A Hybridized Disparity Computation Fusing Disparity Space Image and Multi Resolution Image Segmentation

Abstract

D information is getting more importance nowadays.There are several ways to get 3D information.One way to get 3D information involves the laser based depth estimators, which is a very costly process. Other way is to extract 3D information from 2D stereo image pairs by using stereo matching techniques. Stereo matching is a vast area of research. It can be classified into area (window) based approach, feature based approach, and optimization based approach. Area based approach generally generates dense disparity map with low accuracy and low computation time. Feature based method produces highly accurate and sparse disparity map with low computation time.Optimization based method produces dense disparity map with high accuracy and high execution time.By keeping these things in mind, we proposed a new hybrid way for disparity computation. The proposed method consists of three steps. Theyare disparityestimation, image segmentation, and disparity refinement. Since the ultimate goal of stereo matching is to obtain dense disparity map with high accuracy and low execution time, we select optimization based approach for disparity estimation step and for image segmentation step we select multi resolution image segmentation. At the end, disparity refinement is done by combining the result of both the previous steps.As there are several optimization techniques, we choose disparity space image (DSI) baseddynamic programming (DP).We tested the proposed method on several stereo pairs and found that method produced reasonably good quality results. Index Terms—Stereo matching, DSI, DP.