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Proceedings Paper

Fuzzy thresholding and linking for wavelet-based edge detection in images
Author(s): Arthur Johnson; Ching-Chung Li
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Paper Abstract

The application of wavelet transforms to edge detection has improved edge localization. The image produced by the local maxima of the wavelet modulus needs to be thresholded to extract out the relevant edge pixels. This is currently done manually. In this paper, we apply a fuzzy thresholding approach for automatic determination of the threshold level for wavelet maxima. A membership function is used to determine the characterization of the candidate edges based on a particular threshold. The threshold which yields the best characteristic or lowest uncertainty is selected. Non-crisp thresholding is achieved by re-evaluating edge pixel membership values to identify those pixels that may have been improperly classified. This results in the closure of small gaps between edge segments and a reduction in the size and number of larger gaps. For disjoint edge segments with a separation of less than six pixels, their endpoints can be linked by fuzzy reasoning based on membership values, distance, and their wavelet angles. Experimental results on test images have demonstrated the effectiveness of this method.

Paper Details

Date Published: 13 October 1997
PDF: 11 pages
Proc. SPIE 3165, Applications of Soft Computing, (13 October 1997); doi: 10.1117/12.279601
Show Author Affiliations
Arthur Johnson, Univ. of Pittsburgh (United States)
Ching-Chung Li, Univ. of Pittsburgh (United States)


Published in SPIE Proceedings Vol. 3165:
Applications of Soft Computing
Bruno Bosacchi; James C. Bezdek; David B. Fogel, Editor(s)

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