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Proceedings Paper

Image segmentation using trainable fuzzy set classifiers
Author(s): Robert J. Schalkoff; Albrecht E. Carver; Sabri Gurbuz
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Paper Abstract

A general image analysis and segmentation method using fuzzy set classification and learning is described. The method uses a learned fuzzy representation of pixel region characteristics, based upon the conjunction and disjunction of extracted and derived fuzzy color and texture features. Both positive and negative exemplars of some visually apparent characteristic which forms the basis of the inspection, input by a human operator, are used together with a clustering algorithm to construct positive similarity membership functions and negative similarity membership functions. Using these composite fuzzified images, P and N, are produced using fuzzy union. Classification is accomplished via image defuzzification, whereby linguistic meaning is assigned to each pixel in the fuzzy set using a fuzzy inference operation. The technique permits: (1) strict color and texture discrimination, (2) machine learning of color and texture characteristics of regions, (3) and judicious labeling of each pixel based upon leaned fuzzy representation and fuzzy classification. This approach appears ideal for applications involving visual inspection and allows the development of image-based inspection systems which may be trained and used by relatively unskilled workers. We show three different examples involving the visual inspection of mixed waste drums, lumber and woven fabric.

Paper Details

Date Published: 21 July 1999
PDF: 9 pages
Proc. SPIE 3716, Visual Information Processing VIII, (21 July 1999); doi: 10.1117/12.354700
Show Author Affiliations
Robert J. Schalkoff, Clemson Univ. (United States)
Albrecht E. Carver, Clemson Univ. (United States)
Sabri Gurbuz, Clemson Univ. (United States)

Published in SPIE Proceedings Vol. 3716:
Visual Information Processing VIII
Stephen K. Park; Richard D. Juday, Editor(s)

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