Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiments in the machine interpretation of visual motion
Experiments in the machine interpretation of visual motion
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We 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 our formulation, we propose two methods for characterizing the conditional probability distribution of the data given the segmentation process. 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.