01267-1
Much, F., & Biehl, M. (2003). Simulation of wetting-layer and island formation in heteroepitaxial growth. Europhysics Letters, 63(1), 14-20. DOI: 10.1209/epl/i2003-00471-9
Kotrla, M., Much, F., Volkmann, T., & Biehl, M. (2003). Mechanisms of formation of self-assembled nanostructures in heteroepitaxy. In P. Sandera (Ed.), Proc. NANO'03 conference in Brno/Cz. (pp. 98-103). FSI VUT Brno.
Biehl, M. (2003). The statistical physics of learning: phase transitions and dynamical symmetry breaking. In R. Kühn, R. Menzel, W. Menzel, U. Ratsch, M. M. Richter, & I-O. Stamatescu (Eds.), Adaptivity and learning, an interdisciplinary debate. (pp. 89-101). Springer. DOI: 10.1007/978-3-662-05594-6_9
2002
Grigorescu, C., Petkov, N., & Westenberg, M. A. (2002). Improved Contour Detection by Non-Classical Receptive Field Inhibition. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Grigorescu, S. E., Petkov, N., & Kruizinga, P. (2002). Comparison of texture features based on Gabor filters. Ieee transactions on image processing, 11(10), 1160-1167. DOI: 10.1109/TIP.2002.804262
Grigorescu, C., Petkov, N., & Westenberg, M. A. (2002). Improved contour detection by non-classical receptive field inhibition. In H. H. Bulthoff, S. W. Lee, T. A. Poggio, & C. Wallraven (Eds.), BIOLOGICALLY MOTIVATED COMPUTER VISION, PROCEEDINGS. (pp. 50-59). (LECTURE NOTES IN COMPUTER SCIENCE; Vol. 2525). BERLIN: Springer.
Grigorescu, C., Petkov, N., & Westenberg, M. A. (2002). Performance enhancement of contour detectors by surround inhibition. In ICCVG 2002. (pp. 25-29)
Hateren, J. H. V., Rüttiger, L., & Lee, B. B. (2002). Processing of natural temporal stimuli by macaque retinal ganglion cells. The Journal of Neuroscience, 22(22), 9945-9960.
Snippe, H. P., & van Hateren, J. H. (2002). Contrast adaptation: dynamics of feedback control. Perception, 31, 63-63.
Urbach, E. R., & Wilkinson, M. H. F. (2002). Shape-Only Granulometries and Gray-Scale Shape Filters. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Wilkinson, M. H. F. (2002). Generalized Pattern Spectra Sensitive to Spatial Information. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Meijster, A., Westenberg, M. A., & Wilkinson, M. H. F. (2002). Interactive Shape Preserving Filtering and Visualization of Volumetric Data. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Meijster, A., & Wilkinson, M. H. F. (2002). A comparison of algorithms for connected set openings and closings. Ieee transactions on pattern analysis and machine intelligence, 24(4), 484-494.
Kamerman, D. J., & Wilkinson, M. H. F. (2002). In Silico Modelling of the Human Intestinal Microflora. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Wilkinson, M. H. F. (2002). Model intestinal microflora in computer simulation: A simulation and modeling package for host-microflora interactions. Ieee transactions on biomedical engineering, 49(10), 1077-1085. DOI: 10.1109/TBME.2002.803548
Wilkinson, M. H. F. (2002). Generalized pattern spectra sensitive to spatial information. In R. Kasturi, D. Laurendeau, & C. Suen (Eds.), 16TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL I, PROCEEDINGS. (pp. 21-24). (INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION). LOS ALAMITOS: IEEE (The Institute of Electrical and Electronics Engineers).
Kamerman, D. J., & Wilkinson, M. H. F. (2002). In silico modelling of the human intestinal microflora. In P. Sloot, C. J. K. Tan, J. J. Dongarra, & A. G. Hoekstra (Eds.), COMPUTATIONAL SCIENCE-ICCS 2002, PT I, PROCEEDINGS. (pp. 117-126). (LECTURE NOTES IN COMPUTER SCIENCE; Vol. 2329). BERLIN: Springer.
Urbach, E. R., & Wilkinson, M. H. F. (2002). Shape-only granulometries and gray-scale shape filters. In H. Talbot, & R. Beare (Eds.), MATHEMATICAL MORPHOLOGY, PROCEEDINGS. (pp. 305-314). EAST MELBOURNE: C S I R O.
Wilkinson, M. H. F. (2002). Zero-Tolerance Math: A Defense of “No Math”. Default journal.
Wilkinson, M. H. F., Jalba, A. C., Urbach, E. R., & Roerdink, J. B. T. M. (2002). Identification by mathematical morphology. In J. M. H. Du~Buf, & M. M. Bayer (Eds.), Automatic Diatom Identification. (Vol. 51, pp. 221-244). [11] (Series in Machine Perception and Artificial Intelligence). World Scientific Publishing Co., Singapore.
Ahr, M., Biehl, M., & Volkmann, T. (2002). Modeling (001) surfaces of II–VI semiconductors. Computer Physics Communications, 147, 107-110. DOI: 10.1016/S0010-4655(02)00226-6
Ahr, M., & Biehl, M. (2002). Flat (001) surfaces of II–VI semiconductors: a lattice gas model. Surface Science, 505, 124-136. DOI: doi:10.1016/S0039-6028(02)01145-7
Biehl, M., & Kinzel, W. (2002). Terrace Sizes and Particle Currents in Epitaxial Growth. JSME International Journal. Series B: Fluids and Thermal Engineering, 45(1), 112-116. DOI: 10.1299/jsmeb.45.112
Much, F., Ahr, M., Biehl, M., & Kinzel, W. (2002). A Kinetic Monte Carlo method for the simulation of heteroepitaxial growth. Computer Physics Communications, 147, 226-229. DOI: 10.1016/S0010-4655(02)00251-5
Bunzmann, C., Biehl, M., & Urbanczik, R. (2002). Supervised Learning in Committee Machines by PCA. In M. Verleysen (Ed.), Proc. 10th European Symposium on Artificial Neural Networks ESANN 2012. (pp. 125-130). d-side publishing.
Biehl, M., & Caticha, N. (2002). Statistical mechanics of on-line learning and generalization. In M. A. Arbib (Ed.), Handbook of Brain Theory and Neural Networks (second editon). MIT Press.
2001
Lippert, T., Petkov, N., Palazzari, P., & Schilling, K. (2001). Hyper-systolic matrix multiplication. Parallel Computing, 27(6), 737-759.
Lippert, T., & Petkov, N. (2001). Hyper-systolic algorithms with applications in linear algebra and molecular dynamics. In Highly parallel computations. (pp. 271-337). WIT Press.
Hateren, J. H. V., & Snippe, H. P. (2001). Information theoretical evaluation of parametric models of gain control in blowfly photoreceptor cells. Vision Research, 41(14), 1851-1865.
Meijster, A., & Wilkinson, M. H. F. (2001). Fast computation of morphological area pattern spectra. In 2001 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL III, PROCEEDINGS. (pp. 668-671). NEW YORK: University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Wilkinson, M. H. F., & Westenberg, M. A. (2001). Shape Preserving Filament Enhancement Filtering. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Wilkinson, M. H. F. (2001). Predation in the Presence of Decoys: An Inhibitory Factor on Pathogen Control by Bacteriophages or Bdellovibrios in Dense and Diverse Ecosystems. Journal of Theoretical Biology, 208(1), 27-36.
Bayer, M. M., Pullan, M. R., Mann, D. G., Juggins, S., Ciobanu, A., Santos, L., ... Ludes, B. (2001). ADIAC: Using computer vision technology for automatic diatom identification. In A. E. . Amilli (Ed.), Proceedings of the 16th International Diatom Symposium, Athens, Greece, 25 aug -1 sept 2000. (pp. 537-562)
Bunzmann, C., Biehl, M., & Urbanczik, R. (2001). Efficiently Learning Multilayer Perceptrons. Physical Review Letters, 86(10), 2166-2169. DOI: 10.1103/PhysRevLett.86.2166
Biehl, M., Ahr, M., Kinne, M., Kinzel, W., & Schinzer, S. (2001). Particle currents and the distribution of terrace sizes in unstable epitaxial growth. Physical Review. B: Condensed Matter and Materials Physics, 64(11), [113405]. DOI: 10.1103/PhysRevB.64.113405
Biehl, M., Bunzmann, C., & Urbanczik, R. (2001). Training multilayer perceptrons by principal component analysis. Physica A: Statistical Mechanics and its Applications, 302(1-4), 56-63. DOI: 10.1016/S0378-4371(01)00440-X
Much, F., Ahr, M., Biehl, M., & Kinzel, W. (2001). Kinetic Monte Carlo simulations of dislocations in heteroepitaxial growth. Europhysics Letters, 56, 791-796. DOI: 10.1209/epl/i2001-00589-8
Biehl, M., Ahr, M., Kinzel, W., Sokolowski, M., & Volkmann, T. (2001). A lattice gas model of II-VI(001) semiconductor surfaces. Europhysics Letters, 53, 169-175. DOI: 10.1209/epl/i2001-00132-1
Floren, A., Freking, A., Biehl, M., & Linsenmair, K. E. (2001). Anthropogenic disturbance changes the structure of arboreal tropical ant communities. Ecography, 24(5), 547-554. DOI: 10.1111/j.1600-0587.2001.tb00489.x
Ahr, M., & Biehl, M. (2001). Modelling sublimation and atomic layer epitaxy in the presence of competing surface reconstructions. Surface Science Letters, 488(1-2), L553-L560. DOI: 10.1016/S0039-6028(01)01157-8
2000
Kruizinga, P., & Petkov, N. (2000). A nonlinear texture operator specialised in the analysis of dot-patterns. In A. Sanfeliu, J. J. Villanueva, M. Vanrell, R. Alquezar, J. O. Eklundh, & Y. Aloimonos (Eds.), 15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 1, PROCEEDINGS. (pp. 197-201). (INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION). LOS ALAMITOS: IEEE (The Institute of Electrical and Electronics Engineers).
Grigorescu, S. E., Petkov, N., & Kruizinga, P. (2000). A comparative study of filter based texture operators using Mahalanobis distance. In A. Sanfeliu, J. J. Villanueva, M. Vanrell, R. Alquezar, T. Huang, & J. Serra (Eds.), 15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3, PROCEEDINGS. (pp. 885-888). (INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION). LOS ALAMITOS: IEEE (The Institute of Electrical and Electronics Engineers).
Grigorescu, C., & Petkov, N. (2000). Graph-based features for texture discrimination. In A. Sanfeliu, J. J. Villanueva, M. Vanrell, R. Alquezar, T. Huang, & J. Serra (Eds.), 15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3, PROCEEDINGS. (pp. 1076-1079). (INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION). LOS ALAMITOS: IEEE (The Institute of Electrical and Electronics Engineers).
Kruizinga, P., & Petkov, N. (2000). Computational model of dot-pattern selective cells. Biological Cybernetics, 83(4), 313-325.
Petkov, N., & Kruizinga, P. (2000). Perception of form and texture through complementary bar and grating cell channels. Perception, 29, 60-60.
Pece, A., & Petkov, N. (2000). Fast atomic decomposition by the inhibition method. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Snippe, H. P., Poot, L., & Hateren, J. H. V. (2000). A temporal model for early vision that explains detection thresholds for light pulses on flickering backgrounds. Visual neuroscience, 17(3), 449-462.
van Hateren, J. H., & Snippe, H. P. (2000). A parametric model for the processing of natural time series of intensities by blowfly photoreceptor cells. Investigative ophthalmology & visual science, 41(4), S492-S492.
Lee, B. B., van Hateren, J. H., & Ruttiger, L. (2000). Information content of macaque ganglion cell spike trains to natural chromatic time series. Investigative ophthalmology & visual science, 41(4), S761-S761.
Snippe, H. P., Poot, L., & van Hateren, J. H. (2000). A model for the dynamics of light adaptation and contrast gain contrast. Investigative ophthalmology & visual science, 41(4), S803-S803.
Snippe, H. P., & van Hateren, J. H. (2000). A model for the asymmetric adaptation after increments and decrements of flicker contrast. Perception, 29, 28-29.
Wilkinson, M. H. F., & Roerdink, J. B. T. M. (2000). Fast morphological attribute operations using Tarjan's union-find algorithm. In J. Goutsias, L. Vincent, & D. S. Bloomberg (Eds.), MATHEMATICAL MORPHOLOGY AND ITS APPLICATIONS TO IMAGE AND SIGNAL PROCESSING. (pp. 311-320). (COMPUTATIONAL IMAGING AND VISION; Vol. 18). NORWELL: Kluwer Academic Publishers.
Wilkinson, M. H. F., Roerdink, J. B. T. M., Droop, S., & Bayer, M. (2000). Diatom contour analysis using morphological curvature scale spaces. In A. Sanfeliu, J. J. Villanueva, M. Vanrell, R. Alquezar, T. Huang, & J. Serra (Eds.), 15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3, PROCEEDINGS. (pp. 652-655). (INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION). LOS ALAMITOS: IEEE (The Institute of Electrical and Electronics Engineers).
Wilkinson, M. H. F. (2000). Cogno-Intellectualism, Rhetorical Logic, and the Craske-Trump Theorem. Default journal.
Boureau, H., Hartmann, L., Karjalainen, T., Rowland, I., & Wilkinson, M. H. F. (2000). Models to Study Colonisation and Colonisation Resistance. Default journal.
Ahr, M., Biehl, M., Kinne, M., & Kinzel, W. (2000). The influence of the crystal lattice on coarsening in unstable epitaxial growth. Surface Science, 465, 339-346. DOI: 10.1016/S0039-6028(00)00725-1
Ahr, M., & Biehl, M. (2000). Singularity spectra of rough growing surfaces from wavelet analysis. Physical Review E, 62, 1773-1777. DOI: 10.1103/PhysRevE.62.1773
Biehl, M., Kühn, R., & Stamatescu, I-O. (2000). Learning structured data from unspecific reinforcement. Journal of Physics A, Mathematical and General, 33, 6843-6857. DOI: 10.1088/0305-4470/33/39/302
Biehl, M., Ahr, M., & Schlösser, E. (2000). Statistical physics of learning: Phase transitions in multilayered neural networks. Advances in Solid State Physics, 40, 819-826. DOI: 10.1007/BFb0108398
1999
Kruizinga, P., & Petkov, N. (1999). Nonlinear operator for oriented texture. Ieee transactions on image processing, 8(10), 1395-1407.
Kruizinga, P., Petkov, N., & Grigorescu, S. E. (1999). Comparison of texture features based on Gabor filters. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Kruizinga, P., & Petkov, N. (1999). Nonlinear operator for blob texture segmentation. In A. E. Cetin, L. Akarun, A. Ertuzun, M. N. Gurcan, & Y. Yardimci (Eds.), PROCEEDINGS OF THE IEEE-EURASIP WORKSHOP ON NONLINEAR SIGNAL AND IMAGE PROCESSING (NSIP'99). (pp. 881-885). ISTANBUL: University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Poot, L., Snippe, H. P., & van Hateren, J. H. (1999). Dynamics of adaptation to the onset and offset of flicker. Investigative ophthalmology & visual science, 40(4), S46-S46.
Schilstra, C., & Van Hateren, J. H. (1999). Blowfly flight and optic flow I. Thorax kinematics and flight dynamics. Journal of Experimental Biology, 202(11), 1481-1490.
Van Hateren, J. H., & Schilstra, C. (1999). Blowfly flight and optic flow II. Head movements during flight. Journal of Experimental Biology, 202(11), 1491-1500.
Snippe, H., Poot, L., & van Hateren, J. H. (1999). Detection thresholds for brief pulses presented on dynamic backgrounds can be explained by a model that relates to retinal physiology. Perception, 28, 144-144.
Buf, H. D., Bayer, M., Droop, S., Head, R., Juggins, S., Fischer, S., ... Ciobanu, A. (1999). Diatom Identification: a Double Challenge Called ADIAC. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Roelofsen, E., Leeuwen, M. V., Meijer-Severs, G. J., Wilkinson, M. H. F., & Degener, J. E. (1999). Evaluation of the effects of storage in two different swab fabrics and under three different transport conditions on recovery of aerobic and anaerobic bacteria. JOURNAL OF CLINICAL MICROBIOLOGY, 37(9), 3041-3043.
Wilkinson, M. H. F., & Roerdink, J. B. T. M. (1999). Fast Morphological Attribute Operations Using Tarjan's Union-Find Algorithm. Default journal.
Wilkinson, M. H. F. (1999). Towards a Quantum Mechanical Interpretation of Homeopathy. Default journal.
Ahr, M., Biehl, M., & Urbanczik, R. (1999). Noisy regression and classification with continuous multilayer networks. Journal of Physics A, Mathematical and General, 32(50), L531-L536. DOI: 10.1088/0305-4470/32/50/101
Ahr, M., Biehl, M., & Schlösser, E. (1999). Weight-decay induced phase transitions in multilayer neural networks. Journal of Physics A, Mathematical and General, 32, 5003-5008. DOI: 10.1088/0305-4470/32/27/301
Schinzer, S., Sokolowski, M., Biehl, M., & Kinzel, W. (1999). Evaporation and step edge diffusion in MBE. Journal of Crystal Growth, 201-202, 85-87. DOI: 10.1016/S0022-0248(98)01293-7
Ahr, M., Biehl, M., & Urbanczik, R. (1999). Statistical physics and practical training of soft-committee machines. The European Physical Journal B - Condensed Matter and Complex Systems, 10, 583-588. DOI: 10.1007/s100510050889
Biehl, M., Kinne, M., Kinzel, W., & Schinzer, S. (1999). A simple model of epitaxial growth: the influence of step edge diffusion. Computer Physics Communications, 121-122, 347-352. DOI: 10.1016/S0010-4655(99)00351-3
Schinzer, S., Kinne, M., Biehl, M., & Kinzel, W. (1999). The role of step edge diffusion in epitaxial crystal growth. Surface Science, 439(1-3), 191-198. DOI: 10.1016/S0039-6028(99)00761-X
Freking, A., Biehl, M., Braun, C., Kinzel, W., & Meesmann, M. (1999). Receiver operating characteristics of perceptrons: Influence of sample size and prevalence. Physical Review E, 60, 5926-5931. DOI: 10.1103/PhysRevE.60.5926
Schinzer, S., Sokolowski, M., Biehl, M., & Kinzel, W. (1999). Unconventional MBE strategies from computer simulations for optimized growth conditions. Physical Review. B: Condensed Matter and Materials Physics, 60(4), 2893-2899. DOI: 10.1103/PhysRevB.60.2893
Schlösser, E., Saad, D., & Biehl, M. (1999). Optimization of on-line principal component analysis. Journal of Physics A, Mathematical and General, 32(22), 4061-4067. DOI: 10.1088/0305-4470/32/22/306
Biehl, M., Ahr, M., & Schlösser, E. (1999). Phase transitions in soft-committee machines. Computer Physics Communications, 121-122, 614. DOI: 10.1016/S0010-4655(06)70021-2
1998
Petkov, N. (1998). Grating cell operator features for oriented texture. In Proceedings of the International Conference on Pattern Recognition, IEEE, Brisbane, Australia. (pp. 1010 - 1014)
Kruizinga, P., & Petkov, N. (1998). Grating cell operator features for oriented texture segmentation. In A. K. Jain, S. Venkatesh, & B. C. Lovell (Eds.), FOURTEENTH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1 AND 2. (pp. 1010-1014). (INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION). LOS ALAMITOS: University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Liebrand, W. B. G., Berendsen, H. J. C., Petkov, N., Wansbeek, T. J., Nerbonne, J., Zwierstra, R. P., ... Duim, L. A. V. D. (1998). ICT-strategie RuG: de bijdrage van informatie- en communicatietechnologie aan onderwijs en onderzoek. Groningen: s.n.
Hateren, J. H. V., & Ruderman, D. L. (1998). Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex. Proceedings of the Royal Society of London. Series B, Biological Sciences, 265(1412), 2315-2320.
van Hateren, J. H., & van der Schaaf, A. (1998). Independent component filters of natural images compared with simple cells in primary visual cortex. Proceedings of the Royal Society of London. Series B, Biological Sciences, 265(1394), 359-366. DOI: 10.1098/rspb.1998.0303
Schilstra, C., & Hateren, J. H. V. (1998). Stabilizing gaze in flying blowflies. Nature, 395(6703), 654-654.
Schilstra, C., & Hateren, J. H. V. (1998). Using miniature sensor coils for simultaneous measurement of orientation and position of small, fast-moving animals. Journal of Neuroscience Methods, 83(2), 125-131.
Poot, L., van Hateren, J. H., & Snippe, H. P. (1998). Luminance and contrast adaptation to time series of natural intensities. Perception, 27, 12-12.
Snippe, H. P., Poot, L., & van Hateren, J. H. (1998). Pulse detection on flickering backgrounds: effects of test pulse polarity. Perception, 27, 52-52.
Jansonius, N. M., Jansen, M., Jongebloed, H., & van Hateren, J. H. (1998). Localized processing by amacrine cells in the fly lamina: a cable model. In Information processing by spiking neurons in the first optic chasm of the blowfly : thesis. University of Groningen.
Wilkinson, M. H. F., & Schut, F. (1998). Quantitative measurements of intestinal ecology by digital image analysed microscopy. Biosci. Microflora, 7 - 14.
Wilkinson, M. H. F. (1998). Optimizing edge detectors for robust automatic threshold selection: coping with edge curvature and noise. Graph. Models Image Proc., 385 - 401.
Wilkinson, M. H. F. (1998). Digital Image Analysis of Microbes: Imaging Morphometry, Fluorometry and Motility techniques and Applications. Chichester, New York, etc.: Wiley.
Wilkinson, M. H. F. (1998). Quantitating single colour fluorescence; immunofluorescence (IF) and fluorescence in situ hybridization (FISH). In F. Schut, & M. H. F. Wilkinson (Eds.), Digital Image Analysis of Microbes: Imaging Morphometry, Fluorometry and Motility techniques and Applications. (pp. 251 - 280). Chichester, New York, etc.: Wiley.
Wilkinson, M. H. F. (1998). Some practical considerations for the development of image analysis applications in microbiology: ensuring software and data durability. In F. Schut, & M. H. F. Wilkinson (Eds.), Digital Image Analysis of Microbes: Imaging Morphometry, Fluorometry and Motility techniques and Applications. (pp. 115 - 133). Chichester, New York, etc.: Wiley.
Wilkinson, M. H. F. (1998). Automated and manual segmentation techniques in image analysis of microbes. In F. Schut, & M. H. F. Wilkinson (Eds.), Digital Image Analysis of Microbes: Imaging Morphometry, Fluorometry and Motility techniques and Applications. (pp. 135 - 171). Chichester, New York, etc.: Wiley.
Meijer, B. C., & Wilkinson, M. H. F. (1998). Optimized population statistics derived from morphometry. In F. Schut, & M. H. F. Wilkinson (Eds.), Digital Image Analysis of Microbes: Imaging Morphometry, Fluorometry and Motility techniques and Applications. (pp. 225 - 250). Chichester, New York, etc.: Wiley.
Wilkinson, M. H. F. (1998). Optical systems for image analysed microscopy. In F. Schut, & M. H. F. Wilkinson (Eds.), Digital Image Analysis of Microbes: Imaging Morphometry, Fluorometry and Motility techniques and Applications. (pp. 65 - 91). Chichester, New York, etc.: Wiley.
Wilkinson, M. H. F. (1998). Optimizing edge detectors for robust automatic threshold selection: Coping with edge curvature and noise. Graphical models and image processing, 60(5), 385-401.
Wilkinson, M. H. F., & Schut, F. (1998). Quantitative Measurements of Intestinal Ecology by Digital Image Analysed Microscopy. Default journal.
Rosen-Zvi, M., Biehl, M., & Kanter, I. (1998). Learnability of periodic activation functions: General results. Physical Review E, 58(3), 3606-3609. DOI: 10.1103/PhysRevE.58.3606
Biehl, M., Freking, A., Reents, G., & Schlösser, E. (1998). Specialization processes in on-line unsupervised learning. Philosophical Magazine. Part B: Electronic, Optical and Magnetic Properties, 77, 1487-1494. DOI: 10.1080/13642819808205040
Biehl, M., Freking, A., Hölzer, M., Reents, G., & Schlösser, E. (1998). On-line Learning of Prototypes and Principal Components. In D. Saad (Ed.), On-line Learning in Neural Networks. Cambridge University Press.
Biehl, M., & Schlösser, E. (1998). The dynamics of on-line principal component analysis. Journal of Physics A, Mathematical and General, 31(5), L97-L103. DOI: 10.1088/0305-4470/31/5/002
Biehl, M., Schlösser, E., & Ahr, M. (1998). Phase transitions in soft-committee machines. Europhysics Letters, 44(2), 261-267. DOI: 10.1209/epl/i1998-00466-6
Biehl, M., Kinzel, W., & Schinzer, S. (1998). A simple model of epitaxial growth. Europhysics Letters, 41(4), 443-448. DOI: 10.1209/epl/i1998-00171-0
1997
Petkov, N., & Kruizinga, P. (1997). Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: Bar and grating cells. Biological Cybernetics, 76(2), 83-96.
Lippert, T., Petkov, N., & Schilling, K. (1997). BLAS-3 for the Quadrics parallel computer. In B. Hertzberger, & P. Sloot (Eds.), HIGH-PERFORMANCE COMPUTING AND NETWORKING. (pp. 332-341). (Lecture Notes in Computer Science; Vol. 1225). BERLIN: Springer.
Petkov, N., & vanDeemter, J. (1997). Multiscale aspects of the visual system and their use for scale invariant object recognition. In F. Karsch, B. Monien, & H. Satz (Eds.), MULTISCALE PHENOMENA AND THEIR SIMULATION. (pp. 37-48). SINGAPORE: World Scientific Publishing.
Hateren, J. H. V. (1997). Processing of natural time series of intensities by the visual system of the blowfly. Vision Research, 37(23), 3407-3416.
Poot, L., Snippe, H. P., & Hateren, J. H. V. (1997). Dynamics of adaptation at high luminances: Adaptation is faster after luminance decrements than after luminance increments. Journal of the optical society of america a-Optics image science and vision, 14(9), 2499-2508.
van Hateren, J. H. (1997). Processing of natural time series of intensities in the early visual system of the blowfly. Perception, 26, 6-7.
Poot, L., Snippe, H. P., & van Hateren, J. H. (1997). Adaptation is faster after luminance decrements than after luminance increments, independently of test pulse polarity. Perception, 26, 24-24.
Snippe, H. P., Poot, L., & van Hateren, J. H. (1997). Detection thresholds for light pulses superimposed on backgrounds with temporally modulated luminance. Perception, 26, 25-25.
Meijer, C., Vries, E. G. E. D., Dam, W. A., Wilkinson, M. H. F., Hollema, H., Hoekstra, H. J., & Mulder, N. H. (1997). Immunocytochemical analysis of cisplatin-induced platinum-DNA adducts with double-fluorescence video microscopy. British Jounal of Cancer, 76(3), 290-298.
Wilkinson, M. H. F. (1997). Nonlinear dynamics, chaos-theory, and the "sciences of complexity": Their relevance to the study of the interaction between host and microflora. In P. J. Heidt, Rusch, & D. VanderWaaij (Eds.), New Antimicrobial Strategies. (pp. 111-130). (OLD HERBORN UNIVERSITY SEMINAR MONOGRAPH; Vol. 10). HERBORN-DILL: INST MICROECOLOGY & BIOCHEM.
Wilkinson, M. H. F. (1997). MIMICS Cellular Automaton Program Design and Performance Testing. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Wilkinson, M. H. F. (1997). Ordinary Differential Equations for Modelling Bacterial Interactions in the Gut. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Biehl, M., Freking, A., & Reents, G. (1997). Dynamics of on-line competitive learning. Europhysics Letters, 38(1), 73-78. DOI: 10.1209/epl/i1997-00536-9
Copelli, M., Eichhorn, R., Kinouchi, O., Biehl, M., Simonetti, R., Riegler, P., & Caticha, N. (1997). Noise robustness in multilayer neural networks. Europhysics Letters, 37, 427-432. DOI: 10.1209/epl/i1997-00167-2
Biehl, M., & Riegler, P. (1997). Comment on ‘‘On-Line Gibbs Learning’’. Physical Review Letters, 78(22), 4305. DOI: 10.1103/PhysRevLett.78.4305
1996
Petkov, N. (1996). Biologically motivated image classification system. In P. A. Lapante, & A. D. Stoyenko (Eds.), Real-Time Imaging: Theory, Techniques, and Applications. (pp. 195-224). New York: IEEE (The Institute of Electrical and Electronics Engineers).
van der Schaaf, A., & van Hateren, J. H. (1996). Modelling the power spectra of natural images: Statistics and information. Vision Research, 36(17), 2759-2770.
Hateren, J. H. V., & Schaaf, A. V. D. (1996). Temporal properties of natural scenes. In B. E. Rogowitz, & J. P. Allebach (Eds.), HUMAN VISION AND ELECTRONIC IMAGING. (pp. 139-143). (PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE); Vol. 2657). BELLINGHAM: SPIE - INT SOC OPTICAL ENGINEERING.
Veenendaal, D., Boer, J. D., Waaij, D. V. D., Wilkinson, M. H. F., & Meijer, B. C. (1996). Micromorphometrical analysis of rodent related (SPF) and unrelated (human) gut microbial flora in germfree mice by digital image processing. EPIDEMIOLOGY AND INFECTION, 116(1), 35-40.
Wilkinson, M. H. F. (1996). Rapid automatic segmentation of fluorescent and phase-contrast images of bacteria. In J. Slavik (Ed.), FLUORESCENCE MICROSCOPY AND FLUORESCENT PROBES. (pp. 261-266). NEW YORK: Plenum Press.
Riegler, P., Biehl, M., Solla, S. A., & Marangi, C. (1996). On-line learning from clustered input examples. In M. Marinaro, & R. Tagliaferri (Eds.), Proc. 7th Italian Workshop on Neural Networks WIRN 1995. (pp. 87-92). World Scientific Publishing.
Marangi, C., Solla, S. A., Biehl, M., & Riegler, P. (1996). Off-line learning from clustered input examples. In M. Marinaro, & R. Tagliaferri (Eds.), Proc. 7th Italian Workshop on Neural Networks WIRN 1995. (pp. 105-110). World Scientific Publishing.
Biehl, M., Riegler, P., & Wöhler, C. (1996). Transient dynamics of on-line learning in two-layered neural networks. Journal of Physics A, Mathematical and General, 29(16), 4769-4780. DOI: 10.1088/0305-4470/29/16/005
1995
Kruizinga, P., & Petkov, N. (1995). Person identification based on multiscale matching of cortical images. In B. Hertzberger, & G. Serazzi (Eds.), HIGH-PERFORMANCE COMPUTING AND NETWORKING. (pp. 420-427). (LECTURE NOTES IN COMPUTER SCIENCE; Vol. 919). BERLIN 33: Springer.
Petkov, N. (1995). Biologically motivated self-organising image classification system. In B. Hertzberger, & G. Serazzi (Eds.), HIGH-PERFORMANCE COMPUTING AND NETWORKING. (pp. 938-938). (LECTURE NOTES IN COMPUTER SCIENCE; Vol. 919). BERLIN 33: Springer.
Kruizinga, P., & Petkov, N. (1995). A computational model of periodic-pattern-selective cells. In J. Mira, & F. Sandoval (Eds.), FROM NATURAL TO ARTIFICIAL NEURAL COMPUTATION. (pp. 90-99). (LECTURE NOTES IN COMPUTER SCIENCE; Vol. 930). BERLIN 33: Springer.
Petkov, N. (1995). Biologically motivated computationally intensive approaches to image pattern recognition. Future generation computer systems, 11(4-5), 451-465.
Aerts, P. J. C., Hoffmann, W., Hollenberg, J., van Lenthe, J., Llurba, R., Petkov, N., ... van der Steen, A. J. (1995). Aspects of Computational Science. NCF.
Petkov, N. (1995). Image Classification System Based on Cortical Representations and Unsupervised Neural Network Learning. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Petkov, N. (1995). Use of cortical filters and neural networks in a self-organising image classification system. In Image Analysis and Processing (ICIAP '95). (pp. 165-170). (Lecture Notes in Computer Science; Vol. 974). Springer Berlin Heidelberg. DOI: 10.1007/3-540-60298-4_253
Bruins, S., Jong, M. C. J. M. D., Heeres, K., Wilkinson, M. H. F., & Jonkman, M. F. (1995). Fluorescence Overlay Antigen Mapping of the Epidermal Basement Membrane Zone: III. Topographic Staining and Effective Resolution. Default journal.
Wilkinson, M. H. F., & Meijer, B. C. (1995). DATAPLOT: A graphical display package for bacterial morphometry and fluorimetry data. Default journal.
JANSEN, G., DEDDENS, B., WILKINSON, M., & VANDERWAAIJ, D. (1995). THE INFLUENCE OF ENTEROCOCCUS-FAECALIS ON THE MORPHOLOGY AND THE ANTIBODY-BINDING CAPACITY OF THE INTESTINAL BACTERIA OF 10 HEALTHY-HUMAN VOLUNTEERS. Infection, 23(1), 46-50.
BRUINS, S., DEJONG, M. C. J. M., HEERES, K., WILKINSON, M. H. F., JONKMAN, M. F., & VANDERMEER, J. B. (1995). FLUORESCENCE OVERLAY ANTIGEN MAPPING OF THE EPIDERMAL BASEMENT-MEMBRANE ZONE .2. COLOR FIDELITY. Journal of Histochemistry & Cytochemistry, 43(7), 715-722.
BRUINS, S., DEJONG, M. C. J. M., HEERES, K., WILKINSON, M. H. F., JONKMAN, M. F., & VANDERMEER, J. B. (1995). FLUORESCENCE OVERLAY ANTIGEN MAPPING OF THE EPIDERMAL BASEMENT-MEMBRANE ZONE .3. TOPOGRAPHIC STAINING AND EFFECTIVE RESOLUTION. Journal of Histochemistry & Cytochemistry, 43(7), 649-656.
WILKINSON, M. H. F., & MEIJER, B. C. (1995). DATAPLOT - A GRAPHICAL DISPLAY PACKAGE FOR BACTERIAL MORPHOMETRY AND FLUOROMETRY DATA. Computer Methods and Programs in Biomedicine, 47(1), 35-49.
LANGENDIJK, P. S., SCHUT, F., JANSEN, G. J., RAANGS, G. C., KAMPHUIS, G. R., WILKINSON, M. H. F., & WELLING, G. W. (1995). QUANTITATIVE FLUORESCENCE IN-SITU HYBRIDIZATION OF BIFIDOBACTERIUM SPP WITH GENUS-SPECIFIC 16S RIBOSOMAL-RNA-TARGETED PROBES AND ITS APPLICATION IN FECAL SAMPLES. Applied and Environmental Microbiology, 61(8), 3069-3075.
Jansen, G., Deddens, B., Wilkinson, M., & Waaij, D. V. D. (1995). The Influence of Enterococcus faecalis on the Morphology and the Antibody-Binding Capacity of the Intestinal Bacteria of Ten Healthy Human Volunteers. Default journal.
Langendijk, P. S., Schut, F., Jansen, G. J., Raangs, G. C., Kamphuis, G. R., Wilkinson, M. H. F., & Welling, G. W. (1995). Quantitative Fluorescence In Situ Hybridization of Bifidobacterium spp. with Genus-Specific 16S rRNA-Targeted Probes and Its Application in Fecal Samples. Default journal.
Wilkinson, M. H. F. (1995). Fluoro- Morphometry, adding Fluorimetry to an image processing system for bacterial morphometry s.n.
Biehl, M., Riegler, P., & Stechert, M. (1995). Learning from noisy data: An exactly solvable model. Physical Review E, 52(5), R4624-R4627. DOI: 10.1103/PhysRevE.52.R4624
Riegler, P., & Biehl, M. (1995). On-line backpropagation in two-layered neural networks. Journal of Physics A, Mathematical and General, 28(20), L507-L513. DOI: 10.1088/0305-4470/28/20/002
Biehl, M., & Schwarze, H. (1995). Learning by on-line gradient descent. Journal of Physics A, Mathematical and General, 52(5). DOI: 10.1088/0305-4470/28/3/018
Marangi, C., Biehl, M., & Solla, S. A. (1995). Supervised Learning from Clustered Input Examples. Europhysics Letters, 30(2), 117-122. DOI: 10.1209/0295-5075/30/2/010
Biehl, M., & Opper, M. (1995). Perceptron learning: the largest version space. In C. Kwon, J-H. Oh, & S. Cho (Eds.), Proc. Workshop on Neural Networks: The Statistical Mechanics Perspective. World Scientific Publishing.
1994
Lourens, T., Petkov, N., & Kruizinga, P. (1994). Large scale natural vision simulations. Future generation computer systems, 10(2-3), 351-358.
KRUIZINGA, P., & PETKOV, N. (1994). Optical flow applied to person identification. In L. Dekker, W. Smit, & J. C. Zuidervaart (Eds.), MASSIVELY PARALLEL PROCESSING APPLICATIONS AND DEVELOPMENT. (pp. 871-878). AMSTERDAM: Elsevier.
Petkov, N. (1994). Conceiving computationally intensive approaches to vision. In High-Performance Computing and Networking. (Vol. 796, pp. 380-389). Springer Berlin Heidelberg.
BRUINS, S., DEJONG, M. C. J. M., HEERES, K., WILKINSON, M. H. F., JONKMAN, M. F., & VANDERMEER, J. B. (1994). FLUORESCENCE OVERLAY ANTIGEN MAPPING OF THE EPIDERMAL BASEMENT-MEMBRANE ZONE .1. GEOMETRIC ERRORS. Journal of Histochemistry & Cytochemistry, 42(4), 555-560.
Wilkinson, M. H. F. (1994). A simple data compression scheme for binary images of bacteria compared with commonly used image data compression schemes. Computer Methods and Programs in Biomedicine, 42(4), 255-262.
Wilkinson, M. H. F. (1994). Shading correction and calibration in bacterial fluorescence measurement by image processing system. Computer Methods and Programs in Biomedicine, 44(2), 61-67.
Wilkinson, M. H. F., Jansen, G. J., & Waaij, D. V. D. (1994). Computer processing of microscopic images of bacteria: morphometry and fluorimetry. Default journal.
Biehl, M., & Riegler, P. (1994). On-Line Learning with a Perceptron. Europhysics Letters, 28(7), 525-530. DOI: 10.1209/0295-5075/28/7/012
Biehl, M. (1994). An Exactly Solvable Model of Unsupervised Learning. Europhysics Letters, 25, 391-396. DOI: 10.1209/0295-5075/25/5/014
Biehl, M., & Mietzner, A. (1994). Statistical mechanics of unsupervised structure recognition. Journal of Physics A, Mathematical and General, 27(6), 1885-1897. DOI: 10.1088/0305-4470/27/6/015
1993
PETKOV, N. (1993). FUZZY NUMBER SUBTRACTION CONVOLUTION ON THE CM-2. International journal of modern physics c-Physics and computers, 4(1), 181-196.
Petkov, N. (1993). Systolic Parallel Processing. Elsevier.
Petkov, N., Kruizinga, P., & Lourens, T. (1993). Biologically motivated approach to face recognition. In J. Mira, J. Cabestany, & A. Prieto (Eds.), New Trends in Neural Computation: nternational Workshop on Artificial Neural Networks, IWANN '93 Sitges, Spain, June 9–11, 1993 Proceedings. (pp. 68-77). ( Lecture Notes in Computer Science ; Vol. 686). Heidelberg: Springer.
Petkov, N., Lourens, T., & Kruizinga, P. (1993). Lateral inhibition in cortical filters. In Proc. of Int. Conf. on Digital Signal Processing and Int. Conf. on Computer Applications to Engineering Systems. (pp. 122-129). Citeseer.
Petkov, N., Kruizinga, P., & Lourens, T. (1993). Orientation competition in cortical filters - an application to face recognition. Computing Science in The Netherlands, 285-296.
Petkov, N., & Lourens, T. (1993). Interacting Cortical Filters for Object Recognition. In Proceedings of Asian Conference on Computer Vision, ACCV '93. (pp. 583-586)
Petkov, N., & Lourens, T. (1993). Human visual system simulations-An application to face recognition. In Proceedings of European Conference on Circuit Theory and Design. (pp. 821-826)
Petkov, N., Kruizinga, P., & Lourens, T. (1993). Face Recognition on the Connection Machine CM-5. In Proceedings of Parallel Computing (PARCO). (pp. 185-192)
Hateren, J. H. V. (1993). SPATIOTEMPORAL CONTRAST SENSITIVITY OF EARLY VISION. Vision Research, 33(2), 257-267.
VANHATEREN, J. H. (1993). SPATIAL, TEMPORAL AND SPECTRAL PREPROCESSING FOR COLOR-VISION. Proceedings of the Royal Society of London. Series B, Biological Sciences, 251(1330), 61-68.
JANSONIUS, N. M., & VANHATEREN, J. H. (1993). ON-OFF UNITS IN THE 1ST OPTIC CHIASM OF THE BLOWFLY .2. SPATIAL PROPERTIES. Journal of comparative physiology a-Sensory neural and behavioral physiology, 172(4), 467-471.
VANHATEREN, J. H. (1993). 3 MODES OF SPATIOTEMPORAL PREPROCESSING BY EYES. Journal of comparative physiology a-Sensory neural and behavioral physiology, 172(5), 583-591.
JANSONIUS, N. M., & VANHATEREN, J. H. (1993). ON SPIKING UNITS IN THE 1ST OPTIC CHIASM OF THE BLOWFLY .3. THE SUSTAINING UNIT. Journal of comparative physiology a-Sensory neural and behavioral physiology, 173(2), 187-192.
Hateren, J. H. V. (1993). Spatial, temporal and spectral pre-processing for colour vision. Default journal.
Hateren, J. H. V. (1993). Three modes of spatiotemporal preprocessing by eyes. Default journal.
Jansonius, N. M., & Hateren, J. H. V. (1993). On spiking units in the first optic chiasm of the blowfly. III. The sustaining unit. Default journal.
Jansonius, N. M., & Hateren, J. H. V. (1993). On-off units in the first optic chiasm of the blowfly. II. Spatial properties. Default journal.
Jansen, G. J., Wilkinson, M. H. F., Deddens, B., & Waaij, D. V. D. (1993). Statistical evaluation of an improved quantitative immunofluorescence method of measuring serum antibody levels directed against intestinal bacteria. Journal of microbiological methods, 17(2), 137-144.
JANSEN, G., DEDDENS, B., WILKINSON, M., & VANDERWAAIJ, D. (1993). SIGNIFICANT DECREASE OF TITERS OF CIRCULATING IGG AFTER ORAL INTAKE OF A PREPARATION OF ENTEROCOCCUS-FAECALIS IN A GROUP OF 10 HEALTHY-VOLUNTEERS. Infection, 21(3), 193-194.
JANSEN, G. J., WILKINSON, M. H. F., DEDDENS, B., & VANDERWAAIJ, D. (1993). CHARACTERIZATION OF HUMAN FECAL FLORA BY MEANS OF AN IMPROVED FLUORO-MORPHOMETRICAL METHOD. EPIDEMIOLOGY AND INFECTION, 111(2), 265-272.
Jansen, G., Deddens, B., Wilkinson, M., & Waaij, D. V. D. (1993). Significant Decrease of Titres of Circulating IgG after Oral Intake of a Preparation of Enterococcus faecalis in a Group of Ten Healthy Volunteers. Default journal.
Jansen, G. J., Wilkinson, M. H. F., Deddens, B., & Waaij, D. V. D. (1993). Characterization of human faecal flora by means of an improved fluoro-morphometrical method. Default journal.
WATKIN, T. L. H., RAU, A., & BIEHL, M. (1993). THE STATISTICAL-MECHANICS OF LEARNING A RULE. Reviews of Modern Physics, 65(2), 499-556. DOI: 10.1103/RevModPhys.65.499
Biehl, M., & Opper, M. (1993). Construction algorithm for the parity-machine. Physica A: Statistical Mechanics and its Applications, 193(3-4), 307-313. DOI: 10.1016/0378-4371(93)90477-L
Biehl, M., & Schwarze, H. (1993). Learning drifting concepts with neural networks. Journal of Physics A, Mathematical and General, 26(11). DOI: 10.1088/0305-4470/26/11/014
Biehl, M., & Mietzner, A. (1993). Statistical Mechanics of Unsupervised Learning. Europhysics Letters, 24, [421-426]. DOI: 10.1209/0295-5075/24/5/017
1992
Lippert, T., Schilling, K., & Petkov, N. (1992). Quark propagator on the Connection Machine. Default journal.
LIPPERT, T., SCHILLING, K., & PETKOV, N. (1992). QUARK PROPAGATOR ON THE CONNECTION MACHINE. Parallel Computing, 18(12), 1291-1299.
Dontje, T., Lippert, T., Petkov, N., & Schilling, K. (1992). Statistical analysis of simulation-generated time series: Systolic vs. semi-systolic correlation on the Connection Machine. Default journal.
Dontje, T., Petkov, N., & Schilling, K. (1992). Computational and Communicational Granularity of Systolic Algorithms on the Connection Machine. Parallel Computing, 91.
Hateren, J. H. V. (1992). Real and optimal neural images in early vision. Default journal.
Hateren, J. H. V. (1992). A THEORY OF MAXIMIZING SENSORY INFORMATION. Biological Cybernetics, 68(1), 23-29.
Hateren, J. H. V. (1992). Theoretical predictions of spatiotemporal receptive fields of fly LMCs, and experimental validation. Journal of comparative physiology a-Sensory neural and behavioral physiology, 171(2), 157-170.
VANHATEREN, J. H. (1992). REAL AND OPTIMAL NEURAL IMAGES IN EARLY VISION. Nature, 360(6399), 68-70.
APPERLOORENKEMA, H. Z., WILKINSON, M. H. F., & VANDERWAAIJ, D. (1992). CIRCULATING ANTIBODIES AGAINST FECAL BACTERIA ASSESSED BY IMMUNOMORPHOMETRY - COMBINING QUANTITATIVE IMMUNOFLUORESCENCE AND IMAGE-ANALYSIS. EPIDEMIOLOGY AND INFECTION, 109(3), 497-506.
Apperloo-Renkema, H. Z., Wilkinson, M. H. F., & Waaij, D. V. D. (1992). Circulating antibodies against faecal bacteria assessed by immunomorphometry: combining quantitative immunofluorescence and image analysis. Default journal.
Biehl, M., & Schwarze, H. (1992). On-Line Learning of a Time-Dependent Rule. Europhysics Letters, 20, 733-738. DOI: 10.1209/0295-5075/20/8/012
1990
Petkov, N. (1990). Mapping systolic FIR filter banks onto fixed-size linear processor arrays. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
Petkov, N. (1990). Systolic simulation of multilayer, feedforward neural networks. In Proc. Int. Conf. on Parallel Processing in Neural Systems and Computers. (pp. 303-306). Düsseldorf.
Petkov, N. (1990). SYSTOLIC ALGORITHMS FOR MULTILAYER, FEEDFORWARD, ARTIFICIAL NEURAL NETWORKS. In First World Conference on Parallel Computing in Engineering and Engineering Education. (pp. 321). Paris: Microcomputer Unit.
Hateren, J. H. V., & Laughlin, S. B. (1990). Membrane parameters, signal transmission, and the design of a graded potential neuron. Journal of comparative physiology a-Neuroethology sensory neural and behavioral physiology, 166(4), 437-448.
Hateren, J. H. V. (1990). Directional Tuning Curves, Elementary Movement Detectors, and the Estimation of the Direction of Visual Movement. Vision Research, 30(4), 603-614.
van Hateren, J. H., Srinivasan, M. V., & Wait, P. (1990). Discrimination of pattern orientation in bees. Perception, 19(4), 392-392.
Hateren, J. H. V., Srinivasan, M. V., & Wait, P. B. (1990). Pattern recognition in bees: orientation discrimination. Journal of comparative physiology a-Sensory neural and behavioral physiology, 167(5), 649-654.
APPERLOORENKEMA, H. Z., JAGT, T. G., WILKINSON, M. H. F., & VANDERWAAIJ, D. (1990). COLONIZATION RESISTANCE AND ANTIBODIES AGAINST THE INDIGENOUS INTESTINAL MICROFLORA IN 10 HEALTHY-VOLUNTEERS - A RELATIONSHIP. In P. J. HEIDT, J. M. VOSSEN, & V. C. RUSCH (Eds.), MICROECOLOGY AND THERAPY, VOL 20. (pp. 489-494). (MICROECOLOGY AND THERAPY; Vol. 20). HERBORN-DILL: INST MICROECOLOGY & BIOCHEM.
APPERLOORENKEMA, H. Z., JAGT, T. G., WILKINSON, M. H. F., MEIJER, B. C., & VANDERWAAIJ, D. (1990). CHARACTERIZATION OF THE INTERINDIVIDUAL DIVERSITY OF THE SERUM ANTIBACTERIAL ANTIBODY-RESPONSE AGAINST THE INDIGENOUS INTESTINAL MICROFLORA OF HEALTHY-VOLUNTEERS. In P. J. HEIDT, J. M. VOSSEN, & V. C. RUSCH (Eds.), MICROECOLOGY AND THERAPY, VOL 20. (pp. 495-499). (MICROECOLOGY AND THERAPY; Vol. 20). HERBORN-DILL: INST MICROECOLOGY & BIOCHEM.
Anlauf, J. K., & Biehl, M. (1990). Properties of an adaptive perceptron algorithm. In R. Eckmiller, G. Hartmann, & G. Haucke (Eds.), Parallel processing in neural networks and computers. Elsevier.
Anlauf, J. K., & Biehl, M. (1990). Erratum: The AdaTron: an adaptive perceptron algorithm. Europhysics Letters, 11, 387-387. DOI: 10.1209/0295-5075/11/4/016
1989
Petkov, N., & Sloboda, F. (1989). A bit-level systolic array for digital contour smoothing. Default journal.
Petkov, N. (1989). Systolische Algorithmen und Arrays. Berlin: Akademie Verlag.
Petkov, N. (1989). Systolic arrays for some proximity problems. Future generation computer systems, 2(3).
Petkov, N., Evans, D. J., Joubert, G. R., & Peters, F. J. (1989). Time-optimal systolic algorithms for some proximity problems'. In Proceedings of International Conference on Parallel Computing. (pp. 229-234)
Petkov, N. (1989). Running order statistics on a bit-level systolic array. In Parcella'88: Fourth International Workshop on Parallel Processing by Cellular Automata and Arrays. (pp. 317-325). Berlin, GDR: Springer Berlin Heidelberg.
Petkov, N. (1989). Utilizing fixed-size systolic arrays for large computational problems. In Recent issues in pattern analysis and recognition. (Vol. 399, pp. 132-142). Springer Berlin Heidelberg.
Petkov, N. (1989). Design of bit-level systolic convolvers for image processing. In V. Cantoni, R. Creutzburg, S. Levialdi, & G. Wolf (Eds.), Recent issues in pattern analysis and recognition. (pp. 121-131). (Lecture Notes in Computer Science; Vol. 399). Springer Berlin Heidelberg. DOI: 10.1007/3-540-51815-0
Petkov, N. (1989). Systolic array for edge detection. In Proceedings CAIP.
Hateren, J. H. V., Hardie, R. C., Laughlin, S. B., & Stavenga, D. G. (1989). The bright zone, a specialized dorsal eye region in the male blowfly Chrysomyia megacephala. Journal of Comparative Physiology A; Sensory Neural, and Behavioral Physiology, 164(3), 297-308. DOI: 10.1007/BF00612990
Hateren, J. H. V., Hardie, R. C., Laughlin, S. B., & Stavenga, D. G. (1989). The bright zone, a specialized dorsal eye region in the male blowfly Chrysomyia megacephala. Default journal.
Hateren, J. H. V. (1989). Photoreceptor Optics, Theory and Practice. In EPRINTS-BOOK-TITLE. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science.
VANHATEREN, J. H., HARDIE, R. C., LAUGHLIN, S. B., & STAVENGA, D. G. (1989). THE BRIGHT ZONE, A SPECIALIZED DORSAL EYE REGION IN THE MALE BLOWFLY CHRYSOMYIA-MEGACEPHALA. Journal of comparative physiology a-Sensory neural and behavioral physiology, 164(3), 297-308.
Anlauf, J. K., & Biehl, M. (1989). The AdaTron: an adaptive perceptron algorithm. Europhysics Letters, 10(7), 687-692. DOI: 10.1209/0295-5075/10/7/014
1988
Petkov, N. (1988). Systolic arrays for matrix I/O format conversion. Default journal.
Petkov, N. (1988). Bit-level systolic array for running order statistics. Preprint Basic Laboratory for Image Processing and Computer Graphics.
Petkov, N. Systolic array for fast multidimensional rank filtering
1987
Petkov, N. (1987). Systolic array for all-nearest-neighbours problem. Default journal.
Petkov, N. (1987). Bit-Organized Systolic FIR Filter. In Proceedings of the 1st Hungarian Custom Circuits Conference. (pp. 161-168)
Petkov, N. (1987). Beitrag zur Theorie und Synthese systolischer Algorithmen und Arrays Dresden: Technische Universitaet Dresden
Hateren, J. H. V. (1987). Neural superposition and oscillations in the eye of the blowfly. Journal of comparative physiology a-Sensory neural and behavioral physiology, 161(6), 849-855.
Hateren, J. H. V., & Nilsson, D-E. (1987). Butterfly Optics Exceed the Theoretical Limits of Conventional Apposition Eyes. Biological Cybernetics, 57(3), 159-168.
van Hateren, J. H. (1987). Photoreceptor optics and neural microcircuitry in the insect eye s.n.
1986
Petkov, N. (1986). Bit-organised systolic convolution algorithm. IETE Technical Review , 3(6). DOI: 10.1080/02564602.1986.11437970
Lalov, I. J., Svetogorski, D. A., & Turkedjiev, N. P. (1986). Overtone spectra of helical polymers: Infrared absorption and vibrational circular dichroism. Journal of Chemical Physics, 84(6), 3545-3552. DOI: 10.1063/1.450240
Petkov, N. (1986). Synthesis of systolic algorithms and processor arrays: Conference on Algorithms and Hardware for Parallel Processing Aachen, September 17--19, 1986 Proceedings. In W. Händler, D. Haupt, R. Jeltsch, W. Juling, & O. Lange (Eds.), CONPAR 86: Conference on Algorithms and Hardware for Parallel Processing Aachen, September 17--19, 1986 Proceedings. (pp. 165-172). Berlin, Heidelberg: Springer Berlin Heidelberg. DOI: 10.1007/3-540-16811-7_167
Hateren, J. H. V. (1986). Electrical coupling of neuro-ommatidial photoreceptor cells in the blowfly. Journal of comparative physiology a-Sensory neural and behavioral physiology, 158(6), 795-811.
VANHATEREN, J. H. (1986). AN EFFICIENT ALGORITHM FOR CABLE THEORY, APPLIED TO BLOWFLY PHOTORECEPTOR CELLS AND LMCS. Biological Cybernetics, 54(4-5), 301-311.
Hateren, J. H. V. (1986). An Efficient Algorithm for Cable Theory, Applied to Blowfly Photoreceptor Cells and LMC's. Default journal.
2017
van Hateren, J. H. (2017). A Unifying Theory of Biological Function. Biological Theory. DOI: 10.1007/s13752-017-0261-y
Arlt, W., Lang, K., Sitch, A. J., Dietz, A. S., Rhayem, Y., Bancos, I., ... Reincke, M. (2017). Steroid metabolome analysis reveals prevalent glucocorticoid excess in primary aldosteronism. JCI insight, 2(8). DOI: 10.1172/jci.insight.93136
1991
Stavenga, D. G., & Hateren, J. H. V. (1991). Focusing by a high-power, low-Fresnel-number lens: the fly facet lens. Journal of the Optical Society of America A, 8(1), 14-19. DOI: 10.1364/JOSAA.8.000014
JANSONIUS, N. M., & VANHATEREN, J. H. (1991). FAST TEMPORAL ADAPTATION OF ON-OFF UNITS IN THE 1ST OPTIC CHIASM OF THE BLOWFLY. Journal of comparative physiology a-Sensory neural and behavioral physiology, 168(6), 631-637.
Jansonius, N. M., & Hateren, J. H. V. (1991). Fast temporal adaptation of on-off units in the first optic chiasm of the blowfly. Default journal.
Wilkinson, M. H. F., & Meijer, B. C. (1991). MORPHOMETRICAL PARAMETERS OF GUT MICROFLORA IN HUMAN VOLUNTEERS. EPIDEMIOLOGY AND INFECTION, 107(2), 383-391.
Geertsma, D. G., Wilkinson, M. H. F., & Meijer, B. C. (1991). Effects of ceftriaxone on faecal flora: analysis by micromorphometry. Default journal.
APPERLOORENKEMA, H. Z., WILKINSON, M. H. F., OENEMA, D. G., & VANDERWAAIJ, D. (1991). OBJECTIVE QUANTITATION OF SERUM ANTIBODY-TITERS AGAINST ENTEROBACTERIACEAE USING INDIRECT IMMUNOFLUORESCENCE, READ BY VIDEOCAMERA AND IMAGE-PROCESSING SYSTEM. Medical microbiology and immunology, 180(2), 93-100.
MEIJER, B. C., KOOTSTRA, G. J., GEERTSMA, D. G., & WILKINSON, M. H. F. (1991). EFFECTS OF CEFTRIAXONE ON FECAL FLORA - ANALYSIS BY MICROMORPHOMETRY. EPIDEMIOLOGY AND INFECTION, 106(3), 513-521.
Apperloo-Renkema, H. Z., Wilkinson, M. H. F., Oenema, D. G., & Waaij, D. V. D. (1991). Objective quantitation of serum antibody titres against Enterobacteriaceae using indirect immunofluorescence, read by videocamera and image processing system. Default journal.
Biehl, M., & Opper, M. (1991). Tilinglike learning in the parity machine. Physical Review A, 44(10), 6888-6894. DOI: 10.1103/PhysRevA.44.6888
Biehl, M., Anlauf, J. K., & Kinzel, W. (1991). Perceptron learning by constrained optimization: the AdaTron algorithm. In F. Pasemann, & H. D. Doebner (Eds.), Neurodynamics: Proc. 9th Summer Workshop on Mathematical Physics, Arnold Sommerfeld Institute, Clausthal, 1990. (pp. 194-210). (Series on Neural Networks; Vol. 1). World Scientific Publishing.
1985
Hateren, J. H. V. (1985). The Stiles-Crawford Effect in the Eye of the Blowfly, Calliphora erythrocephala. Vision Research, 25(9), 1305-1315.
1984
Smakman, J. G. J., Hateren, J. H. V., & Stavenga, D. G. (1984). Angular sensitivity of blowfly photoreceptors: intracellular measurements and wave-optical predictions. Journal of Comparative Physiology A; Sensory Neural, and Behavioral Physiology, 155(2), 239-247. DOI: 10.1007/BF00612641
VANHATEREN, J. H. (1984). WAVEGUIDE THEORY APPLIED TO OPTICALLY MEASURED ANGULAR SENSITIVITIES OF FLY PHOTORECEPTORS. Journal of Comparative Physiology, 154(6), 761-771.
SMAKMAN, J. G. J., VANHATEREN, J. H., & STAVENGA, D. G. (1984). ANGULAR SENSITIVITY OF BLOWFLY PHOTORECEPTORS - INTRACELLULAR MEASUREMENTS AND WAVE-OPTICAL PREDICTIONS. Journal of Comparative Physiology, 155(2), 239-247.
Laatst gewijzigd:02 mei 2016 15:22