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• W1944207639 abstract "A framework for learning parameterized models of optical flow from image sequences is presented. A class of motions is represented by a set of orthogonal basis flow fields that are computed from a training set using principal component analysis. Many complex image motions can be represented by a linear combination of a small number of these basis flows. The learned motion models may be used for optical flow estimation and for model-based recognition. For optical flow estimation we describe a robust, multi-resolution scheme for directly computing the parameters of the learned flow models from image derivatives. As examples we consider learning motion discontinuities, non-rigid motion of human mouths, and articulated human motion." @default.
• W1944207639 created "2016-06-24" @default.
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• W1944207639 date "2002-11-22" @default.
• W1944207639 modified "2023-10-16" @default.
• W1944207639 title "Learning parameterized models of image motion" @default.
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• W1944207639 doi "https://doi.org/10.1109/cvpr.1997.609381" @default.
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