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8 July 1998 Mathematical morphology enhancement of maximum entropy thresholding for small targets
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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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Paul J. Kemper Jr. "Mathematical morphology enhancement of maximum entropy thresholding for small targets", Proc. SPIE 3389, Hybrid Image and Signal Processing VI, (8 July 1998);

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