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Pattern recognition, 0031-3203

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1 - 26 out of 26Page size: 50
  1. 2020
  2. 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, [107528]. https://doi.org/10.1016/j.patcog.2020.107528
  3. Babai, M., Kalantar-Nayestanaki, N., Messchendorp, J. G., & Wilkinson, M. H. F. (2020). An efficient attribute-space connected filter on graphs to reconstruct paths in point-clouds. Pattern recognition, 106, [107467]. https://doi.org/10.1016/j.patcog.2020.107467
  4. Talavera Martínez, E., Petkov, N., & Radeva, P. (2020). Topic modelling for routine discovery from egocentric photo-streams. Pattern recognition, 104, [ 107330]. https://doi.org/10.1016/j.patcog.2020.107330
  5. 2019
  6. Roerdink, J. B. T. M. (2019). Corrigendum: Group morphology” [Pattern Recognition 33(6) (2000) 877–895]. Pattern recognition, 96, [106978]. https://doi.org/10.1016/j.patcog.2019.106978
  7. Strisciuglio, N., Vento, M., & Petkov, N. (2019). Learning representations of sound using trainable COPE feature extractors. Pattern recognition, 92, 25-36. https://doi.org/10.1016/j.patcog.2019.03.016
  8. 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
  9. 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
  10. 2017
  11. 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
  12. 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
  13. 2016
  14. He, S., Samara, P., Burgers, J., & Schomaker, L. (2016). Image-based historical manuscript dating using contour and stroke fragments. Pattern recognition, 58, 159-171. https://doi.org/10.1016/j.patcog.2016.03.032
  15. Kiwanuka, F. N., & Wilkinson, M. H. F. (2016). Automatic attribute threshold selection for morphological connected attribute filters. Pattern recognition, 53, 59-72. https://doi.org/10.1016/j.patcog.2015.11.012
  16. 2015
  17. He, S., Wiering, M., & Schomaker, L. (2015). Junction detection in handwritten documents and its application to writer identification. Pattern recognition, 48(12), 4036-4048. https://doi.org/10.1016/j.patcog.2015.05.022
  18. 2014
  19. van Oosten, J-P., & Schomaker, L. (2014). Separability versus prototypicality in handwritten word-image retrieval. Pattern recognition, 47(3), 1031-1038. https://doi.org/10.1016/j.patcog.2013.09.006
  20. 2012
  21. Brink, A. A., Smit, J., Bulacu, M. L., & Schomaker, L. R. B. (2012). Writer identification using directional ink-trace width measurements. Pattern recognition, 45(1), 162-171. https://doi.org/10.1016/j.patcog.2011.07.005
  22. 2011
  23. 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. https://doi.org/10.1016/j.patcog.2010.08.013
  24. Petkov, N., & Jiang, X. (2011). CAIP—Computer Analysis of Images and Patterns. Pattern recognition, 44(9), 1841-1841. https://doi.org/10.1016/j.patcog.2011.03.017
  25. 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. https://doi.org/10.1016/j.patcog.2010.10.024
  26. 2010
  27. Ouzounis, G. K., & Wilkinson, M. H. F. (2010). Partition-induced connections and operators for pattern analysis. Pattern recognition, 43(10), 3193-3207. https://doi.org/10.1016/j.patcog.2009.10.002
  28. Abdullah, A., Veltkamp, R. C., & Wiering, M. A. (2010). Fixed partitioning and salient points with MPEG-7 cluster correlograms for image categorization. Pattern recognition, 43(3), 650-662. https://doi.org/10.1016/j.patcog.2009.09.007
  29. 2004
  30. 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. https://doi.org/10.1016/j.patcog.2003.09.009
  31. 2001
  32. Barshan, B., & Baskent, D. (2001). Morphological surface profile extraction with multiple range sensors. Pattern recognition, 34(7), 1459-1467. https://doi.org/10.1016/S0031-3203(00)00083-2
  33. 2000
  34. Roerdink, J. B. T. M. (2000). Group morphology. Pattern recognition, 33(6), 877-895. https://doi.org/10.1016/S0031-3203(99)00152-1
  35. Tuzikov, A. V., Roerdink, J. B. T. M., & Heijmans, H. J. A. M. (2000). Similarity measures for convex polyhedra based on Minkowski addition. Pattern recognition, 33(6), 979-995.
  36. 1993
  37. SCHOMAKER, L. (1993). USING STROKE-BASED OR CHARACTER-BASED SELF-ORGANIZING MAPS IN THE RECOGNITION OF ONLINE, CONNECTED CURSIVE SCRIPT. Pattern recognition, 26(3), 443-450.
  38. 1983
  39. Landeweerd, G. H., Timmers, T., Gelsema, E. S., Bins, M., & Halie, M. R. (1983). Binary tree versus single level tree classification of white blood cells. Pattern recognition, 16(6), 571-577. https://doi.org/10.1016/0031-3203(83)90073-0
  40. Landeweerd, G. H., Timmers, T., Gelsema, E. S., Bins, M., & Halie, M. R. (1983). Classification of normal and abnormal samples of peripheral blood by linear mapping of the feature space. Pattern recognition, 16(3), 319-326. https://doi.org/10.1016/0031-3203(83)90037-7

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