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Optical Engineering

Feature space trajectory for distorted-object classification and pose estimation in synthetic aperture radar
Author(s): David P. Casasent; Rajesh Shenoy
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Paper Abstract

Classification and pose estimation of distorted input objects are considered. The feature space trajectory representation of distorted views of an object is used with a new eigenfeature space. For a distorted input object, the closest trajectory denotes the class of the input and the closest line segment on it denotes its pose. If an input point is too far from a trajectory, it is rejected as clutter. New methods for selecting Fukunaga-Koontz discriminant vectors, the number of dominant eigenvectors per class and for determining training, and test set compatibility are presented.

Paper Details

Date Published: 1 October 1997
PDF: 20 pages
Opt. Eng. 36(10) doi: 10.1117/1.601520
Published in: Optical Engineering Volume 36, Issue 10
Show Author Affiliations
David P. Casasent, Carnegie Mellon Univ. (United States)
Rajesh Shenoy, Carnegie Mellon Univ. (United States)

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