• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.75-85
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• 2013

To acquire depth information using stereo vision, it is required to find correspondence points between stereo image pair. Conventional stereo vision systems usually use two cameras to get disparity data. Therefore, conventional stereo matching methods cannot resolve the tradeoff problem between accuracy and precision with respect to the length of baseline. Besides, belief propagation method, which is being used recently, has a problem that matching performance is dependent on the fixed weight parameter ${\lambda}$. In this paper, we propose a modified belief propagation stereo matching technique based on multi-baseline stereo vision to solve the tradeoff problem. The proposed method calculates EMAD(extended mean of absolute differences) as local evidence. And proposed method decides weight parameter ${\lambda}$ adaptively to local texture information. The proposed method shows higher initial matching performance than conventional methods and reached optimum solution in less iteration. The matching performance is increased about 4.85 dB in PSNR.