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2004
IEEE

Efficient Belief Propagation for Early Vision

9 years 8 months ago
Efficient Belief Propagation for Early Vision
Markov random field models provide a robust and unified framework for early vision problems such as stereo, optical flow and image restoration. Inference algorithms based on graph cuts and belief propagation yield accurate results, but despite recent advances are often still too slow for practical use. In this paper we present new algorithmic techniques that substantially improve the running time of the belief propagation approach. One of our techniques reduces the complexity of the inference algorithm to be linear rather than quadratic in the number of possible labels for each pixel, which is important for problems such as optical flow or image restoration that have a large label set. A second technique makes it possible to obtain good results with a small fixed number of message passing iterations, independent of the size of the input images. Taken together these techniques speed up the standard algorithm by several orders of magnitude. In practice we obtain stereo, optical flow and...
Pedro F. Felzenszwalb, Daniel P. Huttenlocher
Added 12 Oct 2009
Updated 29 Oct 2009
Type Conference
Year 2004
Where CVPR
Authors Pedro F. Felzenszwalb, Daniel P. Huttenlocher
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