Searching strain field parameters by genetic algorithms

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.

Image comparison based on morphological transforms

“…We seek to develop image comparison algorithms that exclude prior teaching to avoid limiting the system to recognizing only cases for which it was trained beforehand. 1,2 Our solution is to use morphology transforms based on a cellular genetic algorithm (CGA) that limits neither image comparison nor object recognition. The advantage of such a system is that it can be fed any group of images for processing.…”