We describe a method for computing a dense estimate of motion and disparity, given a stereo video sequence containing moving non-rigid objects. In contrast to previous approaches, motion and disparity are estimated simultaneously from a single coherent probabilistic model that correctly accounts for all occlusions, depth discontinuities, and motion discontinuities. The results demonstrate that simultaneous estimation of motion and disparity is superior to estimating either in isolation, and show the promise of the technique for accurate, probabilistically justified, scene analysis. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Isard, M., & MacCormick, J. (2006). Dense motion and disparity estimation via loopy belief propagation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3852 LNCS, pp. 32–41). https://doi.org/10.1007/11612704_4
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