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Proceedings Paper

Neural net design of Gabor wavelet filters for distortion-invariant object detection in clutter
Author(s): David P. Casasent; John Scott Smokelin
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Paper Abstract

We consider the detection of multiclasses of objects in clutter with 3D object distortions and contrast differences present. We use a correlator since shift invariance is necessary to handle an object whose location is not known and to handle multiple objects. The detection filter used is a linear combination of the real part of different Gabor filters which we refer to as a macro Gabor filter (MGF). A new analysis of the parameters for the initial set of Gabor functions in the MGF is given a new neural net algorithm to refine these initial filter parameters and to determine the combination coefficients to produce the final MGF detection filter are detailed. Initial detection results are given. Use of this general neural net technique to design correlation filters seems very attractive for this and other applications.

Paper Details

Date Published: 15 March 1994
PDF: 12 pages
Proc. SPIE 2242, Wavelet Applications, (15 March 1994); doi: 10.1117/12.170067
Show Author Affiliations
David P. Casasent, Carnegie Mellon Univ. (United States)
John Scott Smokelin, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 2242:
Wavelet Applications
Harold H. Szu, Editor(s)

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