A model-based machine vision system using fuzzy logic

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An effective model-based machine vision system is designed for use in practical production lines. In the proposed system, the gray level corner is selected as a local feature, and a gray level corner defector is developed The gray level corner defection problem is formulated as a pattern classification problem to determine whether a pixel belongs to the class of corners or not. The probability density function is estimated by means of fuzzy logic. A corner matching method is developed to minimize the amount of calculation. All available information obtained from the gray level corner detector is used to make the model. From a fuzzy inference procedure, a matched segment list is extracted and the resulted segment list is used to calculate the transformations between the model object and each object in the scene. In order to reduce the fuzzy rule set, a notion of overlapping cost is introduced. To show the effectiveness of the developed algorithm simulations are conducted for synthetic images, and an experiment is conducted on an image of a real industrial component. (C) 1997 Elsevier Science Inc.
Publisher
ELSEVIER SCIENCE INC
Issue Date
1997-01
Language
English
Article Type
Article
Keywords

RECOGNITION; OBJECTS

Citation

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, v.16, no.1, pp.119 - 135

ISSN
0888-613X
URI
http://hdl.handle.net/10203/78127
Appears in Collection
EE-Journal Papers(저널논문)
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