Stereo matching using genetic algorithm with adaptive chromosomes
A new stereo matching scheme using a genetic algorithm is presented to improve the depth reconstruction method of stereo vision systems. Genetic algorithms are efficient search methods based on principles of population genetics, i.e. mating, chromosome crossover, gene mutation, and natural selection. The proposed approach considers the matching environment as an optimization problem and finds the optimal solution by using an evolutionary strategy. Accordingly, genetic operators are adapted for the circumstances of stereo matching: (1) an individual is a disparity set, (2) a chromosome has a 2D structure for handling image signals efficiently, and (3) a fitness function is composed of certain constraints which are commonly used in stereo matching. Since the fitness function consists of intensity, similarity and disparity smoothness, the matching and relaxation processes are considered at the same time in each generation. In order to acquire a disparity map consistent with the image appearance, a region of the input image, divided by zero-crossing points, is extracted and used in the determination of the chromosome shape. As a result, all chromosomes contain the external image form, and the disparity output coincides with the input image without any modification of the matching algorithm. In addition, an informed gene generation based on intensity difference is applied to reduce the searching space of the genetic operations.
stereo matching genetic algorithm crossover mutation natural selection fitness function disparity smoothness intensity similarity informed generation