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Proceedings Paper

Morphological algorithm development case study: detection of shapes in low-contrast gray-scale images with replacement and clutter noise
Author(s): Larry R. Rystrom; Philip L. Katz; Robert M. Haralick; Christian J. Eggen
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Paper Abstract

This paper presents a case study of the design of a fully autonomous morphological detection algorithm. Grayscale input images contain objects to be detected among difficult clutter, replacement noise, and background tilt. The criteria for choosing algorithm structure is included, with associated grayscale and binary structuring elements based upon comparing the geometry of target and noise/clutter objects. Background cancellation is discussed, along with histogram-based techniques for final thresholding to binary detection images. Finally a performance characterization methodology for the detection algorithm is presented. In addition to conventional detection statistics, the authors consider the 'quality' of the hits and false alarms, vis-a-vis the feature set and classifier used in classification downstream of the detector in the overall system design.

Paper Details

Date Published: 1 April 1992
PDF: 18 pages
Proc. SPIE 1658, Nonlinear Image Processing III, (1 April 1992); doi: 10.1117/12.58368
Show Author Affiliations
Larry R. Rystrom, Univ. of Washington (United States)
Philip L. Katz, Univ. of Washington (United States)
Robert M. Haralick, Univ. of Washington (United States)
Christian J. Eggen, Univ. of Washington (United States)

Published in SPIE Proceedings Vol. 1658:
Nonlinear Image Processing III
Edward R. Dougherty; Jaakko T. Astola; Charles G. Boncelet Jr., Editor(s)

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