<title>Target detection using co-occurrence matrix segmentation and its hardware implementation</title>

Abstract

A number of acquisition, tracking, and classification algorithms have been developed to deal with various image processing problems in the laboratory. Typically these are too complicated to implement in a low-cost, real-time processor. Using image data in many real-time applications requires a system with very high data rates, low power dissipation, and a small packaging volume. A processor architecture suitable for these applications have been developed, and a co-occurrence matrix target detection algorithm adapted and demonstrated in computer simulation and real-time hardware. A histogram or gray-level distribution is often used to select a threshold for image segmentation. This is often inadequate, as the histograms tend to be noisy and exhibit many small peaks. Co-occurrence matrix based segmentation allows homogeneous regions of an image to be identified and separated from a cluttered background. Results are shown for target segmentation using representative infrared imagery and real-time hardware.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.