Concerning Bayesian Motion Segmentation, Model, Averaging, Matching and the Trifocal Tensor

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.

• Type: Conference
• Year: 1998
• Where: ECCV
• Authors: Philip H. S. Torr, Andrew Zisserman
• Keywords: Common Bayesian Framework, Computer Vision, ECCV 1998, Model Selection, Motion Model, Motion Segmentation