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Proceedings Paper

Searching strain field parameters by genetic algorithms
Author(s): Janne Koljonen; Timo Mantere; Olli Kanniainen; Jarmo T. Alander
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Paper Abstract

This paper studies the applicability of genetic algorithms and imaging to measure deformations. Genetic algorithms are used to search for the strain field parameters of images from a uniaxial tensile test. The non-deformed image is artificially deformed according to the estimated strain field parameters, and the resulting image is compared with the true deformed image. The mean difference of intensities is used as a fitness function. Results are compared with a node-based strain measurement algorithm developed by Koljonen et al. The reference method slightly outperforms the genetic algorithm as for mean difference of intensities. The root-mean-square difference of the displacement fields is less than one pixel. However, with some improvements suggested in this paper the genetic algorithm based method may be worth considering, also in other similar applications: Surface matching instead of individual landmarks can be used in camera calibration and image registration. Search of deformation parameters by genetic algorithms could be applied in pattern recognition tasks e.g. in robotics, object tracking and remote sensing if the objects are subject to deformation. In addition, other transformation parameters could be simultaneously looked for.

Paper Details

Date Published: 10 September 2007
PDF: 9 pages
Proc. SPIE 6764, Intelligent Robots and Computer Vision XXV: Algorithms, Techniques, and Active Vision, 67640O (10 September 2007); doi: 10.1117/12.751725
Show Author Affiliations
Janne Koljonen, Univ. of Vaasa (Finland)
Timo Mantere, Univ. of Vaasa (Finland)
Olli Kanniainen, Univ. of Vaasa (Finland)
Jarmo T. Alander, Univ. of Vaasa (Finland)

Published in SPIE Proceedings Vol. 6764:
Intelligent Robots and Computer Vision XXV: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall; Juha Röning, Editor(s)

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