Genetic algorithm for stereo correspondence with a novel fitness function and occlusion handling

Abstract

This paper proposes a genetic algorithm for solving the stereo correspondence problem. Applied to stereo, genetic algorithms are flexible in the cost function and permit global reasoning. The main contribution of this paper is a new crossover and a mutation operator which accounts for occlusion management and a new fitness function which considers occluded pixels and photometric derivatives. Both left and right disparity images are analysed in order to classify occluded pixels correctly. The proposed fitness function is compared to the traditional energy function based in the framework of the Markov Random Fields. The results show that a 32% bad-pixel error reduction can be achieved on average using the proposed fitness function. The results have been uploaded to the Middlebury ranking webpage, as the first evolutionary algorithm evaluated.This paper proposes a genetic algorithm for solving the stereo correspondence problem. Applied to stereo, genetic algorithms are flexible in the cost function and permit global reasoning. The main contribution of this paper is a new crossover and a mutation operator which accounts for occlusion management and a new fitness function which considers occluded pixels and photometric derivatives. Both left and right disparity images are analysed in order to classify occluded pixels correctly. The proposed fitness function is compared to the traditional energy function based in the framework of the Markov Random Fields. The results show that a 32% bad-pixel error reduction can be achieved on average using the proposed fitness function. The results have been uploaded to the Middlebury ranking webpage, as the first evolutionary algorithm evaluated.