<title>Optical image segmentor using wavelet filtering techniques as the front-end of a neural network classifier</title>

Abstract

This paper presents an automatic, optically based image segmentation scheme for locating potential targets in cluttered FLIR images as the front-end image processor of a neural network classifier. The advantage of such a scheme is speed, i.e., the speed of light. Such a design is critical to achieve real-time segmentation and classification for machine vision applications. The segmentation scheme used was based on texture discrimination and employed orientation specific, bandpass spatial filters (wavelet filters) as its main component. By using the proper choice of aperture pair separation, dilation, and orientation, potential targets in FLIR imagery can be optically segmented using spatial filtering techniques. Segmentation is illustrated for glass template slides, and static and real-time FLIR imagery displayed on an LCTV.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.