A Improved Stereo Matching Fast Algorithm Based on Dynamic Programming

Abstract:

Compared with the local algorithm in stereo matching, the high quality disparity space image is calculated by the global algorithm, which is difficult to use in practical application for its long computation time. The dynamic programming is one of the global algorithms with a fast matching speed, but it has strip blemish in matching result. In this paper, a new dynamic programming based method is proposed to accelerate the matching speed and improve the matching quality. Firstly, the color feature of two images are calculated using the Laplacians of Gaussian pyramid algorithm, and the color feature of the image pair obtained are matched. Secondly, the matching points are taken as the ground control points of the scan line, which is cut into several short line segments. Finally, all line segments are matched to obtain the disparity of the scan line. The experimental results show that the matching speed is accelerated greatly with improved disparity image quality

• Periodical: Key Engineering Materials (Volumes 531-532)
• Pages: 657-661
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.531-532.657
• Online since: December 2012
• Authors: Zi Wei Zhou, Ge Li, Chang Le Li, Ji Zhuang Fan
• Keywords: Dynamic Programming, Global Optimize Algorithm, Image Pyramid, Stereo Matching

References

[1] D. Scharstein, R. Szeliski, and R. Zabih. "A taxonomy and evaluation of dense two-frame stereo correspondence algorithms". In IEEE Workshop on Stereo and Multi-Baseline Vision, 2001.

DOI: 10.1109/smbv.2001.988771

[2] P. Anandan. "A computational framework and an algorithm for the measurement of visual motion". IJCV, 2(3):283–310, 1989.

[3] Y. Boykov, O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 11, p.1222–1239, (2001)

DOI: 10.1109/34.969114

[4] J. Sun, N.-N. Zheng and H.-Y. Shum, "Stereo Matching Using Belief Propagation", PAMI, Vol.25, No. 7, July 2003.

[5] P. F. Felzenszwalb and D. P. Huttenlocher, "Efficient Belief Propagation for Early Vision", CVPR,Vol. I:261-268, 2004.

[6] L. Wang, M. Liao, M. Gong, R. Yang, and D. Nist´er, "High-quality real-time stereo usingadaptive cost aggregation and dynamic programming", 3DPVT 2006.

DOI: 10.1109/3dpvt.2006.75

[7] Ohta Y, Kanade T. "Stereo by intra and inter-scanline search using dynamic programming"[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 1985, 7(2): 139-154.

DOI: 10.1109/tpami.1985.4767639

[8] Stephen S Intille, Aaron F Bobick. "Disparity-space images and large occlusion stereo"[C]. 3rd European Conf on Computer Vision. Stockholm, 1994:179-186.

DOI: 10.1007/bfb0028349

[9] M. Bleyer, C. Rother, and P. Kohli, "Surface stereo with soft segmentation," in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p.1570–1577, June 2010.

DOI: 10.1109/cvpr.2010.5539783

[10] Ohta Y, Kanade T. "Stereo by intra and inter-scanline search using dynamic programming"[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 1985, 7(2): 139-154.

DOI: 10.1109/tpami.1985.4767639