Proceedings PaperUncertainty reduction: a framework for the integration of visual motion
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Motion uncertainty arises whenever there is ambiguity in local velocity vector assignment, such as along a straight contour or in a textureless region. Motion uncertainty can be quantified by computing the entropy of the corresponding velocity probability distribution. We propose a new framework for the integration of visual motion where the objective is the reduction of motion uncertainty. Based on this approach, we have developed a model that searches for the proper extent of motion integration in order to minimize motion entropy. By modeling our task as a multi-stage stochastic optimization problem, the control structure for motion integration can be inferred through dynamic programming. Results from initial experiments demonstrate that our model is capable of analyzing image sequences containing the aperture problem and textureless regions.