Abstract
This paper presents a near-real-time stereo matching method using both cross-based support regions in stereo views. By applying the logical AND operator to the cross-based support region in the reference image and target image, we can obtain an intersection support region, which is used as an adaptive matching window. The proposed method aggregates absolute difference estimates in the intersection support region, which are combined with the census transform results. The census transform with a fixed window size and shape is applied, and only the resultant binary code of the pixel in the intersection support region is used. From Middlebury images and their ground truth disparity maps, we compute the area similarity ratio of support regions in stereo views. Then, a conditional probability of observing a correct disparity estimate with respect to the area similarity ratio is examined. By taking a natural logarithm of the probability, a relative reliability weight about the area similarity of support regions is obtained. The initial matching cost is then combined with the reliability weight to obtain the final cost, and the disparity with the minimum cost is chosen as the final disparity estimate. Experimental results demonstrate that the proposed method can estimate accurate disparity maps.
Highlights
Stereo vision applications based on stereo matching are becoming increasingly common, ranging from mobile robotics to driver assistance system
Dense stereo matching to find the disparity for every pixel between two or more images has been actively researched for decades.[1,2,3,4,5,6,7,8,9,10]
This paper introduces an adaptive stereo matching method using the cross-based support regions in stereo views
Summary
Stereo vision applications based on stereo matching are becoming increasingly common, ranging from mobile robotics to driver assistance system. The goal of stereo matching is to determine a precise disparity, which indicates the difference in the location of the corresponding pixels. The corresponding pixels between two images of the same scene are established based on similarity measures. Dense stereo matching to find the disparity for every pixel between two or more images has been actively researched for decades.[1,2,3,4,5,6,7,8,9,10]. Stereo matching algorithms are classified into global and local approaches.[1] Local methods utilize the color or intensity values within a finite support window to determine the disparity for each pixel. Local algorithms select the potential disparity with the minimal matching cost at the pixel; they are efficient and easy to implement
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