Stereo matching using belief propagation and local edge construction-based cost aggregation
置信度传播和区域边缘构建的立体匹配算法

In this paper,the importance of cost aggregation,also called similarity measure,for belief propagation and the interaction of them are discussed.A global stereo matching algorithm is proposed by combining the belief propagation and local edge construction-based cost aggregation.First,a virtual closed edge is formed surrounding each pixel via second derivative operator in order to construct an adaptive window for the centered pixel.Then,the local cost aggregation is calculated on support pixels in an adaptive window.Finally,accelerated belief propagation optimization algorithm is used to obtain the disparity.The experiments based on the Middlebury benchmark indicate that the local edge construction-based cost aggregation can do well with belief propagation optimization and show encouraging results of the proposed stereo matching algorithm.