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A Window-Based Adaptive Correspondence Search Algorithm Using Mean Shift and Disparity Estimation

Shujun Zhang, Jianbo Zhang, Yun Liu
This article is referenced in HAL

The International Journal of Virtual Reality, 2011, 10 (3): 65-72


Current methods to solve the problem of binocular stereo matching can be divided into two categories: sparse points based methods and dense points based methods. However, both of them have different shortcomings and limitations. There is no perfect method to solve the disparity problem. Dense points based techniques relatively obtain more accurate results but with higher computation. A large number of window-based adaptive corres-pondence techniques have emerged in recent years. In order to solve the problem of high time complexity and large amount of calculation in matching process, we propose a new window-based correspondence search algorithm using mean shift and disparity estimation. Mean shift can aggregate the same or similar colors so it can be applied to pre-process the source images to reduce their dynamic color range. Disparity estimation is conducted on the pre-processed two images to compute disparities of uniform texture regions. Adaptive window matching through similarity computation and window-based support aggregation is finally executed and exact depth map is obtained. Experimental results show that our algorithm is more efficient and keeps smooth dis-parity better than the prior window method


Window-based adaptive correspondence,  mean shift,  disparity estimation,  binocular stereo matching

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