Analysis of grey-level features for line segment stereo matching
In order to recover 3D-information from stereo image pairs, a number of stereo matching methods are available. These methods mainly differ in various tokens which are applied to solve the correspondence problem between left and right image. In most of the line segment based stereo matching algorithms, geometrical and structural information is used to estimate the correspondence. Unfortunately these features are dependent on the accuracy of segmentation and the angle of view. This might cause an ambiguity regarding the solution. Grey-level features (GLF) are introduced as robust features independent of segmentation errors and the angle of view. Concerning different line segmentation algorithms, e.g. sequential-oriented or global-oriented algorithms, two methods are proposed for a proper estimation of grey-level information. Finally this additional information will be applied in a line segment based stereo matching algorithm. Regarding these different grey-level features the uniqueness of the solution and the computational effort will be compared with geometrical features. Experimental results are presented.
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- N. Ayache: „Artificial Vision for Mobile Robots”, MIT Press, Cambridge, Massachusetts, 1991.Google Scholar
- O. D. Faugeras: „Three-Dimensional Computer Vision”, MIT Press, Cambridge, Massachusetts, London, England, 1993.Google Scholar
- W. D. Fellner: Computer Grafik, Reihe Informatik, Band 58, BI Wissenschaftsverlag, 1988, pp. 95–98.Google Scholar
- N. Guil, J. Villalba, E. Zapata: “A Fast Hough Transform for Segment Detection”, IEEE Transactions on Image Processing, Vol.4, No. 11, Nov. 1995, pp. 1541–1548.Google Scholar
- R. M. Haralick: “A Facet Model for Image Data, Computer Graphics and Image Processing”, Nr. 15, pp. 113–129, from R. Chellappa, A. Sawchuk: Digital Image Processing and Analysis: Vol. 1, IEEE Computer Society Press, Silver Spring 1981.Google Scholar
- D. Torkar, N. Pavesic: „Feature Extraction from Aerial Images and Structural Stereo Matching”, Int. Conf. on Pattern Recognition, Aug. 1996, Austria.Google Scholar
- A. Ude, T. E. Ekre: „Stereo Grouping for Model-Based Recognition”, Int. Conf. on Pattern Recognition, Aug. 1996, Austria.Google Scholar
- V. Venkateswar and R. Chellappa: „Hierarchical Stereo Matching Using Feature Groupings”, Proc. of Image Understanding Workshop, January 1992.Google Scholar
- K. Wall and P. E. Danielson:“A Fast Sequential Method for Polygonal Approximation of Digitized Curves”, Pattern Recognition Vol. 12, Pergamon Press Ltd., England 1980, pp. 327–331.Google Scholar