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Proceedings Paper

Design of minimum entropy wavelet filters using genetic algorithms
Author(s): Warren J. Jasper; Jeff Joines
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Paper Abstract

This paper presents a method to design a wavelet-filter that minimizes entropy in the wavelet transform. Filters that minimize entropy in images tend to filter out texture while highlighting features of interest. The design of the wavelet filter is couched as a non-convex optimization problem which is solved using a hybridized Genetic Algorithm. As an example, three distinct filters are tuned to detect horizontal, vertical and blob defects in woven fabrics. The effects of shifting on the optimized set of coefficients is also explored.

Paper Details

Date Published: 7 November 2005
PDF: 15 pages
Proc. SPIE 6001, Wavelet Applications in Industrial Processing III, 60010G (7 November 2005); doi: 10.1117/12.630604
Show Author Affiliations
Warren J. Jasper, North Carolina State Univ. (United States)
Jeff Joines, North Carolina State Univ. (United States)

Published in SPIE Proceedings Vol. 6001:
Wavelet Applications in Industrial Processing III
Frederic Truchetet; Olivier Laligant, Editor(s)

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