Bayesian selection of scaling laws for motion modeling in images

Based on scaling laws describing the statistical structure of turbulent motion across scales, we propose a multiscale and non-parametric regularizer for optic-flow estimation. Regularization is achieved by constraining motion increments to behave through scales as the most likely selfsimilar process given some image data. In a first level of inference, the hard constrained minimization problem is optimally solved by taking advantage of lagrangian duality. It results in a collection of first-order regularizers acting at different scales. This estimation is non-parametric since the optimal regularization parameters at the different scales are obtained by solving the dual problem. In a second level of inference, the most likely self-similar model given the data is optimally selected by maximization of Bayesian evidence. The motion estimator accuracy is first evaluated on a synthetic image sequence of simulated bi-dimensional turbulence and then on a real meteorological ima...

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Patrick H´eas, Etienne M´emin, Dominique Heitz, Pablo D. Mininni
• Keywords: Bayesian Selection, Computer Vision, ICCV 2009