Abstract

We describe an approach to compute filters that automatically performs a spatial frequency selection to improve interclass discrimination and to reduce intraclass sensitivity. This approach is achieved by using as input to the filter synthesis a set of reference images to compute the filters and a set of distorted images to introduce the distortion or noise model of the reference images. Simulation results of correlation examples are provided for two pattern-recognition problems and are compared with the ones obtained with the standard minimum average correlation energy filters.

© 1993 Optical Society of America

Nonlinear cascaded correlation processes to improve the performances of automatic spatial-frequency-selective filters in pattern recognition

Frank Dubois
Appl. Opt. 35(23) 4589-4597 (1996)

Nonredundant filters for pattern recognition and orientation estimation of rotated images

Frank Dubois
Appl. Opt. 30(11) 1388-1395 (1991)

Performance evaluation of minimum average correlation energy filters

A. Mahalanobis and David P. Casasent
Appl. Opt. 30(5) 561-572 (1991)

Pattern recognition with a digital holographic microscope working in partially coherent illumination

F. Dubois, C. Minetti, O. Monnom, C. Yourassowsky, J.-C. Legros, and P. Kischel
Appl. Opt. 41(20) 4108-4119 (2002)

Reduction in correlation-filter sensitivity to background clutter by the automatic spatial frequency selection algorithm

Christophe Minetti and Frank Dubois
Appl. Opt. 35(11) 1900-1903 (1996)