GEOMETRIC MOTION SEGMENTATION AND MODEL SELECTION

Motion segmentation involves clustering features together that belong to independently moving objects. The image features on each of these o bjects conform to one of several putative motion models, but the numbe r and type of motion is unknown a priori. In order to cluster these fe atures, the problems of model selection, robust estimation and cluster ing must all be addressed simultaneously. Within this paper I place th e three problems into a common statistical framework; investigating th e use of information criteria and robust mixture models as a principle d way for motion segmentation of images. The final result is a general fully automatic algorithm for clustering that works in the presence o f noise and outliers.