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

We consider the detection of multiple classes of objects in clutter with 3-D object distortions and contrast differences present. We use a correlator because shift invariance is necessary to locate and recognize one object whose position is not known and to handle multiple objects in the same scene. 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, and a new neural network algorithm to refine these initial filter parameters and to determine the combination coefficients to produce the final MGF detection filter is detailed. Initial detection results are given. Use of this general neural network technique to design correlation filters for other applications seems very attractive.