Abstract
This paper presents a comparative study between two road approximation techniques—planar surfaces—from stereo vision data. The first approach is carried out in the v-disparity space and is based on a voting scheme, the Hough transform. The second one consists in computing the best fitting plane for the whole 3D road data points, directly in the Euclidean space, by using least squares fitting. The comparative study is initially performed over a set of different synthetic surfaces (e.g., plane, quadratic surface, cubic surface) digitized by a virtual stereo head; then real data obtained with a commercial stereo head are used. The comparative study is intended to be used as a criterion for fining the best technique according to the road geometry. Additionally, it highlights common problems driven from a wrong assumption about the scene’s prior knowledge.
This work has been partially supported by the Spanish Ministry of Education and Science under project TRA2004-06702/AUT. The first author was supported by The Ramón y Cajal Program. The fourth author was supported by Spanish Ministry of Education and Science grant BES-2005-8864.
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Sappa, A.D., Herrero, R., Dornaika, F., Gerónimo, D., López, A. (2007). Road Approximation in Euclidean and v-Disparity Space: A Comparative Study. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2007. EUROCAST 2007. Lecture Notes in Computer Science, vol 4739. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75867-9_138
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DOI: https://doi.org/10.1007/978-3-540-75867-9_138
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