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Proceedings Paper

Classification and pose estimation of objects using nonlinear features
Author(s): Ashit Talukder; David P. Casasent
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Paper Abstract

A new nonlinear feature extraction method called the maximum representation and discrimination feature (MRDF) method is presented for extraction of features from input image data. It implements transformations similar to the Sigma-Pi neural network. However, the weights of the MRDF are obtained in closed form, and offer advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We show its use in estimating the class and pose of images of real objects and rendered solid CAD models of machine parts from single views using a feature-space trajectory (FST) neural network classifier. We show more accurate classification and pose estimation results than are achieved by standard principal component analysis (PCA) and Fukunaga-Koontz (FK) feature extraction methods.

Paper Details

Date Published: 25 March 1998
PDF: 12 pages
Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998);
Show Author Affiliations
Ashit Talukder, Carnegie Mellon Univ. (United States)
David P. Casasent, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 3390:
Applications and Science of Computational Intelligence
Steven K. Rogers; David B. Fogel; James C. Bezdek; Bruno Bosacchi, Editor(s)

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