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Proceedings Paper

Classification of product inspection items using nonlinear features
Author(s): Ashit Talukder; David P. Casasent; H.-W. Lee
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Paper Abstract

Automated processing and classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non-invasive detection of defective product items on a conveyor belt. This approach involves two main steps: preprocessing and classification. Preprocessing locates individual items and segments ones that touch using a modified watershed algorithm. The second stage involves extraction of features that allow discrimination between damaged and clean items (pistachio nuts). This feature extraction and classification stage is the new aspect of this paper. We use a new nonlinear feature extraction scheme called the maximum representation and discriminating feature (MRDF) extraction method to compute nonlinear features that are used as inputs to a classifier. The MRDF is shown to provide better classification and a better ROC (receiver operating characteristic) curve than other methods.

Paper Details

Date Published: 23 March 1998
PDF: 12 pages
Proc. SPIE 3386, Optical Pattern Recognition IX, (23 March 1998); doi: 10.1117/12.304761
Show Author Affiliations
Ashit Talukder, Carnegie Mellon Univ. (United States)
David P. Casasent, Carnegie Mellon Univ. (United States)
H.-W. Lee, Dongyang Univ. (South Korea)

Published in SPIE Proceedings Vol. 3386:
Optical Pattern Recognition IX
David P. Casasent; Tien-Hsin Chao, Editor(s)

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