Motion-field segmentation using an adaptive MAP criterion

The authors propose a general formulation for adaptive, maximum a posteriori probability (MAP) segmentation of image sequences on the basis of interframe displacement and gray level information. The segmentation classifies pixel sites to independently moving objects in the scene. In this formulation, two methods for characterizing the conditional probability distribution of the data given the segmentation process are proposed. The a priori probability distribution is characterized on the basis of a Gibbsian model of the segmentation process, where a novel motion-compensated spatiotemporal neighborhood system is defined. The proposed formulation adapts to the displacement field accuracy by appropriately adjusting the relative emphasis on the estimated displacement field, gray level information, and prior knowledge implied by the Gibbsian model. Experiments have been performed with a five-frame simulated sequence containing translation and rotation.<>