Discrete Optimization for Computer Vision
At Microsoft Research in Cambridge we are developing new discrete optimization methods for computer vision applications, such as segmentation, matching, etc. Most of the techniques relate to energy minimization in Markov Random Field (MRF) models.
All publications
Recent publications
- Victor Lempitsky, Surface Extraction from Binary Volumes with Higher-Order Smoothness, no. MSR-TR-2009-31, March 2009
- Carsten Rother, Pushmeet Kohli, Wei Feng, and Jiaya Jia, Minimizing Sparse Higher Order Energy Functions of Discrete Variables, in CVPR, 2009
- Lorenzo Torresani, Vladimir Kolmogorov, and carsten Rother, Feature Correspondence via Graph Matching: Models and Global Optimization, in ECCV, October 2008
- Victor S. Lempitsky, Andrew Blake, and Carsten Rother, Image Segmentation by Branch-and-Mincut, in 10th European Conference on Computer Vision - ECCV (4), Springer Verlag, 2008
