| A comparative study of energy minimization methods for markov random fields with smoothness-based priors R Szeliski, R Zabih, D Scharstein, O Veksler, V Kolmogorov, A Agarwala, ... IEEE transactions on pattern analysis and machine intelligence 30 (6), 1068-1080, 2008 | 1357 | 2008 |
| Sparse convolutional neural networks B Liu, M Wang, H Foroosh, M Tappen, M Pensky Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 1057 | 2015 |
| Comparison of graph cuts with belief propagation for stereo, using identical MRF parameters Tappen Proceedings Ninth IEEE International Conference on Computer Vision, 900-906 …, 2003 | 683 | 2003 |
| Recovering intrinsic images from a single image M Tappen, W Freeman, E Adelson Advances in neural information processing systems 15, 2002 | 632 | 2002 |
| A comparative study of energy minimization methods for markov random fields R Szeliski, R Zabih, D Scharstein, O Veksler, V Kolmogorov, A Agarwala, ... Computer Vision–ECCV 2006: 9th European Conference on Computer Vision, Graz …, 2006 | 539 | 2006 |
| Learning to recognize shadows in monochromatic natural images J Zhu, KGG Samuel, SZ Masood, MF Tappen 2010 IEEE Computer Society conference on computer vision and pattern …, 2010 | 320 | 2010 |
| Exploring the trade-off between accuracy and observational latency in action recognition C Ellis, SZ Masood, MF Tappen, JJ LaViola, R Sukthankar International Journal of Computer Vision 101, 420-436, 2013 | 311 | 2013 |
| Exploiting the sparse derivative prior for super-resolution and image demosaicing M Russell, WT Freeman Proceedings of the Third International Workshop Statistical and …, 2003 | 233 | 2003 |
| Learning gaussian conditional random fields for low-level vision MF Tappen, C Liu, EH Adelson, WT Freeman 2007 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2007 | 220 | 2007 |
| Learning pedestrian dynamics from the real world P Scovanner, MF Tappen 2009 IEEE 12th International Conference on Computer Vision, 381-388, 2009 | 191 | 2009 |
| Context-constrained hallucination for image super-resolution J Sun, J Zhu, MF Tappen 2010 IEEE Computer Society Conference on Computer Vision and Pattern …, 2010 | 145 | 2010 |
| Estimating intrinsic component images using non-linear regression MF Tappen, EH Adelson, WT Freeman 2006 IEEE Computer Society Conference on Computer Vision and Pattern …, 2006 | 135 | 2006 |
| Learning optimized MAP estimates in continuously-valued MRF models KGG Samuel, MF Tappen 2009 IEEE Conference on Computer Vision and Pattern Recognition, 477-484, 2009 | 124 | 2009 |
| Probabilistic label trees for efficient large scale image classification B Liu, F Sadeghi, M Tappen, O Shamir, C Liu Proceedings of the IEEE conference on computer vision and pattern …, 2013 | 117 | 2013 |
| Latent pyramidal regions for recognizing scenes F Sadeghi, MF Tappen Computer Vision–ECCV 2012: 12th European Conference on Computer Vision …, 2012 | 105 | 2012 |
| A bayesian approach to alignment-based image hallucination MF Tappen, C Liu Computer Vision–ECCV 2012: 12th European Conference on Computer Vision …, 2012 | 93 | 2012 |
| Utilizing variational optimization to learn markov random fields MF Tappen 2007 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2007 | 93 | 2007 |
| Learning non-local range Markov random field for image restoration J Sun, MF Tappen CVPR 2011, 2745-2752, 2011 | 76 | 2011 |
| Efficient graphical models for processing images MF Tappen, BC Russell, WT Freeman Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision …, 2004 | 66 | 2004 |
| Learning-based shadow recognition and removal from monochromatic natural images M Xu, J Zhu, P Lv, B Zhou, MF Tappen, R Ji IEEE Transactions on Image Processing 26 (12), 5811-5824, 2017 | 65 | 2017 |
