Target detection using co-occurrence matrix segmentation and its hardware implementation

John Eric Auborn; James Martin Fuller Jr.; Howard M. McCauley

doi:10.1117/12.45700

1 August 1991 Target detection using co-occurrence matrix segmentation and its hardware implementation

John Eric Auborn, James Martin Fuller Jr., Howard M. McCauley

Proceedings Volume 1482, Acquisition, Tracking, and Pointing V; (1991) https://doi.org/10.1117/12.45700
Event: Orlando '91, 1991, Orlando, FL, United States

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.

Citation Download Citation

John Eric Auborn, James Martin Fuller Jr., and Howard M. McCauley "Target detection using co-occurrence matrix segmentation and its hardware implementation", Proc. SPIE 1482, Acquisition, Tracking, and Pointing V, (1 August 1991); https://doi.org/10.1117/12.45700

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