A number of acquisition, tracking, and classification algorithms have been developed to deal with various image processing problems in the laboratory. Typically, these algorithms 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. We developed a processor architecture suitable for these applications, and adapted and demonstrated a co-occurrence matrix target detection algorithm in computer simulation and real-time hardware. A histogram, or gray-level distribution, is often used to select a threshold for image segmentation. This technique is often inadequate because 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.