Abstract
In this paper we propose a new stereo matching algorithm for real-time obstacle detection in front of a moving vehicle. The stereo matching problem is viewed as a constraint satisfaction problem where the objective is to highlight a solution for which the matches are as compatible as possible with respect to specific constraints. These constraints are of two types: local constraints, namely position, slope and gradient magnitude constraints, and global ones, namely uniqueness, ordering and smoothness constraints. The position and slope constraints are first used to discard impossible matches. Based on the global constraints, a voting stereo matching procedure is then achieved to calculate the scores of the possible matches. These scores are then weighted by means of the gradient magnitude constraint. The correct matches are finally obtained by selecting the pairs for which the weighted scores are maximum. The performance of the voting stereo matching algorithm is evaluated for real-time obstacle detection using linear cameras.
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REFERENCES
Jähne, B., and Haußecker, H. (2000). Computer Vision and Applications. Academic Press.
Barnard, S., and Fisher, M. (1982). Computational Stereo. ACM Computational Surveys, 14, pp. 553–572.
Kriegman, D.J., Triendl, E., and Binford, T.O. (1989). Stereo Vision and Navigation in Buildings for Mobile Robot. IEEE Transactions on Robotics and Automation, Vol. 5, No. 6.
Nitzan, D. (1988). Three-dimensional Vision Structure for Robot Application. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 10, No. 3, pp. 291–309.
Bruyelle, J.L. (1994). Conception and Realization of a Linear Stereoscopic Sensor: Application to Obstacle Detection if Front of Vehicles. PhD Thesis, University of Sciences and Technologies of Lille, France.
Inigo, R.M., and Tkacik, T. (1987). Mobile Robot Operation in Real-time With Linear Image Array Based Vision. Proceedings of the IEEE Intelligent Control Symposium, pp. 228–233.
Burie, J.C., Bruyelle, J.L., and Postaire, J.G. (1995). Detecting and Localising Obstacles in Front of a Moving Vehicle Using Linear Stereo Vision. Mathematical and Computer Modelling, Vol. 22, No. 4–7, pp. 235–246.
Deriche, R. (1990). Fast Algorithms for Low-level Vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 1, pp. 78–87.
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© 2006 Springer
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Harti, M., Ruichek, Y., Koukam, A. (2006). A VOTING STRATEGY FOR HIGH SPEED STEREO MATCHING. In: Wojciechowski, K., Smolka, B., Palus, H., Kozera, R., Skarbek, W., Noakes, L. (eds) Computer Vision and Graphics. Computational Imaging and Vision, vol 32. Springer, Dordrecht. https://doi.org/10.1007/1-4020-4179-9_27
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DOI: https://doi.org/10.1007/1-4020-4179-9_27
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-4178-5
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