A High-Dimensionality Pattern Recognition Feature Space

David Casasent; Hironobu Okuyama

doi:10.1117/12.950807

11 December 1985 A High-Dimensionality Pattern Recognition Feature Space

David Casasent, Hironobu Okuyama

Proceedings Volume 0579, Intelligent Robots and Computer Vision IV; (1985) https://doi.org/10.1117/12.950807
Event: 1985 Cambridge Symposium, 1985, Cambridge, United States

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.

Citation Download Citation

David Casasent and Hironobu Okuyama "A High-Dimensionality Pattern Recognition Feature Space", Proc. SPIE 0579, Intelligent Robots and Computer Vision IV, (11 December 1985); https://doi.org/10.1117/12.950807

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