A neural network approach for stereo vision

A method for achieving stereo vision using a neural network to solve the correspondence problem is presented. The algorithm is edge based and uses the epipolar constraint. The algorithm is in two stages. The first stage is designed to extract the features or primitives for matching, using a static connectionist network. A similarity of measure is defined for each pair of primitive matches, which are then passed on to the second stage of the algorithm. The purpose of the second stage is to turn the difficult correspondence problem into a constraint satisfaction problem by imposing some relational constraints. This is solved using a network of neurons. The results of computer simulations are presented to demonstrate the effectiveness of the approach.<<ETX>>

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