Automatic image generation by genetic algorithms for testing halftoning methods

Timo J. Mantere; Jarmo T. Alander

doi:10.1117/12.403775

11 October 2000 Automatic image generation by genetic algorithms for testing halftoning methods

Timo J. Mantere, Jarmo T. Alander

Proceedings Volume 4197, Intelligent Robots and Computer Vision XIX: Algorithms, Techniques, and Active Vision; (2000) https://doi.org/10.1117/12.403775
Event: Intelligent Systems and Smart Manufacturing, 2000, Boston, MA, United States

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.

Citation Download Citation

Timo J. Mantere and Jarmo T. Alander "Automatic image generation by genetic algorithms for testing halftoning methods", Proc. SPIE 4197, Intelligent Robots and Computer Vision XIX: Algorithms, Techniques, and Active Vision, (11 October 2000); https://doi.org/10.1117/12.403775

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