Multihypothesis Prior for Segmentation of Stereo Disparity

An iterative segmentation-estimation framework for segmentation of planar surfaces in the disparity space is proposed. Disparity of a scene is modeled by approximating various surfaces in the scene to be planar. Uncertainty in the stereo disparity of these surfaces is modeled with a hidden Markov random field. Planar surface labels are the hidden variables of the model. Surface labels are estimated during the segmentation phase of the framework with help of underlying plane parameters and the spatial coherency prior. After segmentation, the planar surfaces are separated into spatially continuous regions. Each spatially continuous region is treated as a hypothesis for the underlying plane. Hypotheses for each plane are then combined to form a multihypothesis Gaussian mixture prior. The underlying planar surface parameters are estimated with maximum a posteriori estimation from the multihypothesis prior. The iterative process is continued until the labels converge or satisfactory segmentation is achieved. Experimental results are presented with synthetic as well as real-life scenes to demonstrate the success of the proposed method.