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Proceedings Paper

New feature extraction method for classification of agricultural products from x-ray images
Author(s): Ashit Talukder; David P. Casasent; Ha-Woon Lee; Pamela M. Keagy; Thomas F. Schatzki
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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. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discrimination between damaged and clean items. 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. The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC data.

Paper Details

Date Published: 14 January 1999
PDF: 12 pages
Proc. SPIE 3543, Precision Agriculture and Biological Quality, (14 January 1999); doi: 10.1117/12.336874
Show Author Affiliations
Ashit Talukder, Carnegie Mellon Univ. (United States)
David P. Casasent, Carnegie Mellon Univ. (United States)
Ha-Woon Lee, Carnegie Mellon Univ. (South Korea)
Pamela M. Keagy, USDA Agricultural Research Service (United States)
Thomas F. Schatzki, USDA Agricultural Research Service (United States)


Published in SPIE Proceedings Vol. 3543:
Precision Agriculture and Biological Quality
George E. Meyer; James A. DeShazer, Editor(s)

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