Ninad S. Thakoor, Ph.D.
Affiliations: | 2009 | Electrical Engineering | University of Texas at Arlington, Arlington, TX, United States |
Area:
Electronics and Electrical Engineering, Computer ScienceGoogle:
"Ninad Thakoor"Parents
Sign in to add mentorVenkat Devarajan | grad student | 2009 | UT Arlington | |
(Dynamic scene interpretation and understanding from two views.) |
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Publications
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Theagarajan R, Thakoor NS, Bhanu B. (2020) Physical Features and Deep Learning-based Appearance Features for Vehicle Classification from Rear View Videos Ieee Transactions On Intelligent Transportation Systems. 21: 1096-1108 |
Yang S, An L, Lei Y, et al. (2017) A dense flow-based framework for real-time object registration under compound motion Pattern Recognition. 63: 279-290 |
Nair SKR, Bhanu B, Ghosh S, et al. (2014) Predictive models for multibiometric systems Pattern Recognition. 47: 3779-3792 |
Thakoor NS, Bhanu B. (2013) Structural signatures for passenger vehicle classification in video Ieee Transactions On Intelligent Transportation Systems. 14: 1796-1805 |
Wang R, Bhanu B, Thakoor NS. (2013) Learning small gallery size for prediction of recognition performance on large populations Pattern Recognition. 46: 3533-3547 |
Thakoor N, Gao J. (2011) Branch-and-Bound for Model Selection and Its Computational Complexity Ieee Transactions On Knowledge and Data Engineering. 23: 655-668 |
Kumar RS, Bhanu B, Ghosh S, et al. (2011) Prediction and validation of indexing performance for biometrics International Journal of Central Banking. 1-6 |
Thakoor N, Gao J, Devarajan V. (2010) Multibody structure-and-motion segmentation by branch-and-bound model selection. Ieee Transactions On Image Processing : a Publication of the Ieee Signal Processing Society. 19: 1393-402 |
Thakoor N, Gao J, Devarajan V. (2008) Multistage branch-and-bound merging for planar surface segmentation in disparity space. Ieee Transactions On Image Processing : a Publication of the Ieee Signal Processing Society. 17: 2217-26 |
Gopinath S, Wen Q, Thakoor N, et al. (2008) A statistical approach for intensity loss compensation of confocal microscopy images. Journal of Microscopy. 230: 143-159 |