Moving scene segmentation using median radial basis function network

Various approaches were suggested for simultaneous optical flow estimation and segmentation in image sequences. In this study, the moving scene is decomposed in different regions with respect to their motion, by means of a pattern recognition scheme. The inputs of the proposed scheme are the feature vectors representing still image and motion information. The classifier employed is the Median Radial Basis Function (MRBF) neural network. Each class corresponds to a moving object. An error criterion function derived from the probability estimation theory and related to the moving scene model is used as cost function. Marginal median and median of the absolute deviations estimators are employed for estimating the basis function parameters