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Proceedings Paper

A High-Dimensionality Pattern Recognition Feature Space
Author(s): David Casasent; Hironobu Okuyama
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Paper Abstract

The use of optical Fourier transform and computer generated hologram (CGH) techniques allows a high-dimensionality feature space to be produced in parallel. By the proper coordinate transformation CGH, a position, rotation and shift invariant feature space results. The use of synthetic discriminant functions (SDF) and CGH techniques allows high-dimensionality of optical linear discriminant functions (LDFs) to be produced. These optical LDFs allow high-dimensionality and when designed by SDF techniques, 3-D distortion-invariance results. Initial simulation results using a ship image data base are presented.

Paper Details

Date Published: 11 December 1985
PDF: 13 pages
Proc. SPIE 0579, Intelligent Robots and Computer Vision IV, (11 December 1985); doi: 10.1117/12.950807
Show Author Affiliations
David Casasent, Carnegie-Mellon University (United States)
Hironobu Okuyama, Computer and Analysis Center (Japan)


Published in SPIE Proceedings Vol. 0579:
Intelligent Robots and Computer Vision IV
David P. Casasent, Editor(s)

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