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Optical Engineering

Feature space trajectory distorted object representation for classification and pose estimation
Author(s): David P. Casasent; Leonard Neiberg; Michael A. Sipe
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Paper Abstract

The feature space trajectory (FST) neural net is used for classification and pose estimation of the contents of regions of interest. The FST provides an attractive representation of distorted objects that overcomes problems present in other classifiers. We discuss its use in rejecting clutter inputs, selecting the number and identity of the aspect views most necessary to represent an object, and to distinguish between two objects, temporal image processing, automatic target recognition, and active vision.

Paper Details

PDF: 10 pages
Opt. Eng. 37(3) doi: 10.1117/1.602017
Published in: Optical Engineering Volume 37, Issue 3, March 1998
Show Author Affiliations
David P. Casasent, Carnegie Mellon Univ. (United States)
Leonard Neiberg, Carnegie Mellon Univ. (United States)
Intel Corp. (United States)
Michael A. Sipe, Carnegie Mellon Univ. (United States)

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