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

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

doi:10.1117/12.148105

1 November 1993 Target detection by co-occurrence matrix segmentation and its hardware implementation

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

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Optical Engineering, Vol. 32, Issue 11, (November 1993). https://doi.org/10.1117/12.148105

Abstract

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.

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

Published: 1 November 1993

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