Optical image segmentation using neural-based wavelet filtering techniques

Abstract

A neural-based optical image segmentation scheme for locating potential targets in cluttered FLIR images is presented. 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 biologically based orientation specific filters (wavelet filters) as its main component. These filters are the well-understood impulse response functions of mammalian vision systems from input to striate cortex. By using the proper choice of aperture pair separation, dilation, and orientation, targets in FLIR imagery were optically segmented. Wavelet filtering is illustrated for glass template slides, as well as segmentation for static and real-time FLIR imagery displayed on a liquid crystal television.