A connectome of a learning and memory center in the adult Drosophila brain S Takemura, Y Aso, T Hige, A Wong, Z Lu, CS Xu, PK Rivlin, H Hess, ... Elife 6, e26975, 2017 | 359 | 2017 |
Synaptic circuits and their variations within different columns in the visual system of Drosophila S Takemura, CS Xu, Z Lu, PK Rivlin, T Parag, DJ Olbris, S Plaza, T Zhao, ... Proceedings of the National Academy of Sciences 112 (44), 13711-13716, 2015 | 260 | 2015 |
Machine learning of hierarchical clustering to segment 2D and 3D images J Nunez-Iglesias, R Kennedy, T Parag, J Shi, DB Chklovskii PloS one 8 (8), e71715, 2013 | 161 | 2013 |
Comparisons between the ON- and OFF-edge motion pathways in the Drosophila brain K Shinomiya, G Huang, Z Lu, T Parag, CS Xu, R Aniceto, N Ansari, ... Elife 8, e40025, 2019 | 129 | 2019 |
A framework for feature selection for background subtraction T Parag, A Elgammal, A Mittal Computer Vision and Pattern Recognition 2006. CVPR 2006 IEEE Conference on …, 2006 | 114 | 2006 |
Boosting adaptive linear weak classifiers for online learning and tracking T Parag, F Porikli, A Elgammal 2008 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008 | 79 | 2008 |
Predicate logic based image grammars for complex visual pattern recognition VD Shet, MK Singh, C Bahlmann, V Ramesh, SP Masticola, J Neumann, ... US Patent 8,548,231, 2013 | 76 | 2013 |
Videossl: Semi-supervised learning for video classification L Jing, T Parag, Z Wu, Y Tian, H Wang Proceedings of the IEEE/CVF winter conference on applications of computer …, 2021 | 63 | 2021 |
A Context-Aware Delayed Agglomeration Framework for Electron Microscopy Segmentation T Parag, A Chakraborty, S Plaza, L Scheffer PLoS ONE 10 (5), e0125825, 2015 | 41 | 2015 |
A Context-aware Delayed Agglomeration Framework for Electron Microscopy Segmentation T Parag, A Chakraborty, S Plaza, L Scheffer arXiv preprint arXiv:1406.1476, 2014 | 41 | 2014 |
Scalable interactive visualization for connectomics D Haehn, J Hoffer, B Matejek, A Suissa-Peleg, AK Al-Awami, L Kamentsky, ... Informatics 4 (3), 29, 2017 | 29 | 2017 |
Small sample learning of superpixel classifiers for EM segmentation T Parag, S Plaza, L Scheffer Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th …, 2014 | 26 | 2014 |
Parallel Separable 3D Convolution for Video and Volumetric Data Understanding F Gonda, D Wei, T Parag, H Pfister British Machine Vision Conference (BMVC), 2018, 2018 | 25 | 2018 |
Morphological error detection in 3D segmentations D Rolnick, Y Meirovitch, T Parag, H Pfister, V Jain, JW Lichtman, ... arXiv preprint arXiv:1705.10882, 2017 | 25 | 2017 |
Annotating synapses in large EM datasets SM Plaza, T Parag, GB Huang, DJ Olbris, MA Saunders, PK Rivlin arXiv preprint arXiv:1409.1801, 2014 | 25 | 2014 |
Biologically-constrained graphs for global connectomics reconstruction B Matejek, D Haehn, H Zhu, D Wei, T Parag, H Pfister Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 23 | 2019 |
Method for adaptively boosting classifiers for object tracking FM Porikli, T Parag US Patent 7,840,061, 2010 | 19 | 2010 |
Two stream active query suggestion for active learning in connectomics Z Lin, D Wei, WD Jang, S Zhou, X Chen, X Wang, R Schalek, D Berger, ... Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 17 | 2020 |
Efficient classifier training to minimize false merges in electron microscopy segmentation T Parag, DC Ciresan, A Giusti Proceedings of the IEEE International Conference on Computer Vision, 657-665, 2015 | 17 | 2015 |
Detecting Synapse Location and Connectivity by Signed Proximity Estimation and Pruning with Deep Nets T Parag, D Berger, L Kamentsky, B Staffler, D Wei, M Helmstaedter, ... arXiv preprint arXiv:1807.02739, 2018 | 15 | 2018 |