Developing matching algorithms from stereo image pairs to obtain correct disparity maps for 3D reconstruction has been the focus of intensive research. A constant computational complexity algorithm to calculate dissimilarity aggregation in assessing disparity based on separable successive weighted summation (SWS) among horizontal and vertical directions was proposed but still not satisfactory. This paper presents a novel method which enables decoupled dissimilarity measure in the aggregation, further improving the accuracy and robustness of stereo correspondence. The aggregated cost is also used to refine disparities based on a local curve-fitting procedure. According to our experimental results on Middlebury benchmark evaluation, the proposed approach has comparable performance when compared with the selected state-of-the-art algorithms and has the lowest mismatch rate. Besides, the refinement procedure is shown to be capable of preserving object boundaries and depth discontinuities while smoothing out disparity maps. 1. Introduction Stereo vision is the technique of constructing a 3D description of the scene from stereo image pairs, which is important in many computer vision tasks such as inspection [1], 3D object recognition [2], robot manipulation [3], and autonomous navigation [4]. Stereo vision systems can be active or passive. Active techniques utilize ultrasonic transducers and structured light or laser to simplify the stereo matching problem. On the other hand, passive stereo vision based only on stereo image pairs is less intrusive and typically able to provide a compact and affordable solution for range sensing. For passive stereo vision systems, stereo matching algorithms are crucial for correct and accurate depth estimation, which find for each pixel in one image the corresponding pixel in the other image. A 2D picture of displacements between corresponding pixels of a stereo image pair is named as a disparity map [5]. Reference [6] is an intensively cited classification of stereo matching algorithms for rectified image pairs. The paper divides most of the algorithms into four sequential parts: matching cost calculation, cost aggregation, disparity computation, and disparity refinement. Among the steps, cost aggregation determines the performance of an algorithm in terms of computational complication and correctness. Cost aggregation can be local [7–12] or global [13–16], based on differences in the range of supporting regions or windows. Global methods assume that the scene is piecewise smooth and search for disparity assignments over the
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