Optimization Algorithms for Discrete Markov Random Fields, with Applications to Computer Vision (2006)
Abstract / truncated to 115 words
A large variety of important tasks in low-level vision, image analysis and pattern recognition can be formulated as discrete labeling problems where one seeks to optimize some measure of the quality of the labeling. For example such is the case in optical flow estimation, stereo matching, image restoration to mention only a few of them. Discrete Markov Random Fields are ideal candidates for modeling these labeling problems and, for this reason, they are ubiquitous in computer vision. Therefore, an issue of paramount importance, that has attracted a significant amount of computer vision research over the past years, is how to optimize discrete Markov Random Fields efficiently and accurately. The main theme of this thesis is ...
Information
- Author
- Komodakis, Nikos
- Institution
- University of Crete
- Supervisor
- Publication Year
- 2006
- Upload Date
- Dec. 12, 2008
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