| 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 | 161 | 2019 |
| Discovering internal representations from object-cnns using population encoding J Wang, Z Zhang, V Premachandran, A Yuille arXiv preprint arXiv:1511.06855 2, 2015 | 65* | 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 | 65 | 2013 |
| 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 | 55 | 2017 |
| Perceptually motivated shape context which uses shape interiors V Premachandran, R Kakarala Pattern recognition 46 (8), 2092-2102, 2013 | 47 | 2013 |
| 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 | 35 | 2014 |
| 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 | 26 | 2013 |
| Unsupervised learning using generative adversarial training and clustering V Premachandran, AL Yuille | 23 | 2016 |
| 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 | 17 | 2010 |
| 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 | 14* | 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 | 14 | 2014 |
| What parts of a shape are discriminative? V Premachandran, R Kakarala 2013 IEEE International Conference on Image Processing, 2857-2861, 2013 | 3 | 2013 |
| 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 | 2 | 2016 |
| 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 | 2 | 2013 |
| Comparing automated and human ratings of photographic aesthetics R Kakarala, TS Sachs, V Premachandran Color and Imaging Conference 19, 217-222, 2011 | 2 | 2011 |
| Dense sampling of shape interiors for improved representation V Premachandran, R Kakarala Image Processing: Machine Vision Applications VI 8661, 72-82, 2013 | 1 | 2013 |
| 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 |
Ramakrishna KakaralaOmnivision Technologies, IncDirección de correo verificada de ieee.org