Neural net selection methods for Gabor transform detection filters

David P. Casasent; John Scott Smokelin

doi:10.1117/12.150130

20 August 1993 Neural net selection methods for Gabor transform detection filters

David P. Casasent, John Scott Smokelin

Proceedings Volume 2055, Intelligent Robots and Computer Vision XII: Algorithms and Techniques; (1993) https://doi.org/10.1117/12.150130
Event: Optical Tools for Manufacturing and Advanced Automation, 1993, Boston, MA, United States

Abstract

New Gabor transform (GT) filters to detect candidate object locations independent of the object class, object distortions, and for low contrast objects in clutter are described. A new neural network (NN) technique is described to automate selection of GT parameters and to combine multiple Gabor functions (GFs) into once composite macro GF detection filter. Fusion of real and imaginary GT filter outputs is used to reduce false alarms, (P_FA), while maintaining high detection rates (P_D). Test results on the TRIM-2 database are provided.

Citation Download Citation

David P. Casasent and John Scott Smokelin "Neural net selection methods for Gabor transform detection filters", Proc. SPIE 2055, Intelligent Robots and Computer Vision XII: Algorithms and Techniques, (20 August 1993); https://doi.org/10.1117/12.150130

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