| Region covariance: A fast descriptor for detection and classification O Tuzel, F Porikli, P Meer European conference on computer vision (ECCV), 589-600, 2006 | 1643 | 2006 |
| Learning from Simulated and Unsupervised Images through Adversarial Training. A Shrivastava, T Pfister, O Tuzel, J Susskind, W Wang, R Webb IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2 (4), 5, 2017 | 1485 | 2017 |
| Voxelnet: End-to-end learning for point cloud based 3d object detection Y Zhou, O Tuzel IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2018 | 1356 | 2018 |
| Coupled generative adversarial networks MY Liu, O Tuzel Advances in neural information processing systems (NIPS), 469-477, 2016 | 1269 | 2016 |
| Pedestrian detection via classification on riemannian manifolds O Tuzel, F Porikli, P Meer IEEE transactions on pattern analysis and machine intelligence 30 (10), 1713 …, 2008 | 1091 | 2008 |
| Entropy rate superpixel segmentation MY Liu, O Tuzel, S Ramalingam, R Chellappa IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2097-2104, 2011 | 878 | 2011 |
| Covariance tracking using model update based on lie algebra F Porikli, O Tuzel, P Meer IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 728-735, 2006 | 822* | 2006 |
| Human Detection via Classification on Riemannian Manifolds. O Tuzel, F Porikli, P Meer IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 1 (2), 4, 2007 | 638 | 2007 |
| A Multi-Stream Bi-Directional Recurrent Neural Network for Fine-Grained Action Detection B Singh, M Jones, T Marks, O Tuzel, M Shao IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2016 | 374 | 2016 |
| Fast directional chamfer matching MY Liu, O Tuzel, A Veeraraghavan, R Chellappa IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1696-1703, 2010 | 243 | 2010 |
| Joint geodesic upsampling of depth images MY Liu, O Tuzel, Y Taguchi IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 169-176, 2013 | 216 | 2013 |
| Human body tracking by adaptive background models and mean-shift analysis F Porikli, O Tuzel IEEE International Workshop on Performance Evaluation of Tracking and …, 2003 | 181 | 2003 |
| Voting-based pose estimation for robotic assembly using a 3D sensor C Choi, Y Taguchi, O Tuzel, MY Liu, S Ramalingam 2012 IEEE International Conference on Robotics and Automation (ICRA), 1724-1731, 2012 | 170 | 2012 |
| Fast object localization and pose estimation in heavy clutter for robotic bin picking MY Liu, O Tuzel, A Veeraraghavan, Y Taguchi, TK Marks, R Chellappa The International Journal of Robotics Research 31 (8), 951-973, 2012 | 163 | 2012 |
| Semi-supervised kernel mean shift clustering S Anand, S Mittal, O Tuzel, P Meer IEEE transactions on pattern analysis and machine intelligence 36 (6), 1201-1215, 2013 | 137 | 2013 |
| Gaussian Conditional Random Field Network for Semantic Segmentation R Vemulapalli, O Tuzel, MY Liu, R Chellappa IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2016 | 134 | 2016 |
| R-CNN for small object detection C Chen, MY Liu, O Tuzel, J Xiao Asian conference on computer vision, 214-230, 2016 | 133 | 2016 |
| Deep Hierarchical Parsing for Semantic Segmentation A Sharma, O Tuzel, DW Jacobs Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on, 2015 | 121 | 2015 |
| Learning on lie groups for invariant detection and tracking. O Tuzel, FM Porikli, P Meer IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2 (5), 8, 2008 | 110 | 2008 |
| A bayesian approach to background modeling O Tuzel, F Porikli, P Meer 2005 IEEE Computer Society Conference on Computer Vision and Pattern …, 2005 | 107 | 2005 |
Fatih PorikliQualcomm, Australian National UniversityVerified email at anu.edu.au
