Efficient Belief Propagation with Census and Intensity Measure

Stereo matching is one of the most active research areas in computer vision. In this paper, a novel stereo matching is proposed that utilizes Census measure and pixels-based intensity measure into data term of Belief Propagation algorithm. Traditional data term of Belief Propagation lies on pixels-based intensity measure, and its effect is not very well. We combine intensity and Census algorithm into data term, run through Belief Propagation algorithm and acquire more accurate results. This proposed method may be more exacter than traditional BP algorithm. The experimental results demonstrate the superior performance of our proposed method.