Feature extraction using morphological analysis of multiresolution gray-scale images
Louis A. Tamburino, Mateen M. Rizki, Michael A. Zmuda · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
In this paper, we discuss an initial effort to generate pattern recognizers using a multi- resolution Gabor stack of filtered images and a simple evolutionary search algorithm. The generated feature detectors are sets of pixel detectors that measure intensities and pass these values as feature vectors to neural net classifiers. We demonstrate the use of random search to solve a discrimination problem in which tank images are separated from other military vehicle images. The techniques and results used in this paper for discrimination of grey-scale images are reminiscent of similar approaches used to generate pattern recognizers for binary images. A sparse sampling of the Gabor image stack, using only 35 pixel detectors, produces feature vectors which are readily separated by linear perceptrons.