1 October 1997 Feature space trajectory for distorted-object classification and pose estimation in synthetic aperture radar
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Optical Engineering, 36(10), (1997). doi:10.1117/1.601520
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.
David P. Casasent, Rajesh Shenoy, "Feature space trajectory for distorted-object classification and pose estimation in synthetic aperture radar," Optical Engineering 36(10), (1 October 1997). http://dx.doi.org/10.1117/1.601520
JOURNAL ARTICLE
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KEYWORDS
Databases

Synthetic aperture radar

Polarization

Signal to noise ratio

Data modeling

Optical engineering

Image segmentation

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