This paper studies the testing of the imaging systems and algorithms with the genetic algorithms. We test if there are inherent natural weaknesses in the image processing algorithm or system and can they are search and found with the evolutionary algorithms. In this paper, we test the weaknesses of the error diffusion halftoning methods. We also take a closer look at the method and identify why these weaknesses appear and are relatively easy to identify with synthetic test images. Moreover, we discuss the importance of comprehensive testing before the results with some image processing methods can be trustworthy. The results seem to suggest that the error diffusion methods do not have as apparent inherent problems as e.g. dispersed dot method, but the GA testing does reveal some other problems, like delayed response to the image tone changes. The different error diffusion methods have similar problems, but with different intensity.
We have studied the use of cellular automata and cellular genetic algorithms for the object recognition, pose recognition, and image classification problems. The cellular genetic algorithm is a genetic algorithm that has some similarities with cellular automata. The preliminary results seem to support the hypothesis that in principle this kind of object and pose recognition and image classification method works relatively well. The problem with the proposed method is a large amount of calculations needed when we are testing the unknown object against the objects in the comparison set.
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.
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.
In this study we use genetic algorithms to generate test surfaces for a proposed structured light 3D vision system in order to estimate the worst case behavior of error tolerances. The object software evaluates surface profiles for measuring volumes of small objects attached on surfaces that are highly constrained while somewhat arbitrarily shaped. The test system tries to find, by using genetic algorithm search, the shape that results the highest relative error of volume. The parameters of the object system to be optimized include laser angle, image size, object step size, and the number of scan directions. The preliminary results seem to indicate that a genetic algorithm based approach is a beneficial aid in optical system design.
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.
Digital halftoning is used both in low and high resolution high quality printing technologies. Our method is designed to be mainly used for low resolution ink jet marking machines to produce both gray tone and color images. The main problem with digital halftoning is pink noise caused by the human eye's visual transfer function. To compensate for this the random dot patterns used are optimized to contain more blue than pink noise. Several such dot pattern generator threshold matrices have been created automatically by using genetic algorithm optimization, a non-deterministic global optimization method imitating natural evolution and genetics. A hybrid of genetic algorithm with a search method based on local backtracking was developed together with several fitness functions evaluating dot patterns for rectangular grids. By modifying the fitness function, a family of dot generators results, each with its particular statistical features. Several versions of genetic algorithms, backtracking and fitness functions were tested to find a reasonable combination. The generated threshold matrices have been tested by simulating a set of test images using the Khoros image processing system. Even though the work was focused on developing low resolution marking technology, the resulting family of dot generators can be applied also in other halftoning application areas including high resolution printing technology.