Segmentation of epipolar-plane image volumes with occlusion and disocclusion competition

Consider a dense array of cameras uniformly distributed along a line. A solid block of 3D data can be constructed by arranging the images into a stack. This volume, also known as the Epipolar-Plane Image volume, contains highly structured data that can be segmented for object removal, insertion and compression. In this paper, we propose a segmentation scheme that takes fully advantage of the known geometry in order to model occlusions explicitly as a result of disparity. Moreover, we include this knowledge into an energy minimization scheme based on region competition with active contours. Instead of extracting layers sequentially from front to back, each layer is made to compete with the regions it is going to occlude and the ones it is going to disocclude. This enables a virtually unsupervised segmentation.