Stereo Matching Algorithm Based on Detecting Feature Points

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A new method for solving the stereo matching problem in the presence of large occlusion is presented. This method for stereo matching and occlusion detection is based on searching disparity point. In this paper, we suppose that a pair of epipolar-line images is a projection of a group of piece-wise straight lines on the left and right images respective. Therefore the disparity curve corresponding to a pair of epipolar-line images may be approximated by a group of piece-wise straight lines. Then the key of solving disparity curve is how to get the “characteristic points” on the group of piece-wise straight lines. Based on this view, we fetched out the conception “disparity point”, and three kinds of special disparity points are correctly corresponding to the “characteristic point”. By analyzing intensity property of a disparity point and its neighbor points, an approach which combines stepwise hypothesis-verification strategy with three constraint conditions is devised to extract the candidate disparity points from the epipolar images.

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Advanced Materials Research (Volumes 433-440)

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6190-6194

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January 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] Stephen S. Intille and Aaron F. Bobick Disparity –Space Images and Large Occlusion Stereo, Third European Conference Computer Vision, pp.179-186, (1994).

DOI: 10.1007/bfb0028349

Google Scholar

[2] Vladimir Kolmogorov and Ramin Zabih Computing Visual Correspondence with Occlusions via Graph Cuts" in International Conference on Computer Vision (ICCV, 01), July 07 - 14 (2001).

DOI: 10.1109/iccv.2001.937668

Google Scholar

[3] C. Lawrence Zitnick, Takeo Kanade A Cooperative Algorithm for Stereo Matching and Occlusion Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 22 ,  Issue 7 pp。 675 – 684, (2000).

DOI: 10.1109/34.865184

Google Scholar

[4] A. Luo and H. Burkhardt An intensity-based cooperative bidirectional stereo matching with simultaneous detection of discontinuities and occlusions, IJCV, 15(3): 171-188, July (1995).

DOI: 10.1007/bf01451740

Google Scholar

[5] Jana Kostlova Stereoscopic Matching: Problems and Solutions.

Google Scholar

[6] Richard Szeliski and Polina Golland Stereo Matching with Transparency and Matting, In Proceedings of International Conference on Computer Vision, pages 517-524, Sept (1998).

DOI: 10.1109/iccv.1998.710766

Google Scholar

[7] Ebroul Izquierdo M. Disparity/Segmentation Analysis: Matching with an Adaptive Window and Depth-Driven Segmentation, Circuits and Systems for Video Technology, IEEE Transactions on Publication , Vol: 9,  Iss: 4 pp: 589-607, (1999).

DOI: 10.1109/76.767125

Google Scholar

[8] Atsushi Marugame, Akio Yamada Focused Object Extraction with Multiple Cameras, IEEE Trans. Circuits Syst. Video Technol. 10(4), 530–540 (2000).

DOI: 10.1109/76.844998

Google Scholar

[9] Jane Mulligan, Xenophon Zabulis, Nikhil Kelshikar, and Kostas Daniilidis Stereo-based Environment Scanning for Immersive Telepresence, IEEE Trans. on Circuits and Systems for Video Technology, Special Issue on Immersive Telepresence, (2003).

DOI: 10.1109/tcsvt.2004.823390

Google Scholar

[10] Daniel Scharstein,Richard Szeliski A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms, Intl. Journal of Comp. Vision, vol. 47, pp.7-42, May (2002).

DOI: 10.1109/smbv.2001.988771

Google Scholar