Wide baseline stereo matching based on double topological relationship consistency

Xiaohong Zou; Bin Liu; Xiaoxue Song; Yang Liu

doi:10.1117/12.832371

6 August 2009 Wide baseline stereo matching based on double topological relationship consistency

Xiaohong Zou, Bin Liu, Xiaoxue Song, Yang Liu

Proceedings Volume 7384, International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Imaging Detectors and Applications; 738405 (2009) https://doi.org/10.1117/12.832371
Event: International Symposium on Photoelectronic Detection and Imaging 2009, 2009, Beijing, China

Abstract

Stereo matching is one of the most important branches in computer vision. In this paper, an algorithm is proposed for wide-baseline stereo vision matching. Here, a novel scheme is presented called double topological relationship consistency (DCTR). The combination of double topological configuration includes the consistency of first topological relationship (CFTR) and the consistency of second topological relationship (CSTR). It not only sets up a more advanced model on matching, but discards mismatches by iteratively computing the fitness of the feature matches and overcomes many problems of traditional methods depending on the powerful invariance to changes in the scale, rotation or illumination across large view changes and even occlusions. Experimental examples are shown where the two cameras have been located in very different orientations. Also, epipolar geometry can be recovered using RANSAC by far the most widely method adopted possibly. By the method, we can obtain correspondences with high precision on wide baseline matching problems. Finally, the effectiveness and reliability of this method are demonstrated in wide-baseline experiments on the image pairs.

Citation Download Citation

Xiaohong Zou, Bin Liu, Xiaoxue Song, and Yang Liu "Wide baseline stereo matching based on double topological relationship consistency", Proc. SPIE 7384, International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Imaging Detectors and Applications, 738405 (6 August 2009); https://doi.org/10.1117/12.832371

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KEYWORDS

Computer vision technology

Machine vision

Cameras

Image processing

Lithium

Evolutionary algorithms

Image segmentation

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