Seuraa
Toufiq Parag
Toufiq Parag
Harvard University
Vahvistettu sähköpostiosoite verkkotunnuksessa g.harvard.edu - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
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
3352017
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
2402015
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
1552013
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
1172006
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
1162019
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
772008
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
712013
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
522021
A Context-Aware Delayed Agglomeration Framework for Electron Microscopy Segmentation
T Parag, A Chakraborty, S Plaza, L Scheffer
PLoS ONE 10 (5), e0125825, 2015
402015
A Context-aware Delayed Agglomeration Framework for Electron Microscopy Segmentation
T Parag, A Chakraborty, S Plaza, L Scheffer
arXiv preprint arXiv:1406.1476, 2014
402014
Scalable interactive visualization for connectomics
D Haehn, J Hoffer, B Matejek, A Suissa-Peleg, AK Al-Awami, L Kamentsky, ...
Informatics 4 (3), 29, 2017
272017
Morphological error detection in 3d segmentations
D Rolnick, Y Meirovitch, T Parag, H Pfister, V Jain, JW Lichtman, ...
arXiv preprint arXiv:1705.10882, 2017
252017
Annotating synapses in large EM datasets
SM Plaza, T Parag, GB Huang, DJ Olbris, MA Saunders, PK Rivlin
arXiv preprint arXiv:1409.1801, 2014
242014
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
232019
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
212014
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
202018
Method for adaptively boosting classifiers for object tracking
FM Porikli, T Parag
US Patent 7,840,061, 2010
192010
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
172015
Coupled label and intensity MRF models for IR target detection
T Parag
CVPR 2011 WORKSHOPS, 7-13, 2011
162011
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
152018
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