The MPM–MAP algorithm for motion segmentation

We present an MPM–MAP application for the efficient estimation of piecewise parametric models for motion segmentation. This algorithm permits the simultaneous estimation of: the number of models, the parameters for each model and the regions where each model is applicable. It is based on Bayesian estimation theory, and is theoretically justified by the use of a specific cost function whose expected value decreases at every iteration and by a new model for the posterior marginal distributions which is amenable to the use of fast computational methods. We compare the performance of this method with the most similar segmentation algorithm, the well known Expectation-Maximization algorithm. We present a comparison of the performance of both algorithms using synthetic and real image sequences.