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Proceedings Paper

New nonlinear features for inspection, robotics, and face recognition
Author(s): David P. Casasent; Ashit Talukder
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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. Other applications of these new feature spaces in robotics and face recognition are also noted.

Paper Details

Date Published: 1 October 1999
PDF: 11 pages
Proc. SPIE 3804, Algorithms, Devices, and Systems for Optical Information Processing III, (1 October 1999); doi: 10.1117/12.363953
Show Author Affiliations
David P. Casasent, Carnegie Mellon Univ. (United States)
Ashit Talukder, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 3804:
Algorithms, Devices, and Systems for Optical Information Processing III
Bahram Javidi; Demetri Psaltis, Editor(s)

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