A genetic aggregate stereo algorithm for 3-D classification of occluded shapes
In computer vision, various stereo algorithms have been developed to determine the shape depth from a pair of images for that shape. To find the depth traditionally, one has to solve the point-to-point correspondence problem. By nature, this problem is complicated and time consuming. In this paper, a genetic algorithm has been exploited to obtain a depth estimation for the underlying shape without using any point-to-point correspondence. Instead, correspondence of the outer contours as a whole is required. Consequently, the three-dimensional (3-D) moments as well as a chain code, could be computed and relied upon as shape invariant features. Such features are used as input to a next stage that consists of a neural network classifier. Combining the robustness of neural network classifiers with the robustness of genetic algorithms has led to a fairly robust pattern recognition system that can tolerate high degrees of noise and occlusion levels. Eventually, a dynamic alignment procedure is attached to the classifier outputs to make the proposed shape representation independent of the starting point.
genetic aggregate stereo aggregate stereo genetic algorithm depth estimation neural network classifier dynamic alignment stereo disparity partial occlusion counter-propagation 3-d moments