Many stereo matching algorithms use fixed color thresholds and a rigid cross skeleton to segment supports (viz., Cross method), which, however, does not work well for different images. To address this issue, this paper proposes a novel dual adaptive support (viz., DAS)-based stereo matching method, which uses both appearance and shape information of a local region to segment supports automatically, and, then, integrates the DAS-based cost aggregation with the absolute difference plus census transform cost, scanline optimization and disparity refinement to develop a stereo matching system. The performance of the DAS method is also evaluated in the Middlebury benchmark and by comparing with the Cross method. The results show that the average error for the DAS method 25.06% lower than that for the Cross method, indicating that the proposed method is more accurate, with fewer parameters and suitable for parallel computing.
The scale-invariant feature transform (SIFT) algorithm is devised to detect keypoints via the difference of Gaussian (DoG) images. However, the DoG data lacks the high-frequency information, which can lead to a performance drop of the algorithm. To address this issue, this paper proposes a novel log-polar feature detector (LPFD) to detect scale-invariant blubs (keypoints) in log-polar space, which, in contrast, can retain all the image information. The algorithm consists of three components, viz. keypoint detection, descriptor extraction and descriptor matching. Besides, the algorithm is evaluated in detecting keypoints from the INRIA dataset by comparing with the SIFT algorithm and one of its fast versions, the speed up robust features (SURF) algorithm in terms of three performance measures, viz. correspondences, repeatability, correct matches and matching score.
The 1-D mapping is an intensity-based method used to estimate a projective transformation between two images. However, it lacks an intensity-invariant criterion for deciding whether two images can be aligned or not. The paper proposes a novel decision criterion and, thus, develops an error-detective 1-D mapping method. First, a multiple 1-D mapping scheme is devised for yielding redundant estimates of an image transformation. Then, a voting scheme is proposed for verifying these multiple estimates, in which at least one estimate without receiving all the votes is taken as a decision criterion for false-match rejection. Based on the decision criterion, an error-detective 1-D mapping algorithm is also constructed. Finally, the proposed algorithm is evaluated in registering real image pairs with a large range of projective transformations.