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Feature space trajectory for distorted-object classification and pose estimation in synthetic aperture radar

[+] Author Affiliations
David Casasent, Rajesh Shenoy

Carnegie Mellon University, Department of Electrical and Computer Engineering, Pittsburgh, Pennsylvania 15213

Opt. Eng. 36(10), 2719-2728 (Oct 01, 1997). doi:10.1117/1.601520
History: Received Feb. 11, 1997; Revised Apr. 12, 1997; Accepted May 23, 1997
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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. © 1997 Society of Photo-Optical Instrumentation Engineers.

© 1997 Society of Photo-Optical Instrumentation Engineers

Citation

David Casasent and Rajesh Shenoy
"Feature space trajectory for distorted-object classification and pose estimation in synthetic aperture radar", Opt. Eng. 36(10), 2719-2728 (Oct 01, 1997). ; http://dx.doi.org/10.1117/1.601520


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