IMAGE-GUIDED NON-LOCAL DENSE MATCHING WITH THREE-STEPS OPTIMIZATION

This paper introduces a new image-guided non-local dense matching algorithm that focuses on how to solve the following problems: 1) mitigating the influence of vertical parallax to the cost computation in stereo pairs; 2) guaranteeing the performance of dense matching in homogeneous intensity regions with significant disparity changes; 3) limiting the inaccurate cost propagated from depth discontinuity regions; 4) guaranteeing that the path between two pixels in the same region is connected; and 5) defining the cost propagation function between the reliable pixel and the unreliable pixel during disparity interpolation. This paper combines the Census histogram and an improved histogram of oriented gradient (HOG) feature together as the cost metrics, which are then aggregated based on a new iterative non-local matching method and the semi-global matching method. Finally, new rules of cost propagation between the valid pixels and the invalid pixels are defined to improve the disparity interpolation results. The results of our experiments using the benchmarks and the Toronto aerial images from the International Society for Photogrammetry and Remote Sensing (ISPRS) show that the proposed new method can outperform most of the current state-of-the-art stereo dense matching methods.