Abstract
Stereo matching has been under development for decades and is an important process for many applications. Difficulties in stereo matching include textureless regions, occlusion, illumination variation, the fattening effect, and discontinuity. These challenges are effectively solved in recently developed stereo matching algorithms. A new imperfect rectification problem has recently been encountered in stereo matching, and the problem results from the high resolution of stereo images. State-of-the-art stereo matching algorithms fail to exactly reconstruct the depth information using stereo images with imperfect rectification, as the imperfectly rectified image problems are not explicitly taken into account. In this paper, we solve the imperfect rectification problems, and propose matching stereo matching methods that based on absolute differences, square differences, normalized cross correlation, zero-mean normalized cross correlation, and rank and census transforms. Finally, we conduct experiments to evaluate these stereo matching methods using the Middlebury datasets. The experimental results show the proposed stereo matching methods can reduce error rate significantly for stereo images with imperfect rectification.
Highlights
Stereo matching is an important process in the field of computer vision, the goal of which is to reconstruct three-dimensional (3D) information from a scene with left and right stereo images [1].Stereo matching algorithms have been commonly applied in medical imaging and 3D imaging systems, such as satellite-based earth and space exploration, autonomous robots, and vehicle and security systems [2]
We do not intend to compare the performance of the test matching cost functions and stereo matching algorithms
The global stereo matching algorithms, which are based on ImpAD, ImpSD, ImpRank, and ImpCensus, use the same parameter values as the global algorithms that are based on the absolute different (AD), squared difference (SD), Rank, and Census matching cost functions, respectively
Summary
Stereo matching is an important process in the field of computer vision, the goal of which is to reconstruct three-dimensional (3D) information from a scene with left and right stereo images [1]. The assumption of existing dense stereo matching algorithms is that input stereo images are perfectly rectified such that correspondent pixels between the rectified stereo images have the same y-coordinate values This assumption is commonly known as the frontal-parallel assumption. When working on stereo images with high resolution, stereo matching algorithms are required to consider this imperfect rectification problem, as the frontal-parallel assumption does not hold true anymore. Correspondent pixels in stereo images with imperfect rectification may be located in different epipolar lines [19] This means that correspondent pixels do not satisfy the frontal-parallel assumption that all dense stereo matching algorithms require. Existing stereo matching methods are dense methods that compute disparity values for each pixel, and most algorithms implicitly or explicitly make an assumption about epipolar geometry that the corresponding pixels locate in the same epipolar line. The testing global stereo matching algorithms include the AD and graph cut (GC) [13], SD and GC, Rank and GC, and Census and GC algorithms
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