Object recognition using neural networks and high-order perspective-invariant relational descriptions

Kenyon R. Miller; John F. Gilmore

doi:10.1117/12.57098

1 February 1992 Object recognition using neural networks and high-order perspective-invariant relational descriptions

Kenyon R. Miller, John F. Gilmore

Proceedings Volume 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques; (1992) https://doi.org/10.1117/12.57098
Event: Robotics '91, 1991, Boston, MA, United States

Abstract

The task of 3-D object recognition can be viewed as consisting of four modules: extraction of structural descriptions, hypothesis generation, pose estimation, and hypothesis verification. The recognition time is determined by the efficiency of each of the four modules, but particularly on the hypothesis generation module which determines how many pose estimates and verifications must be done to recognize the object. In this paper, a set of high-order perspective-invariant relations are defined which can be used with a neural network algorithm to obtain a high-quality set of model-image matches between a model and image of a robot workstation. Using these matches, the number of hypotheses which must be generated to find a correct pose is greatly reduced.

Citation Download Citation

Kenyon R. Miller and John F. Gilmore "Object recognition using neural networks and high-order perspective-invariant relational descriptions", Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); https://doi.org/10.1117/12.57098

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