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Schomaker, prof. dr. Lambert

Lambert Schomaker
Lambert Schomaker

Lambert Schomaker is hoogleraar Kunstmatige Intelligentie en wetenschappelijk directeur van het onderzoeksinstituut ALICE (Artificial Intelligence & Cognitive Engineering). Schomaker heeft zijn naam verbonden aan diverse projecten waarin in schuinschrift geschreven teksten online worden geanalyseerd op basis van de kennis over de bewegingen die bij het schrijven worden gemaakt. Op dit moment werkt hij aan een aantal projecten op het gebied van verwerking op basis van afbeeldingen, online en offline handschriftherkenning, forensische auteursherkenning en cognitieve robotnavigatiemodellen. Zijn werk op het gebied van neurale netwerken voor handschrift- en gebaarherkenning was een voorloper van de hedendaagse methoden van handschrift- en gebaarherkenning die op tabletcomputers zoals de iPad worden gebruikt.

Momenteel werkt hij aan een project (Target) op het gebied van grootschalige opslag, supercomputers en datamining, aan de hand waarvan de generieke zoekmachine Monk voor handgeschreven historische archieven kan worden geïmplementeerd. Het systeem achter Monk is wereldwijd uniek dankzij zijn enorme schaal, genericiteit en het gebruik van ‘live’ , '24/7', machinaal leren. Binnen een ander project, genaamd Mantis, is Schomaker bezig met het ontwikkelen van slimme systemen die problemen in productielijnen kunnen opsporen en oplossen. Schomaker ontwikkelt op basis van de robuustheidsbeginselen uit de Kunstmatige Intelligentie slimme systemen die problemen in productielijnen kunnen opsporen en oplossen.

In 2021 ontdekten prof. Mladen Popović (expert op het gebied van de Dode Zeerollen), promovendus Maruf Dhali en Schomaker, door analyse van het handschrift met kunstmatige intelligentie, dat de beroemde grote Jesaja-rol door twee schrijvers is geschreven.

In 2023 kreeg het project HAICu, waar Schomaker penvoerder vanuit de RUG van is, een subsidie van 10,3 miljoen euro van de Nationale Wetenschapsagenda. In het onderzoeksproject werken AI- en Digital Humanities-onderzoekers samen met erfgoedprofessionals en geïnteresseerde burgers aan wetenschappelijke doorbraken om grootschalige digitale erfgoedcollecties te ontsluiten, te koppelen en te analyseren.

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Publicaties

2024

Chen, Y., Schomaker, L., & Cruz, F. (2024). Boosting Reinforcement Learning Algorithms in Continuous Robotic Reaching Tasks using Adaptive Potential Functions. arXiv. http://arxiv.org/abs/2402.04581v1
Ogum, B., Schomaker, L., & Carloni, R. (2024). Learning to Walk With Deep Reinforcement Learning: Forward Dynamic Simulation of a Physics-Based Musculoskeletal Model of an Osseointegrated Transfemoral Amputee: Forward Dynamic Simulation of a Physics-Based Musculoskeletal Model of an Osseointegrated Transfemoral Amputee. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 32, 431-441. https://doi.org/10.1109/TNSRE.2024.3352416
Cipollini, D., Swierstra, A., & Schomaker, L. (2024). Modeling a domain wall network in BiFeO3 with stochastic geometry and entropy-based similarity measure. Frontiers in Materials, 11, Article 1323153. https://doi.org/10.3389/fmats.2024.1323153
Zhang, Z., & Schomaker, L. (2024). Optimizing and interpreting the latent space of the conditional text-to-image GANs. Neural Computing and Applications, 36(5), 2549–2572. https://doi.org/10.1007/s00521-023-09185-6
Dhali, M. A., Reynolds, T., Alizadeh, A. Z., Nijdam, S. H., & Schomaker, L. (2024). Pattern Recognition Techniques in Image-Based Material Classification of Ancient Manuscripts. In Lecture Notes in Computer Science: Pattern Recognition Applications and Methods (Vol. 14547, pp. 124–150). Springer International Publishing, Cham, Switzerland. https://doi.org/10.1007/978-3-031-54726-3_8

2023

Cipollini, D., & Schomaker, L. (2023). Conduction and entropy analysis of a mixed memristor-resistor model for neuromorphic networks. Neuromorphic computing and engineering, 3(3), Article acd6b3. https://doi.org/10.1088/2634-4386/acd6b3
Ameryan, M., & Schomaker, L. (2023). Correction to: A Limited-size ensemble of homogeneous CNN/LSTMS for high-performance word classification. Neural Computing and Applications, 35, 20443–20444. https://doi.org/10.1007/s00521-023-08855-9
Zhang, Z., & Schomaker, L. (2023). Fusion-S2iGan: An Efficient and Effective Single-Stage Framework for Speech-to-Image Generation. arXiv. http://arxiv.org/abs/2305.10126v1
Ameryan, M., & Schomaker, L. (2023). How to limit label dissipation in neural-network validation: Exploring label-free early-stopping heuristics. Journal on Computing and Cultural Heritage, 16(1), 1-20. Article 2. https://doi.org/10.1145/3587168
Reynolds, T., Dhali, M., & Schomaker, L. (2023). Image-Based Material Analysis of Ancient Historical Documents. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM (Vol. 1, pp. 697-706). SciTePress. https://doi.org/10.5220/0011743700003411
Luo, S., & Schomaker, L. (2023). Multiple Subgoals-guided Hierarchical Learning in Robot Navigation. In 2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022 (pp. 9-14). (2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ROBIO55434.2022.10011912
Wenniger, G. M. D. B., Dongen, T. V., & Schomaker, L. (2023). MultiSChuBERT: Effective Multimodal Fusion for Scholarly Document Quality Prediction. arXiv. https://doi.org/10.48550/arXiv.2308.07971
Schomaker, L., Timmermans, J., & Banerjee, T. (2023). Non-linear adaptive control inspired by neuromuscular systems. Bioinspiration & biomimetics, 18(4), Article 046015. https://doi.org/10.1088/1748-3190/acd896
Anteghini, M., Haja, A., Martins Dos Santos, V. A. P., Schomaker, L., & Saccenti, E. (2023). OrganelX web server for sub-peroxisomal and sub-mitochondrial protein localization and peroxisomal target signal detection. Computational and Structural Biotechnology Journal, 21, 128-133. https://doi.org/10.1016/j.csbj.2022.11.058
Luo, S., & Schomaker, L. (2023). Reinforcement learning in robotic motion planning by combined experience-based planning and self-imitation learning. Robotics and Autonomous Systems, 170, Article 104545. https://doi.org/10.1016/j.robot.2023.104545
Haja, A., Zhelev, I., & Schomaker, L. (2023). Segmentation Of Organoid Cultures Images Using Diffusion Networks with Triplet Loss. In Proceedings of 2023 10th International Conference on Biomedical and Bioinformatics Engineering (ICBBE 2023) (pp. 1-10). ACM Press. https://doi.org/10.1145/3637732.3637770
Haja, A., Brouwer, E., & Schomaker, L. (2023). Self-Supervised Versus Supervised Training for Segmentation of Organoid Images. arXiv. https://doi.org/10.48550/arXiv.2311.11198
Koopmans, L., Dhali, M., & Schomaker, L. (2023). The Effects of Character-Level Data Augmentation on Style-Based Dating of Historical Manuscripts. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM (Vol. 1, pp. 124-135). SciTePress. https://doi.org/10.5220/0011699500003411
Haja, A., Horcas-Nieto, J. M., Bakker, B. M., & Schomaker, L. (2023). Towards automatization of organoid analysis: A deep learning approach to localize and quantify organoid images. Computer Methods and Programs in Biomedicine Update, 3, Article 100101. https://doi.org/10.1016/j.cmpbup.2023.100101

2022

Rieck, J. L., Cipollini, D., Salverda, M., Quinteros, C. P., Schomaker, L. R. B., & Noheda, B. (2023). Ferroelastic Domain Walls in BiFeO3 as Memristive Networks. Advanced Intelligent Systems, 5(1), Article 2200292. https://doi.org/10.1002/aisy.202200292
Haja, A., Radu, S., & Schomaker, L. (2022). A Comparison of Different U-Net Models for Segmentation of Overlapping Organoids. In ICBBE 2022: Proceeding of 2022 9th International Conference on Biomedical and Bioinformatics Engineering (pp. 1-10). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3574198.3574199
Jacobs, P. F., Maillette De Buy Wenniger, G., Wiering, M., & Schomaker, L. (2022). Active Learning for Reducing Labeling Effort in Text Classification Tasks. In L. A. Leiva , C. Pruski , R. Markovich , A. Najjar , & C. Schommer (Eds.), 33rd Benelux Conference on Artificial Intelligence, BNAIC/Benelearn 2021: Esch-sur-Alzette, Luxembourg, November 10–12, 2021, Revised Selected Papers (pp. 3-29). (Artificial Intelligence and Machine Learning; Vol. 1530). Springer Verlag. https://doi.org/10.1007/978-3-030-93842-0_1
Chen, P.-H., Carmona-Garcia, J., Schomaker, L., & Roca-Sanjuán, D. (2022). Combined QM-ML Approach to Accelerate Photodynamics Simulation with High Robustness. Poster session presented at 12th Congress on Electronic Structure Principles and Applications (ESPA-2022) , Valencia, Spain.
Zhang, Z., & Schomaker, L. (2022). DiverGAN: An Efficient and Effective Single-Stage Framework for Diverse Text-to-Image Generation. Neurocomputing, 473, 182-198. https://doi.org/10.1016/j.neucom.2021.12.005
de Wit, B., Schomaker, L., & Broekhuizen, J. (2022). From Data To Control: Learning an HVAC Control Policy. In CLIMA 2022 The 14th REHVA HVAC World Congress https://doi.org/10.34641/clima.2022.362
Marcos Mazon, D., Groefsema, M., Schomaker, L., & Carloni, R. (2022). IMU-Based Classification of Locomotion Modes, Transitions, and Gait Phases with Convolutional Recurrent Neural Networks. Sensors, 22, Article 8871. https://doi.org/10.3390/s22228871
Zhang, Z., & Schomaker, L. (2022). Optimized latent-code selection for explainable conditional text-to-image GANs. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1-9). IEEE. https://doi.org/10.1109/IJCNN55064.2022.9892738
Bleijendaal, H., Boersma, J., Lopes, R. R., Van Der Ree, M. H., Meijborg, V. M. F., Amin, A. S., Winter, M. M., Marquering, H., Schomaker, L., Zwinderman, A. H., Pinto, Y. M., Wilde, A. A. M., & Postema, P. G. (2022). PO-631-03 DEEP LEARNING FOR THE IDENTIFICATION OF PATIENTS WITH A HIGH RISK FOR IDIOPATHIC VENTRICULAR FIBRILLATION. Heart Rhythm, 19(5, Supplement), S168-S169. https://doi.org/10.1016/j.hrthm.2022.03.904
Mostard, W., Schomaker, L., & Wiering, M. (2022). Semantic Preserving Siamese Autoencoder for Binary Quantization of Word Embeddings. In NLPIR 2021: 2021 5th International Conference on Natural Language Processing and Information Retrieval (NLPIR) (pp. 30-38). ACM Press. https://doi.org/10.1145/3508230.3508235

2021

Jacobs, P. F., Wenniger, G. M. D. B., Wiering, M., & Schomaker, L. (2021). Active learning for reducing labeling effort in text classification tasks. (ArXiv). arXiv.
Haja, A., & Schomaker, L. R. B. (2021). A Fully Automated End-to-End Process for Fluorescence Microscopy Images of Yeast Cells: From Segmentation to Detection and Classification. In R. Su, Y.-D. Zhang, & H. Liu (Eds.), Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021): Medical Imaging and Computer-Aided Diagnosis (pp. 37-46). (Lecture Notes in Electrical Engineering; Vol. 784). Springer. https://doi.org/10.1007/978-981-16-3880-0_5
Ameryan, M., & Schomaker, L. (2021). A high-performance word recognition system for the biological fieldnotes of the Natuurkundige Commissie. In A. Weber, M. Heerlien, E. Gassó Miracle, & K. Wolstencroft (Eds.), Collect and Connect: Archives and Collections in a Digital Age 2020 (pp. 92-103). (CEUR Workshop Proceedings; Vol. 2810). CEUR-WS.org.
Chen, Y., Schomaker, L., & Wiering, M. (2021). An Investigation Into the Effect of the Learning Rate on Overestimation Bias of Connectionist Q-learning. In A. P. Rocha, L. Steels, & J. van den Herik (Eds.), Proceedings of the 13th International Conference on Agents and Artificial Intelligence (Vol. 2, pp. 107-118). SciTePress. https://doi.org/10.5220/0010227301070118
Popović, M., Dhali, M. A., & Schomaker, L. (2021). Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa). PLoS ONE, 16(4), Article e0249769. https://doi.org/10.1371/journal.pone.0249769
He, S., & Schomaker, L. (2021). CT-Net: Cascade T-shape deep fusion networks for document binarization. Pattern recognition, 118, Article 108010. https://doi.org/10.1016/j.patcog.2021.108010
Zhang, Z., & Schomaker, L. (2021). DTGAN: Dual Attention Generative Adversarial Networks for Text-to-Image Generation. In 2021 International Joint Conference on Neural Networks (IJCNN) IEEE. https://doi.org/10.1109/IJCNN52387.2021.9533527
He, S., & Schomaker, L. (2021). GR-RNN: Global-context residual recurrent neural networks for writer identification. Pattern recognition, 117, Article 107975. https://doi.org/10.1016/j.patcog.2021.107975
Schomaker, L. (2021). Lifelong Learning for Text Retrieval and Recognition in Historical Handwritten Document Collections. In A. Fischer, M. Liwicki, & R. Ingold (Eds.), Handwritten Historical Document Analysis, Recognition, and Retrieval — State of the Art and Future Trends: Series in Machine Perception and Artificial Intelligence (Vol. 89, pp. 221-248). (Series in Machine Perception and Artificial Intelligence; Vol. 89). World Scientific Publishing. https://doi.org/10.1142/9789811203244_0012
Chanda, S., Haitink, D., Prasad, P. K., Baas, J., Pal, U., & Schomaker, L. (2021). Recognizing Bengali Word Images - A Zero-Shot Learning Perspective. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 5603-5610). Article 9412607 IEEE. https://doi.org/10.1109/ICPR48806.2021.9412607
Chen, Y., Kasaei, H., Schomaker, L., & Wiering, M. (2021). Reinforcement Learning with Potential Functions Trained to Discriminate Good and Bad States. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). Article 9533682 IEEE. https://doi.org/10.1109/IJCNN52387.2021.9533682
Luo, S., Kasaei, H., & Schomaker, L. (2021). Self-Imitation Learning by Planning. In 2021 IEEE International Conference on Robotics and Automation (ICRA) (pp. 4823-4829). IEEE. https://doi.org/10.1109/ICRA48506.2021.9561411
Shantia, A., Timmers, R., Chong, Y., Kuiper, C., Bidoia, F., Schomaker, L., & Wiering, M. (2021). Two-stage visual navigation by deep neural networks and multi-goal reinforcement learning. Robotics and Autonomous Systems, 138, Article 103731. https://doi.org/10.1016/j.robot.2021.103731

2020

Dijkstra, K., van de Loosdrecht, J., Atsma, W. A., Schomaker, L. R. B., & Wiering, M. A. (2021). CentroidNetV2: A hybrid deep neural network for small-object segmentation and counting. Neurocomputing, 423, 490-505. https://doi.org/10.1016/j.neucom.2020.10.075
Luo, S., Kasaei, H., & Schomaker, L. (2020). Accelerating Reinforcement Learning for Reaching using Continuous Curriculum Learning. In Proceedings of 2020 International Joint Conference on Neural Networks (IJCNN) Article 9207427 IEEE. https://doi.org/10.1109/IJCNN48605.2020.9207427
Pawara, P., Boshchenko, A., Schomaker, L. R. B., & Wiering, M. A. (2020). Deep Learning with Data Augmentation for Fruit Counting. In L. Rutkowski, R. Scherer, M. Korytkowski, W. Pedrycz, R. Tadeusiewicz, & J. M. Zurada (Eds.), 19th International Conference, ICAISC 2020, Zakopane, Poland, October 12-14, 2020: Proceedings, Part I (pp. 203-214). ( Lecture Notes in Computer Science; Vol. 12415). Springer. https://doi.org/10.1007/978-3-030-61401-0_20
Zhang, Z., & Schomaker, L. (2020). DTGAN: Dual Attention Generative Adversarial Networks for Text-to-Image Generation. (ArXiv). arXiv. http://arxiv.org/abs/2011.02709v2
Dhali, M. A., Jansen, C. N., De Wit, J. W., & Schomaker, L. (2020). Feature-extraction methods for historical manuscript dating based on writing style development. Pattern Recognition Letters, 131, 413-420. https://doi.org/10.1016/j.patrec.2020.01.027
He, S., & Schomaker, L. (2020). FragNet: Writer Identification using Deep Fragment Networks. IEEE transactions on information forensics and security, 15, 3013-3022. Article 9040654. https://doi.org/10.1109/TIFS.2020.2981236
Ameryan, M., & Schomaker, L. (2020). Improving the robustness of LSTMs for word classification using stressed word endings in dual-state word-beam search. In Proceedings of the 2020, 17th International Conference on Frontiers in Handwriting Recognition (ICFHR) (pp. 13-18). Article 9257737 (Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR; Vol. 2020-September). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/icfhr2020.2020.00014
Lu, H., Schomaker, L., & Carloni, R. (2020). IMU-based Deep Neural Networks for Locomotor Intention Prediction. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) IEEE. https://doi.org/10.1109/IROS45743.2020.9341649
Li, Y., Schomaker, L., & Kasaei, S. H. (2020). Learning to Grasp 3D Objects using Deep Residual U-Nets. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) (pp. 781-787). IEEE. https://doi.org/10.1109/RO-MAN47096.2020.9223541
Pawara, P., Okafor, E., Groefsema, M., He, S., Schomaker, L. R. B., & Wiering, M. A. (2020). One-vs-One classification for deep neural networks. Pattern recognition, 108, Article 107528. https://doi.org/10.1016/j.patcog.2020.107528
van Dongen, T., Maillette de Buy Wenniger, G., & Schomaker, L. (2020). SChuBERT: Scholarly Document Chunks with BERT-encoding boost Citation Count Prediction. In M. K. Chandrasekaran (Ed.), Proceedings of the First Workshop on Scholarly Document Processing (pp. 148-157). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.sdp-1.17
Wenniger, G. M. D. B., Dongen, T. V., Aedmaa, E., Kruitbosch, H. T., Valentijn, E. A., & Schomaker, L. (2020). Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction. In Proceedings of the First Workshop on Scholarly Document Processing (pp. 158-167). (Proceedings of the First Workshop on Scholarly Document Processing. Association for Computational Linguistics.). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.sdp-1.18
Maillette de Buy Wenniger, G., van Dongen, T., Aedmaa, E., Teun Kruitbosch, H., Valentijn, E. A., & Schomaker, L. (2020). Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction. (ArXiv). arXiv. http://adsabs.harvard.edu/abs/2020arXiv200500129M
Oosterhuis, T., & Schomaker, L. (2020). "Who is Driving around Me?": Unique Vehicle Instance Classification using Deep Neural Features. ArXiv. https://arxiv.org/abs/2003.08771

2019

Ameryan, M., & Schomaker, L. (2021). A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification. Neural Computing and Applications, 33, 8615–8634. https://doi.org/10.1007/s00521-020-05612-0
Schomaker, L. (2019). A large-scale field test on word-image classification in large historical document collections using a traditional and two deep-learning methods. ArXiv. https://arxiv.org/abs/1904.08421
Dhali, M. A., Wit, J. W. D., & Schomaker, L. (2019). BiNet: Degraded-Manuscript Binarization in Diverse Document Textures and Layouts using Deep Encoder-Decoder Networks. ArXiv. https://arxiv.org/pdf/1911.07930v1
Dijkstra, K., van de Loosdrecht, J., Schomaker, L. R. B., & Wiering, M. A. (2019). CentroidNet: A Deep Neural Network for Joint Object Localization and Counting. In U. Brefeld, E. Curry, E. Daly, B. MacNamee, A. Marascu, F. Pinelli, M. Berlingerio, & N. Hurly (Eds.), ECML PKDD 2018: Machine Learning and Knowledge Discovery in Databases (pp. 585-601). ( Lecture Notes in Computer Science; Vol. 11053). Springer. https://doi.org/10.1007/978-3-030-10997-4_36
He, S., & Schomaker, L. (2019). Deep adaptive learning for writer identification based on single handwritten word images. Pattern recognition, 88, 64-74. https://doi.org/10.1016/j.patcog.2018.11.003
He, S., & Schomaker, L. (2019). DeepOtsu: Document enhancement and binarization using iterative deep learning. Pattern recognition, 91, 379-390. https://doi.org/10.1016/j.patcog.2019.01.025
Sriman, B., & Schomaker, L. (2019). Multi-script text versus non-text classification of regions in scene images. Journal of Visual Communication and Image Representation, 62, 23-42. https://doi.org/10.1016/j.jvcir.2019.04.007
Wenniger, G. M. D. B., Schomaker, L., & Way, A. (2019). No Padding Please: Efficient Neural Handwriting Recognition. In 2019 International Conference on Document Analysis and Recognition (ICDAR) (pp. 355-362). IEEE. https://doi.org/10.1109/ICDAR.2019.00064
Sillitti, A., Schomaker, L., Anakabe, J. F., Basurko, J., Dam, P., Ferreira, H., Ferreiro, S., Gijsbers, J., He, S., Hegedus, C., Holenderski, M., Hooghoudt, J.-O., Lecuona, I., Leturiondo, U., Marcelis, Q., Moldovan, I., Okafor, E., Rebelo de Sa, C., Romero, R., ... Zurutuza, U. (2019). Providing Proactiveness: Data Analysis Techniques Portfolios. In M. Albano, E. Jantunen, G. Papa, & U. Zurutuza (Eds.), The MANTIS Book : Cyber Physical System Based Proactive Collaborative Maintenance (pp. 145-238). River Publishers. https://doi.org/10.1201/9781003339748-5
Schomaker, L., Albano, M., Jantunen, E., & Ferreira, L. L. (2019). The future of Maintenance. In M. Albano, E. Jantunen, G. Papa, & U. Zurutuza (Eds.), The MANTIS Book : Cyber Physical System Based Proactive Collaborative Maintenance (pp. 555-567). River Publishers. https://doi.org/10.1201/9781003339748-9
Steging, C., Schomaker, L., & Verheij, B. (2019). The Xai paradox: Systems that perform well for the wrong reasons. Paper presented at BNAIC/Benelearn Conference, Brussels, Belgium.

2018

Bidoia, F., Sabatelli, M., Shantia, A., Wiering, M. A., & Schomaker, L. (2018). A Deep Convolutional Neural Network for Location Recognition and Geometry based Information. In M. De Marsico, G. Sanniti di Baja, & A. Fred (Eds.), Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods (pp. 27-36). SciTePress. https://doi.org/10.5220/0006542200270036
Okafor, E., Schomaker, L., & Wiering, M. A. (2018). An analysis of rotation matrix and colour constancy data augmentation in classifying images of animals. Journal of Information and Telecommunication, 2(4), 465-491. https://doi.org/10.1080/24751839.2018.1479932
Chanda, S., Okafor, E., Hamel, S., Stutzmann, D., & Schomaker, L. (2018). Deep Learning for Classification and as Tapped-Feature Generator in Medieval Word-Image Recognition. In 13th IAPR International Workshop on Document Analysis Systems (DAS) (pp. 217-222). IEEE. https://doi.org/10.1109/DAS.2018.82
van de Wolfshaar, J., Wiering, M., & Schomaker, L. (2018). Deep Learning Policy Quantization. In Proceedings of the 10th International Conference on Agents and Artificial Intelligence (pp. 122-130). SciTePress. https://doi.org/10.5220/0006592901220130
Okafor, E., Berendsen, G., Schomaker, L., & Wiering, M. (2018). Detection and Recognition of Badgers Using Deep Learning. In V. Kurkova, Y. Manolopoulos, B. Hammer, L. Iliadis, & I. Maglogiannis (Eds.), International Conference on Artificial Neural Networks (pp. 554-563). (Lecture Notes in Computer Science book series; Vol. 11141). Springer International Publishing, Cham, Switzerland. https://doi.org/10.1007/978-3-030-01424-7_54
Dijkstra, K., van de Loosdrecht, J., Schomaker, L. R. B., & Wiering, M. A. (2018). Hyperspectral demosaicking and crosstalk correction using deep learning. Machine Vision and Applications, 30(1). https://doi.org/10.1007/s00138-018-0965-4
Okafor, E., & Schomaker, L. (2018). Integrated Dimensionality Reduction and Sequence Prediction using LSTM. Poster session presented at ICT.Open, Amersfoort, Netherlands.
He, S., & Schomaker, L. (2018). Open Set Chinese Character Recognition using Multi-typed Attributes. ArXiv. https://arxiv.org/pdf/1808.0899
van Erp, M., Vuurpijl, L., Franke, K., & Schomaker, L. (2018). The wanda measurement tool for forensic document examination. Journal of Forensic Document Examination, 28, 5-14. https://doi.org/10.31974/jfde28-5-14
Weber, A., Ameryan, M., Wolstencroft, K., Stork, L., Heerlien, M., & Schomaker, L. (2018). Towards a Digital Infrastructure for Illustrated Handwritten Archives. In M. Ioannides (Ed.), Lecture Notes in Computer Science, vol. 10605: Final Conference of the Marie Skłodowska-Curie Initial Training Network for Digital Cultural Heritage, ITN-DCH 2017, Olimje, Slovenia (pp. 155-166). Springer. https://doi.org/10.1007/978-3-319-75826-8_13
Chanda, S., Baas, J., Haitink, D., Hamel, S., Stutzmann, D., & Schomaker, L. (2018). Zero-shot learning based approach for medieval word recognition using deep-learned features. In Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR (pp. 345-350). (Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR; Vol. 2018-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICFHR-2018.2018.00067

2017

Dhali, M., He, S., Popovic, M., Tigchelaar, E., & Schomaker, L. (2017). A Digital Palaeographic Approach towards Writer Identification in the Dead Sea Scrolls. In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, 693-702, 2017, Porto, Portugal (pp. 693-702) https://doi.org/10.5220/0006249706930702
He, S., & Schomaker, L. (2017). Beyond OCR: Multi-faceted understanding of handwritten document characteristics. Pattern recognition, 63, 321-333. https://doi.org/10.1016/j.patcog.2016.09.017
Okafor, E., Pawara, P., Karaaba, M., Surinta, O., Codreanu, V., Schomaker, L., & Wiering, M. (2017). Comparative study between deep learning and bag of visual words for wild-animal recognition. In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 (2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2016.7850111
Pawara, P., Okafor, E., Surinta, O., Schomaker, L., & Wiering, M. (2017). Comparing local descriptors and bags of visualwords to deep convolutional neural networks for plant recognition. In A. Fred, M. D. De Marsico, & G. S. di Baja (Eds.), ICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods (pp. 479-486). SciTePress.
Pawara, P., Okafor, E., Surinta, O., Schomaker, L., & Wiering, M. (2017). Comparing Local Descriptors and Bags of Visual Words to Deep Convolutional Neural Networks for Plant Recognition. In 6th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2017) ICPRAM .
He, S., & Schomaker, L. (2017). Co-occurrence features for writer identification. In Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR (pp. 78-83). (Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICFHR.2016.0027
Pawara, P., Okafor, E., Schomaker, L., & Wiering, M. (2017). Data Augmentation for Plant Classification. In Advanced Concepts for Intelligent Vision Systems (Acivs 2017) Article 112
Shantia, A., Bidoia, F., Schomaker, L., & Wiering, M. (2017). Dynamic Parameter Update for Robot Navigation Systems through Unsupervised Environmental Situational Analysis. In IEEE Symposium Series on Computational Intelligence (pp. 1-7). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2016.7850238
Dijkstra, K., van de Loosdrecht, J., Schomaker, L., & Wiering, M. (2017). Hyper-spectral frequency selection for the classification of vegetation diseases. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (2017 ed., pp. 483-488). ESANN.
Okafor, E., Smit, R., Schomaker, L., & Wiering, M. (2017). Operational Data Augmentation in Classifying Single Aerial Images of Animals. In IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), 2017 (pp. 354-360). IEEE. https://doi.org/10.1109/INISTA.2017.8001185
Valentijn, E. A., Begeman, K., Belikov, A., Boxhoorn, D. R., Brinchmann, J., McFarland, J., Holties, H., Kuijken, K. H., Verdoes Kleijn, G., Vriend, W.-J., Williams, O. R., Roerdink, J. B. T. M., Schomaker, L. R. B., Swertz, M. A., Tsyganov, A., & van Dijk, G. J. W. (2017). Target and (Astro-)WISE technologies - Data federations and its applications. In Astroinformatics 2017 (pp. 333-340). (Proceedings IAU Symposium; Vol. 12, issue S325, Astroinformatics). International Astronomical Union. https://doi.org/10.1017/S1743921317000254
He, S., & Schomaker, L. (2017). Writer identification using curvature-free features. Pattern recognition, 63, 451-464. https://doi.org/10.1016/j.patcog.2016.09.044

2016

He, S., Samara, P., Burgers, J., & Schomaker, L. (2016). A Multiple-Label Guided Clustering Algorithm for Historical Document Dating and Localization. Ieee transactions on image processing, 25(11), 5252-5265. https://doi.org/10.1109/TIP.2016.2602078
Bhowmik, T. K., Parui, S. K., Roy, U., & Schomaker, L. (2016). Bangla Handwritten Character Segmentation Using Structural Features: A Supervised and Bootstrapping Approach. ACM Transactions on Asian and Low-Resource Language Information Processing, 15(4), 29:1-29:26. Article 29. https://doi.org/10.1145/2890497
Schomaker, L. (2016). Caveats on Bayesian and hidden-Markov models (v2.8). Manuscript submitted for publication.
Okafor, E., Pawara, P., Karaaba, M., Surinta, O., Codreanu, V., Schomaker, L., & Wiering, M. (2016). Comparative Study Between Deep Learning and Bag of Visual Words for Wild-Animal Recognition. In IEEE Symposium Series on Computational Intelligence IEEE.
He, S., Samara, P., Burgers, J., & Schomaker, L. (2016). Discovering visual element evolutions for historical document dating. In 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) (pp. 7-12). IEEE (The Institute of Electrical and Electronics Engineers).
Schimbinschi, F., Schomaker, L., & Wiering, M. (2016). Ensemble methods for robust 3D face recognition using commodity depth sensors. In IEEE Symposium Series on Computational Intelligence: Symposium on Computational Intelligence in Biometrics and Identity Management (Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015). IEEE (The Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/SSCI.2015.36
He, S., & Schomaker, L. (2016). General Pattern Run-Length Transform for Writer Identification. In Proceedings - 12th IAPR International Workshop on Document Analysis Systems, DAS 2016 (pp. 60-65). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DAS.2016.42
Sriman, B., Schomaker, L., & Pruksasri, P. (2016). General Text-Chunk Localization in Scene Images using a Codebook-based Classifier. In Proceedings of the 4th IIAE International Conference on Intelligent Systems and Image Processing 2016 (pp. 134-141). IEEE. http://www2.ia-engineers.org/icisip2016/?utm_source=researchbib
He, S., Samara, P., Burgers, J., & Schomaker, L. (2016). Historical Document Dating Using Unsupervised Attribute Learning. In 2016 12th IAPR Workshop on Document Analysis Systems (DAS) (pp. 36-44). IEEE (The Institute of Electrical and Electronics Engineers).

2015

Schomaker, L. (2016). Design considerations for a large-scale image-based text search engine in historical manuscript collections. Information Technology, 58(2), 80-88. https://doi.org/10.1515/itit-2015-0049
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