Computation of Stereo Dense Disparity Maps Using Region Segmentation


The KIPS Transactions:PartB , Vol. 15, No. 6, pp. 517-526, Dec. 2008
10.3745/KIPSTB.2008.15.6.517,   PDF Download:

Abstract

Stereo vision is a fundamental method for measuring 3D structures by observing them from two cameras placed on different positions. In order to reconstruct 3D structures, it is necessary to create a disparity map from a pair of stereo images. To create a disparity map we compute the matching cost for each point correspondence and compute the disparity that minimizes the sum of the whole matching costs. In this paper, we propose a method to estimate a dense disparity map using region segmentation. We segment each scanline using region homogeneity properties. Using the segmented regions, we prohibit false matches in the stereo matching process. Disparities for pixels that failed in matching are filled by interpolating neighborhood disparities. We applied the proposed method to various stereo images of real environments. Experimental results showed that the proposed method is stable and potentially viable in practical applications.


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Cite this article
[IEEE Style]
B. J. Lee, J. S. Park, C. K. Kim, "Computation of Stereo Dense Disparity Maps Using Region Segmentation," The KIPS Transactions:PartB , vol. 15, no. 6, pp. 517-526, 2008. DOI: 10.3745/KIPSTB.2008.15.6.517.

[ACM Style]
Bum Jong Lee, Jong Seung Park, and Chung Kyue Kim. 2008. Computation of Stereo Dense Disparity Maps Using Region Segmentation. The KIPS Transactions:PartB , 15, 6, (2008), 517-526. DOI: 10.3745/KIPSTB.2008.15.6.517.