A feature-based algorithm for detecting and classifying production effects
Multimedia Systems
View synthesis using stereo vision
View synthesis using stereo vision
A markov random field framework for protein side-chain resonance assignment
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
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We describe a method for computing visual correspondence which employs a formal model of the probability of a false match. This model estimates the chance that the best match for each point could have occurred at random. The model is effective at identifying points in one image for which there is no corresponding point in the other image, as occurs at depth boundaries in stereo and at motion boundaries in optical flow. More generally, the model can be used to identify points where the best match is of poor quality, as occurs in regions of uniform texture. We describe the similarity measure used in the method and present the formal model of a false match. We also show examples of using the method to compute stereo disparity.