Alphabet SOUP: A Framework for Approximate Energy Minimization
Many problems in computer vision can be modeled using conditional Markov random fields (CRF). Since finding the maximum a posteriori (MAP) solution in such models is NP-hard, much attention in recent years has been placed on finding good approximate solutions. In particular, graph-cut based algorithms, such as α-expansion, are tremendously successful at solving problems with regular potentials. However, for arbitrary energy functions, message passing algorithms, such as max-product belief...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceeings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009)|
|01_Gould_Alphabet_SOUP:_A_Framework_for_2009.pdf||779.23 kB||Adobe PDF||Request a copy|
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