1 February 1992 Object recognition using neural networks and high-order perspective-invariant relational descriptions
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Proceedings Volume 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques; (1992); doi: 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.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kenyon R. Miller, 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); doi: 10.1117/12.57098; https://doi.org/10.1117/12.57098
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KEYWORDS
Neural networks

Robot vision

Evolutionary algorithms

Image segmentation

Computer vision technology

Machine vision

Object recognition

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