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Proceedings Paper

Computer vision system for automated inspection of molded plastic print wheels
Author(s): Yong-Lin Hu; William G. Wee; William A. Gruver; Chia Yung Han
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Paper Abstract

This research describes a robust, efficient, and real-time computer vision system that can automatically inspect defects of protruded print characters on injection molded plastic, typewriter print wheels. Possible defect types include insufficient fill, voids, and cracks. These defects can be described as poor edge sharpness, large edge position deviation from an established standard, and irregularities of the inside surface. Template matching is used for character detection and extraction. Matching performance measurements are used to evaluate closeness with respect to a reference print character of accepted quality. A hierarchical structure is used to improve the robustness of position detection and acceptable performance measures in knowledge rules are incorporated to increase the speed of the search. Characters are extracted from the image by a logical 'AND' operation in which a filled, slightly enlarged, uniform gray scale pattern of the print character is used as a template. Feature extraction and matching is done by using a distance image template matching technique which makes the system more robust and effective. Finally, a set of matching measurements is extracted to determine edge sharpness, edge deviation, and smoothness of the inside surface and local matching measurements are used for determining the detail of defects.

Paper Details

Date Published: 1 March 1991
PDF: 9 pages
Proc. SPIE 1468, Applications of Artificial Intelligence IX, (1 March 1991); doi: 10.1117/12.45506
Show Author Affiliations
Yong-Lin Hu, Univ. of Cincinnati (United States)
William G. Wee, Univ. of Cincinnati (United States)
William A. Gruver, Univ. of Kentucky (Canada)
Chia Yung Han, Univ. of Cincinnati (United States)

Published in SPIE Proceedings Vol. 1468:
Applications of Artificial Intelligence IX
Mohan M. Trivedi, Editor(s)

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