A Hierarchical Genetic Disparity Estimation Algorithm for Multiview Image Synthesis

In this paper, a hierarchical genetic algorithm for disparity estimation is presented. The goal, to estimate reliable disparity fields with low computational cost, is reached using a hierarchical genetic matching procedure. Firstly, each hierarchical image of the stereo pair is divided into sets of feature points and non-feature points. The image morphological gradient for feature points and the disparity Laplacian function for non-feature points are incorporated into the matching function to serve as an adaptive smoothness term. Meanwhile, the verticaldiscontinuity constraint and the ordering constraint are also proposed to smooth out vertical disparity discontinuities and to obtain a more reliable disparity estimation. In the hierarchical genetic matching procedure, previously estimated vectors at the former image hierarchy are used to predict the corresponding searching , space of chromosomes, and to correct each newly calculated set of disparity vectors. This significantly increases the accuracy of disparity estimation.