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Proceedings Paper

Nonlinear features for product inspection
Author(s): Ashit Talukder; David P. Casasent
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Paper Abstract

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. We discuss the extraction of new features that allow better discrimination between damaged and clean items (pistachio nuts). This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discriminating feature (MRDF) extraction method computes nonlinear features that are used as inputs to a new modified k nearest neighbor classifier. In this work, the MRDF is applied to standard features (rather than iconic data). The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC (receiver operating characteristic) data.

Paper Details

Date Published: 9 March 1999
PDF: 12 pages
Proc. SPIE 3715, Optical Pattern Recognition X, (9 March 1999); doi: 10.1117/12.341305
Show Author Affiliations
Ashit Talukder, Carnegie Mellon Univ. (United States)
David P. Casasent, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 3715:
Optical Pattern Recognition X
David P. Casasent; Tien-Hsin Chao, Editor(s)

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