Abstract
This paper describes a new algorithm to simultaneously detect and classify straight lines according to their orientation in 3-D. The fundamental assumption is that the most “interesting” lines in a 3-D scene have orientations which fall into a few precisely defined categories. The algorithm we propose uses this assumption to extract the projection of straight edges from the image and to determine the most likely corresponding orientation in the 3-D scene. The extracted 2-D line segments are therefore “perceptually” grouped according to their orientation in 3-D. Instead of extracting all the line segments from the image before grouping them by orientation, we use the orientation data at the lowest image processing level, and detect segments separately for each predefined 3-D orientation. A strong emphasis is placed on real-world applications and very fast processing with conventional hardware.
This research was supported in part by the DoD Joint Services Electronics Program through the Air Force Office of Scientific Research (AFSC) Contract F49620-89-C-0044, and in part by the Army Research Office under contract DAAL03-91-G-0050.
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© 1992 Springer-Verlag Berlin Heidelberg
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Lebègue, X., Aggarwal, J.K. (1992). Detecting 3-D parallel lines for perceptual organization. In: Sandini, G. (eds) Computer Vision — ECCV'92. ECCV 1992. Lecture Notes in Computer Science, vol 588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-55426-2_80
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DOI: https://doi.org/10.1007/3-540-55426-2_80
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