Recognizing And Locating Objects Using Partial Object Description Generated By Feature Extraction By Demands

Hsianglung Wu; Francis L. Merat

doi:10.1117/12.942718

19 February 1988 Recognizing And Locating Objects Using Partial Object Description Generated By Feature Extraction By Demands

Hsianglung Wu, Francis L. Merat

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Proceedings Volume 0848, Intelligent Robots and Computer Vision VI; (1988) https://doi.org/10.1117/12.942718
Event: Advances in Intelligent Robotics Systems, 1987, Cambridge, CA, United States

Abstract

A method called Feature Extraction by Demands(FED) has been developed to generate object descriptions. Objects are described by surface adjacency graphs containing the surface class and the surface equation at each node. Due to occlusion and the use of 21D range images the generated object description is frequently partial. This paper describes a new method to generate object hypotheses and to recognize and locate viewed objects using partial descriptions of objects bounded by quadric surfaces. The method proceeds in two phases. In phase one, the object location is estimated from matched surface pairs(between an object description and an object model). Depending upon the surface type, each surface may provide partial or complete location information. As long as the location information calculated from matched surface pairs is consistent, that is passes matching feasibility tests, the object model is a candidate for the viewed object. The consistent partial location information is combined into a more complete object location estimation sequentially. The order of location information to be used in object location estimation is decided by whether a more complete object location can be calculated. If a complete location can be calculated or the object location estimation cannot be further refined the hypothesis is verified by phase two. In phase two, each remaining surface which was not used for object location estimation is searched for a matched model surface and the neighboring relations between surfaces are verified. If the hypothesis passes phase two the model is accepted as a matched model. If a complete object location can be calculated from the accepted hypothesis an optimal object location is calculated.

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Hsianglung Wu and Francis L. Merat "Recognizing And Locating Objects Using Partial Object Description Generated By Feature Extraction By Demands", Proc. SPIE 0848, Intelligent Robots and Computer Vision VI, (19 February 1988); https://doi.org/10.1117/12.942718

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KEYWORDS

Visual process modeling

Object recognition

Feature extraction

Machine vision

Optical spheres

Computer vision technology

Robot vision

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