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Proceedings Paper

Mathematical morphology enhancement of maximum entropy thresholding for small targets
Author(s): Paul J. Kemper
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Paper Abstract

The author shows that mathematical morphology imaging filter techniques enhance the effectiveness and versatility of maximum entropy thresholding in separating foreground and background, especially for small targets. Mathematical morphological image processing techniques, specifically openings and closing, tend to set large areas of a gray- level image to the same gray-level while preserving the number of gray-levels present in small areas, i.e., small targets. In an entropic analysis of the image, this equates to minimizing the entropy of the areas set to identical gray-levels, while conversely enhancing that of small, information-rich regions. Maximum entropy thresholding entropy contribution of each gray-level. Thus, prefiltering an image using an opening or closing operation immensely improves maximum entropy thresholding. Examples of this combined technique are shown for both one- and two- dimensional entropic thresholding. The author points to this synergism as an example of the inherent interconnectedness of image processing and thresholding algorithms, and emphasizes the importance of the analysis of combined algorithms in the design of target detection and tracking schemes.

Paper Details

Date Published: 8 July 1998
PDF: 8 pages
Proc. SPIE 3389, Hybrid Image and Signal Processing VI, (8 July 1998); doi: 10.1117/12.316531
Show Author Affiliations
Paul J. Kemper, Georgia Tech Research Institute (United States)

Published in SPIE Proceedings Vol. 3389:
Hybrid Image and Signal Processing VI
David P. Casasent; Andrew G. Tescher, Editor(s)

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