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ACCV
2006
Springer

Stereo Matching Using Iterated Graph Cuts and Mean Shift Filtering

10 years 4 months ago
Stereo Matching Using Iterated Graph Cuts and Mean Shift Filtering
In this paper, we propose a new stereo matching algorithm using an iterated graph cuts and mean shift filtering technique. Our algorithm consists of following two steps. In the first step, given an estimated sparse RDM (Reliable Disparity Map), we obtain an updated dense disparity map through a new constrained energy minimization framework that can cope with occlusion. The graph cuts technique is employed for the solution of the proposed stereo model. In the second step, we reestimate the RDM from the disparity map obtained in the first step. In order to obtain accurate reliable disparities, the crosschecking technique followed by the mean shift filtering in the color-disparity space is introduced. The proposed algorithm expands the RDM repeatedly through the above two steps until it converges. Experimental results on the standard data set demonstrate that the proposed algorithm achieves comparable performance to the state-of-the-arts, and gives good results especially in the areas...
Ju Yong Chang, Kyoung Mu Lee, Sang Uk Lee
Added 13 Jun 2010
Updated 13 Jun 2010
Type Conference
Year 2006
Where ACCV
Authors Ju Yong Chang, Kyoung Mu Lee, Sang Uk Lee
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