Следене
Vittal Premachandran
Vittal Premachandran
Applied Scientist, Amazon Search
Потвърден имейл адрес: amazon.com - Начална страница
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Позовавания
Позовавания
Година
Deep residual learning for instrument segmentation in robotic surgery
D Pakhomov, V Premachandran, M Allan, M Azizian, N Navab
Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019 …, 2019
1472019
Discovering internal representations from object-cnns using population encoding
J Wang, Z Zhang, V Premachandran, A Yuille
arXiv preprint arXiv:1511.06855 2, 2015
60*2015
Consensus of k-nns for robust neighborhood selection on graph-based manifolds
V Premachandran, R Kakarala
Proceedings of the IEEE conference on computer vision and pattern …, 2013
592013
Visual concepts and compositional voting
J Wang, Z Zhang, C Xie, Y Zhou, V Premachandran, J Zhu, L Xie, A Yuille
arXiv preprint arXiv:1711.04451, 2017
532017
Perceptually motivated shape context which uses shape interiors
V Premachandran, R Kakarala
Pattern recognition 46 (8), 2092-2102, 2013
462013
Empirical minimum bayes risk prediction: How to extract an extra few% performance from vision models with just three more parameters
V Premachandran, D Tarlow, D Batra
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2014
352014
Three-dimensional bilateral symmetry plane estimation in the phase domain
R Kakarala, P Kaliamoorthi, V Premachandran
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013
242013
Unsupervised learning using generative adversarial training and clustering
V Premachandran, AL Yuille
202016
Measuring the effectiveness of bad pixel detection algorithms using the ROC curve
V Premachandran, R Kakarala
IEEE Transactions on Consumer Electronics 56 (4), 2511-2519, 2010
142010
Pascal boundaries: A semantic boundary dataset with a deep semantic boundary detector
V Premachandran, B Bonev, X Lian, A Yuille
2017 IEEE Winter Conference on Applications of Computer Vision (WACV), 73-81, 2017
13*2017
Rating image aesthetics using a crowd sourcing approach
A Agrawal, V Premachandran, R Kakarala
Image and Video Technology–PSIVT 2013 Workshops: GCCV 2013, GPID 2013 …, 2014
102014
What parts of a shape are discriminative?
V Premachandran, R Kakarala
2013 IEEE International Conference on Image Processing, 2857-2861, 2013
32013
Empirical minimum Bayes risk prediction
V Premachandran, D Tarlow, AL Yuille, D Batra
IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (1), 75-86, 2016
22016
Can relative skill be determined from a photographic portfolio?
A Agrawal, V Premachandran, R Somavarapu, R Kakarala
Human Vision and Electronic Imaging XVIII 8651, 198-207, 2013
22013
Comparing automated and human ratings of photographic aesthetics
R Kakarala, TS Sachs, V Premachandran
Color and Imaging Conference 19, 217-222, 2011
22011
Dense sampling of shape interiors for improved representation
V Premachandran, R Kakarala
Image Processing: Machine Vision Applications VI 8661, 72-82, 2013
12013
Exploiting shape properties for improved retrieval, discrimination and recognition
V Premachandran
2014
Improving shape context using geodesic information and reflection invariance
V Premachandran, R Kakarala
Intelligent Robots and Computer Vision XXX: Algorithms and Techniques 8662 …, 2013
2013
Can relative skill be determined from a photographic portfolio?
V Premachandran, R Somavarapu, R Kakarala, A Agrawal
2013
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