Abstract—3D 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.
The authors are with the Myongji University, Yongin, 449-728, Korea
(e-mail: mudasar.kalwar@gmail.com, soh@mju.ac.kr,
nothan111@gmail.com, kit@mju.ac.kr).
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Cite:Mudasar Qadir, Youngsung Soh, Hadi Ashraf, and Intaek Kim, "A Hybridized Disparity Computation Fusing Disparity Space Image and Multi Resolution Image Segmentation," International Journal of Computer Theory and Engineering vol. 6, no. 5, pp. 421-425, 2014.
