| Time‐of‐flight cameras in computer graphics A Kolb, E Barth, R Koch, R Larsen Computer graphics forum 29 (1), 141-159, 2010 | 772 | 2010 |
| A multi-organ nucleus segmentation challenge N Kumar, R Verma, D Anand, Y Zhou, OF Onder, E Tsougenis, H Chen, ... IEEE transactions on medical imaging 39 (5), 1380-1391, 2019 | 618 | 2019 |
| Variability of eye movements when viewing dynamic natural scenes M Dorr, T Martinetz, KR Gegenfurtner, E Barth Journal of vision 10 (10), 28-28, 2010 | 607 | 2010 |
| Accurate eye centre localisation by means of gradients. F Timm, E Barth Visapp 11, 125-130, 2011 | 483 | 2011 |
| A hybrid convolutional variational autoencoder for text generation S Semeniuta, A Severyn, E Barth arXiv preprint arXiv:1702.02390, 2017 | 336 | 2017 |
| How honeybees make grazing landings on flat surfaces MV Srinivasan, SW Zhang, JS Chahl, E Barth, S Venkatesh Biological cybernetics 83 (3), 171-183, 2000 | 314 | 2000 |
| Recurrent dropout without memory loss S Semeniuta, A Severyn, E Barth arXiv preprint arXiv:1603.05118, 2016 | 307 | 2016 |
| Fundamental limits of linear filters in the visual processing of two-dimensional signals C Zetzsche, E Barth Vision research 30 (7), 1111-1117, 1990 | 276 | 1990 |
| Explainable COVID-19 detection using chest CT scans and deep learning H Alshazly, C Linse, E Barth, T Martinetz Sensors 21 (2), 455, 2021 | 243 | 2021 |
| Deep convolutional neural networks as generic feature extractors L Hertel, E Barth, T Käster, T Martinetz 2015 International Joint Conference on Neural Networks (IJCNN), 1-4, 2015 | 154 | 2015 |
| Simple method for high-performance digit recognition based on sparse coding K Labusch, E Barth, T Martinetz IEEE Transactions on Neural Networks 19, 1985-1989, 2008 | 136 | 2008 |
| Low-level phenomenal vision despite unilateral destruction of primary visual cortex P Stoerig, E Barth Consciousness and cognition 10 (4), 574-587, 2001 | 133 | 2001 |
| The importance of intrinsically two-dimensional image features in biological vision and picture coding C Zetzsche, E Barth, B Wegmann Digital images and human vision, 109-138, 1993 | 123 | 1993 |
| ToF-sensors: New dimensions for realism and interactivity A Kolb, E Barth, R Koch 2008 IEEE Computer Society Conference on Computer Vision and Pattern …, 2008 | 107 | 2008 |
| Analysis of superimposed oriented patterns T Aach, C Mota, I Stuke, M Muhlich, E Barth IEEE Transactions on Image Processing 15 (12), 3690-3700, 2006 | 107* | 2006 |
| Ensembles of deep learning models and transfer learning for ear recognition H Alshazly, C Linse, E Barth, T Martinetz Sensors 19 (19), 4139, 2019 | 99 | 2019 |
| Automated indirect immunofluorescence evaluation of antinuclear autoantibodies on HEp‐2 cells J Voigt, C Krause, E Rohwäder, S Saschenbrecker, M Hahn, ... Journal of Immunology Research 2012 (1), 651058, 2012 | 96 | 2012 |
| Image encoding, labeling, and reconstruction from differential geometry E Barth, T Caelli, C Zetzsche CVGIP: Graphical models and image processing 55 (6), 428-446, 1993 | 94 | 1993 |
| Non-parametric texture defect detection using Weibull features F Timm, E Barth Image Processing: Machine Vision Applications IV 7877, 150-161, 2011 | 90 | 2011 |
| Sparse coding neural gas: learning of overcomplete data representations K Labusch, E Barth, T Martinetz Neurocomputing 72 (7-9), 1547-1555, 2009 | 85 | 2009 |
Thomas MartinetzProfessor of Computer Science, University of LübeckVerified email at inb.uni-luebeck.de
