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Proceedings Paper

Testing digital halftoning filters by generating test images and filters coevolutionarily
Author(s): Timo J. Mantere; Jarmo T. Alander
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Paper Abstract

In this paper we evaluate the potential of using the co-evolutionary optimization method to automatically and concurrently generate halftoning filters and their test images. One genetic algorithm tries to generate the best halftone filters, while the other genetic algorithm tries to create the hardest test image for the filters. The best filter is the one for which the hardest test image, when dithered, differs least from the original. An image population defines the fitness of halftoning filters and vice versa.

Paper Details

Date Published: 30 September 2003
PDF: 12 pages
Proc. SPIE 5267, Intelligent Robots and Computer Vision XXI: Algorithms, Techniques, and Active Vision, (30 September 2003); doi: 10.1117/12.514704
Show Author Affiliations
Timo J. Mantere, Lappeenranta Univ. of Technology (Finland)
Jarmo T. Alander, Univ. of Vaasa (Finland)


Published in SPIE Proceedings Vol. 5267:
Intelligent Robots and Computer Vision XXI: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall; Juha Roning, Editor(s)

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