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Proceedings Paper

Features for automatic surface inspection
Author(s): Joon Hee Han; Doo M. Yoon; Myeong K. Kang
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Paper Abstract

We will discuss about some simple features for automatic inspection of surfaces whose defect patterns are aggregations of irregular shapes. The description or classification of these defects is not an easy task. Two types of feature sets are studied--a set of features based on connected component labeling, and a set of local measurements that can be computed easily. As the first set, several region properties that can be computed from labeled binary images have been tested. Each of these features is weighted according to its variance and dependencies with respect to other. A classification method based on the minimum difference between the trained data and the distribution computed from an image of an unknown class have been used to test the feature set. As the second set, we have defined about 10 features that can be computed without labeling the binary image. By using classification method based on the distribution of feature values along with weighting factors, we have obtained a high rate of correct classification for 20 classes of complex natural images.

Paper Details

Date Published: 6 May 1993
PDF: 10 pages
Proc. SPIE 1907, Machine Vision Applications in Industrial Inspection, (6 May 1993); doi: 10.1117/12.144804
Show Author Affiliations
Joon Hee Han, Pohang Institute of Science and Technology (South Korea)
Doo M. Yoon, Pohang Institute of Science and Technology (South Korea)
Myeong K. Kang, Research Institute of Industrial Science and Technology (South Korea)

Published in SPIE Proceedings Vol. 1907:
Machine Vision Applications in Industrial Inspection
Frederick Y. Wu; Benjamin M. Dawson, Editor(s)

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