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Proceedings Paper

Automatic image generation by genetic algorithms for testing halftoning methods
Author(s): Timo J. Mantere; Jarmo T. Alander
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Paper Abstract

Automatic test image generation by genetic algorithms is introduced in this work. In general the proposed method has potential in functional software testing. This study was done by joining two different projects: the first one concentrates on software test data generation by genetic algorithms and the second one studied digital halftoning for an ink jet marking machine also by genetic algorithm optimization. The object software halftones images with different image filters. The goal was to reveal, if genetic algorithm is able to generate images that re difficult for the object software to halftone, in other words to find if some prominent characteristics of the original image disappear or ghost images appear due to the halftoning process. The preliminary results showed that genetic algorithm is able to find images that are considerable changed when halftoned, and thus reveal potential problems with the halftoning method, i.e. essentially tests for errors in the halftoning software.

Paper Details

Date Published: 11 October 2000
PDF: 12 pages
Proc. SPIE 4197, Intelligent Robots and Computer Vision XIX: Algorithms, Techniques, and Active Vision, (11 October 2000); doi: 10.1117/12.403775
Show Author Affiliations
Timo J. Mantere, Turku Ctr. for Computer Science (Finland)
Jarmo T. Alander, Univ. of Vaasa (Finland)


Published in SPIE Proceedings Vol. 4197:
Intelligent Robots and Computer Vision XIX: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)

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