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Proceedings Paper

Integration of point feature acquisition, tracking, and uncalibrated metric reconstruction
Author(s): Martin Tonko; Keisuke Kinoshita
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Paper Abstract

In this paper a sequence of algorithms for image feature point detection and tracking as well as Euclidian reconstruction of rigid 3D objects from point correspondence is presented. For fully automatic point feature detection, gray value images are processed. High-curvature points on contours, i.e. contour elements with locally maximal curvature, are tracked using a normalized correlation algorithm. High-curvature points that could be tracked in a sequence of more than three images are used as point feature that are eligible for reconstruction. Since the general way to obtain the epipolar, projective, and Euclidian geometries from point feature correspondence is already solved, here the emphasis is on the performance of the algorithms in the presence of noise. Kanatani's epipolar geometry estimation method is improved and this is experimentally validated. Regarding Bougnoux's Euclidian geometry estimation method, the initial linear solution is now obtained with less uncertainty and the non-linear minimization does no longer converge to a hidden solution. Experimental results are given to assess the system performance.

Paper Details

Date Published: 26 August 1999
PDF: 12 pages
Proc. SPIE 3837, Intelligent Robots and Computer Vision XVIII: Algorithms, Techniques, and Active Vision, (26 August 1999); doi: 10.1117/12.360309
Show Author Affiliations
Martin Tonko, ATR Human Information Processing Research Labs. (Japan)
Keisuke Kinoshita, ATR Human Information Processing Research Labs. (Japan)


Published in SPIE Proceedings Vol. 3837:
Intelligent Robots and Computer Vision XVIII: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)

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