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Proceedings Paper

Prediction-Verification, Hypothesis Accumulation For Scene Analysis By Object Identification
Author(s): G. Rives; J.-T. Lapreste; M. Dhome; M. Richetin
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Paper Abstract

For scene analysis by object identification, a new method is presented which involves a structural representation of the objects the components of which are local patterns. These local patterns are detected with a hypothesis accumulation technique analogous to the generalized Hough transform but adapted to polygonal contours as pattern representations. The object identification is controlled by a prediction-verification procedure. When a first local pattern is found by hypothesis accumulation, according to the structural model of the object, a second one is predicted, i.e. its direction and the window in which it is expected are given. Its detection is then verified by hypothesis accumulation with these research parameter values. Such an identification method gathers the advantages of both the prediction-verification and the hypothesis accumulation approaches which are respectively, the ability to make very few hypotheses for object recognition, and the capability of handling variations in polygonal contour segmentation. This method has been applied to 2D-scenes of partially observed pieces and the reported results prove its efficiency.

Paper Details

Date Published: 9 June 1986
PDF: 6 pages
Proc. SPIE 0595, Computer Vision for Robots, (9 June 1986); doi: 10.1117/12.952274
Show Author Affiliations
G. Rives, University of Clermont II (France)
J.-T. Lapreste, University of Clermont II (France)
M. Dhome, University of Clermont II (France)
M. Richetin, University of Clermont II (France)

Published in SPIE Proceedings Vol. 0595:
Computer Vision for Robots
Olivier D. Faugeras; Robert B. Kelley, Editor(s)

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