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Journal ArticleDOI

Feature extractors for distortion- invariant robot vision

David P. Casasent, +1 more
- 01 Oct 1984 - 
- Vol. 23, Iss: 5, pp 492-498
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TLDR
The performance using intensity and phase Fourier transform features and the performance in the presence of noise are studied and quantified for two different two-class pattern recognition data bases.
Abstract
Various feature extractors/classifiers for a hierarchical feature-space pattern recognition system are described. The system is intended to achieve multiclass distortion-invariant object identification. Although only a Fourier transform feature space is used, our basic hierarchical concepts, our theoretical analysis, and our general conclusions are applicable to other feature spaces. The performance using intensity and phase Fourier transform features and the performance in the presence of noise are studied and quantified for two different two-class pattern recognition data bases.

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Citations
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Journal ArticleDOI

Diffraction pattern sampling using a computer-generated hologram.

TL;DR: A grating computer-generated hologram (CGH) to perform wedge and ring detector diffraction pattern sampling is discussed and several optical system solutions are suggested.
Journal ArticleDOI

Image classification at low light levels

TL;DR: In this article, the classification decision is formed on the basis of the cross correlation between a photon-limited input image and a reference function stored in computer memory, and expressions for the statistical parameters of the low-light-level correlation signal are given and verified experimentally.
Journal ArticleDOI

Surface classification by an optoelectronic implementation of the Karhunen-Loève expansion.

TL;DR: An optical-digital approach to the classification of rough surfaces that uses a Fourier-transform feature space is described, which automatically selects the best subspace from the Karhunen-Loève vectors.
Journal ArticleDOI

Image classification with a chirp-encoded joint transform correlator.

TL;DR: Analytical expressions, computer simulations, and optical experiments are presented to investigate the performance of the chirp-encoded joint transform correlator for image classification.
Proceedings ArticleDOI

A High-Dimensionality Pattern Recognition Feature Space

TL;DR: The use of optical Fourier transform and computer generated hologram (CGH) techniques allows a high-dimensionality feature space to be produced in parallel and initial simulation results using a ship image data base are presented.
References
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Book

Pattern classification and scene analysis

TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.