An efficient pattern recognition system based on soft computing concepts has been developed. A new reliable genetic stereo vision algorithm is used in order to estimate depth of objects without using any point-to-point correspondence. Instead, correspondence of the contours as a whole is required. Invariant breakpoints are located on a shape contour using the colinearity principle. Thus, a localized representation of a shape contour including 3-D moments as well as a chain code can be obtained. This representation is invariant to rotation, translation, scale, and starting point. The system is provided with a neural network classifier and a dynamic alignment procedure at its output. Combing the robustness of neural network classifier with the genetic algorithm capability results in a reliable pattern recognition system which can tolerate high degrees of noise and occlusion levels. The performance of the system has been demonstrated using five different types of aircraft and the experimental results are reported.
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