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2000
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A New Bayesian Relaxation Framework for the Estimation and Segmentation of Multiple Motions

9 years 11 months ago
A New Bayesian Relaxation Framework for the Estimation and Segmentation of Multiple Motions
In this paper we propose a new probabilistic relaxation framework to perform robust multiple motion estimation and segmentation from a sequence of images. Our approach uses displacement information obtained from tracked features or raw sparse optical flow to iteratively estimate multiple motion models. Each iteration consists of a segmentation and a motion parameter estimation step. The motion models are used to compute probability density functions for all displacement vectors. Based on the estimated probabilities a pixel-wise segmentation decision is made by a Bayesian classifier, which is optimal in respect to minimum error. The updated segmentation then relaxes the motion parameter estimates. These two steps are iterated until the error of the fitted models is minimized. The Bayesian formulation provides a unified probabilistic framework for various motion models and induces inherent robustness through its rejection mechanism. An implementation of the proposed framework using ...
Alexander Strehl, Jake K. Aggarwal
Added 01 Aug 2010
Updated 01 Aug 2010
Type Conference
Year 2000
Where SSIAI
Authors Alexander Strehl, Jake K. Aggarwal
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