Neural net design of Gabor wavelet filters for distortion-invariant object detection in clutter

David P. Casasent; John Scott Smokelin

doi:10.1117/12.170067

15 March 1994 Neural net design of Gabor wavelet filters for distortion-invariant object detection in clutter

David P. Casasent, John Scott Smokelin

Proceedings Volume 2242, Wavelet Applications; (1994) https://doi.org/10.1117/12.170067
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

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.

Citation Download Citation

David P. Casasent and John Scott Smokelin "Neural net design of Gabor wavelet filters for distortion-invariant object detection in clutter", Proc. SPIE 2242, Wavelet Applications, (15 March 1994); https://doi.org/10.1117/12.170067

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