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- W189822683 abstract "We address the problem of maximum a posteriori (MAP) estimation of optical flow with a geometric prior from gray-value images. We estimate simultaneously the optical flow and the corresponding surface --- the structural optical flow (SOF) --- subject to three types of constraints: intensity constancy, geometric, and smoothness constraints. Our smoothness constraints restrict the unknowns to locally coincide with a set of finitely parameterized admissible functions. The geometric constraints locally enforce consistency between the optical flow and the corresponding surface. Our theory amounts to a discrete generalization of regularization defined in terms of partial derivatives. The point-wise regularizers are efficiently implemented with linear run-time complexity in the number of discretization points. We demonstrate the applicability of our method by example computations of SOF from photographs of human faces." @default.
- W189822683 created "2016-06-24" @default.
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- W189822683 date "2012-01-01" @default.
- W189822683 modified "2023-09-25" @default.
- W189822683 title "Approximate Regularization for Structural Optical Flow Estimation" @default.
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- W189822683 doi "https://doi.org/10.1007/978-3-642-33140-4_30" @default.
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