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We develop a method for learning the spatial statistics of optical flow fields from a novel training database. Training flow fields are constructed using range images of natural scenes and 3D camera motions recovered from handheld and car-mounted video sequences. A detailed analysis of optical flow statistics in natural scenes is presented and machine learning methods are developed to learn a Markov random field model of optical flow. The prior probability of a flow field is formulated as adoi:10.1109/iccv.2005.180 dblp:conf/iccv/RothB05 fatcat:jyhmkvydabfkxkgstg4snp57o4