Prediction-Verification, Hypothesis Accumulation For Scene Analysis By Object Identification

G. Rives; J.-T. Lapreste; M. Dhome; M. Richetin

doi:10.1117/12.952274

9 June 1986 Prediction-Verification, Hypothesis Accumulation For Scene Analysis By Object Identification

G. Rives, J.-T. Lapreste, M. Dhome, M. Richetin

Author Affiliations +

Proceedings Volume 0595, Computer Vision for Robots; (1986) https://doi.org/10.1117/12.952274
Event: 1985 International Technical Symposium/Europe, 1985, Cannes, France

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.

Citation Download Citation

G. Rives, J.-T. Lapreste, M. Dhome, and M. Richetin "Prediction-Verification, Hypothesis Accumulation For Scene Analysis By Object Identification", Proc. SPIE 0595, Computer Vision for Robots, (9 June 1986); https://doi.org/10.1117/12.952274

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE