1 November 1993 Target detection by co-occurrence matrix segmentation and its hardware implementation
Author Affiliations +
Optical Engineering, 32(11), (1993). doi:10.1117/12.148105
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.
John Eric Auborn, James Martin Fuller, Howard M. McCauley, "Target detection by co-occurrence matrix segmentation and its hardware implementation," Optical Engineering 32(11), (1 November 1993). http://dx.doi.org/10.1117/12.148105

Image segmentation

Target detection

Image processing

Signal processing


Detection and tracking algorithms

Algorithm development

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