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Proceedings Paper

Learning to generate noise-removal operators
Author(s): Ronlon Tsai; Shu-Yuen Hwang
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Paper Abstract

Almost all existing noise-removal operators are constructed based on the assumption that noises have some form of distributions. However, the assumption is usually questionable or even not correct in dealing with real images. This paper presents a learning technique for generating structuring elements of morphological operators which is used to remove noises in fingerprint images. The learning technique is based on the genetic procedure, with the chromosome representing the structural elements of the morphological operators. On each iteration of the genetic procedure, some new structural elements will be generated. The usefulness of these elements are evaluated by the quality of resulting images of applying the corresponding morphological operators, followed by a thinning operation, to the fingerprints. Several factors, such as the number branching points, the number of end points, and the speed of convergence of thinning operation are considered in the evaluated formula. The best structuring elements are then selected as the desired one. No assumption about the distributions of the noises are made. Although the domain is very specific, the technique is general enough for learning structuring elements of morphological operators used in other applications. The generality is achieved by changing the evaluation rule such that all factors potentially affecting the result of applying the morphological operators can be considered.

Paper Details

Date Published: 1 March 1992
PDF: 12 pages
Proc. SPIE 1615, Machine Vision Architectures, Integration, and Applications, (1 March 1992); doi: 10.1117/12.58812
Show Author Affiliations
Ronlon Tsai, Industrial Technology Research Institute (Taiwan)
Shu-Yuen Hwang, National Chiao Tung Univ. (Taiwan)

Published in SPIE Proceedings Vol. 1615:
Machine Vision Architectures, Integration, and Applications
Bruce G. Batchelor; Michael J. W. Chen; Frederick M. Waltz, Editor(s)

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