A Nonlocal Method with Modified Initial Cost and Multiple Weight for Stereo Matching

This paper presents a new nonlocal cost aggregation method for stereo matching. The minimum spanning tree (MST) employs color difference as the sole component to build the weight function, which often leads to failure in achieving satisfactory results in some boundary regions with similar color distributions. In this paper, a modified initial cost is used. The erroneous pixels are often caused by two pixels from object and background, which have similar color distribution. And then inner color correlation is employed as a new component of the weight function, which is determined to effectively eliminate them. Besides, the segmentation method of the tree structure is also improved. Thus, a more robust and reasonable tree structure is developed. The proposed method was tested on Middlebury datasets. As can be expected, experimental results show that the proposed method outperforms the classical nonlocal methods.

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