Abstract

. Motion segmentation involves identifying regions of the image that correspond to independently moving objects. The number of independently moving objects, and type of motion model for each of the objects is unknown a priori. In order to perform motion segmentation, the problems of model selection, robust estimation and clustering must all be addressed simultaneously. Here we place the three problems into a common Bayesian framework; investigating the use of model averaging-representing a motion by a combination of models---as a principled way for motion segmentation of images. The final result is a fully automatic algorithm for clustering that works in the presence of noise and outliers. 1 Introduction Detection of independently moving objects is an essential but often neglected precursor to problems in computer vision e.g. e#cient video compression [3], video editing, surveillance, smart tracking of objects etc. The work in this paper stems from the desire to develop a g...

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