Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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  • Publication
    Tiny GAs for image processing applications
    ( 2006)
    Köppen, M.
    ;
    Franke, K.
    ;
    Vicente-Garcia, R.
    The expedience of today's image-processing applications is no longer based on the performance of a single algorithm alone. These systems appear to be complex frameworks with a lot of sub-tasks that are solved by specific algorithms, adaptation procedures, data handling, scheduling, and parameter choices. The venture of using computational intelligence (CI) in such a context, thus, is not a matter of a single approach. Among the great choice of techniques to inject CI in an image-processing framework, the primary focus of this presentation will be on the usage of so-called Tiny-GAs. This stands for an evolutionary procedure with low efforts, i.e. small population size (like 10 individuals), little number of generations, and a simple fitness. Obviously, this is not suitable for solving highly complete: optimization tasks, but the primary interest here is not the best individual's fitness, but the fortune of the algorithm and its population, which has just escaped the Monte-Carlo domain after random initialization. That this approach can work in practice will be demonstrated by means of selected image-processing applications, especially in the context of linear regression and line fitting; evolutionary post processing of various clustering results, in order to select a most suitable one by similarity; and classification by the fitness values obtained after a few generations.
  • Publication
    Soft-biometrics: Soft-computing for biometric-applications
    ( 2002)
    Franke, K.
    ;
    Ruiz del Solar, J.
    ;
    Köppen, M.
    A biometric system testifies the authenticity of a specific physiological or behavioral characteristic possessed by a user. New requirements for biometric systems such as robustness, higher recognition rates, tolerance for imprecision and uncertainty, and flexibility call for the use of new computing technologies. Soft- computing is increasingly being used in the development of biometric applications. Soft-biometrics corresponds to a new emerging paradigm that consists in the use of soft-computing technologies for the development of biometric applications. The aim of this paper is to motivate discussion on the application of soft-computing approaches to specific biometric measurements. The feasibility of soft-computing as a tool-set to biometric applications should be investigated. Finally, an application example on static signature verification is presented, providing evidence of the impact of soft-computing in biometrics.
  • Publication
    Pattern recognition and image analysis
    ( 2001)
    Köppen, M.
    ;
    Franke, K.
    ;
    Unold, O.
  • Publication
    Remarks on a recent paper on the "no free lunch" theorems
    ( 2001)
    Köppen, M.
    ;
    Wolpert, D.H.
    ;
    MacReady, W.G.
    This letter discusses the recent paper "Some technical remarks on the proof of the 'No Free Lunch' theorem," In that paper, some technical issues related to the formal proof of the no free lunch (NFL) theorem for search were given by Wolpert and Macready (1995 and 1997). As a result of a discussion among the authors, this letter explores the issues raised in that paper more thoroughly. This includes the presentation of a simpler version of the NFL proof in accord with a suggestion made explicitly by Koppen (2000) and implicitly by Wolpert and Macready (1997). It also includes the correction of an incorrect claim made by Koppen (2000) of a limitation of the NFL theorem. Finally, some thoughts on future research directions for research into algorithm performance are given.
  • Publication
    Steady-state image processing
    ( 2001)
    Köppen, M.
    ;
    Ruiz del Solar, J.
    This paper presents a new approach to the application mode of image processing operators, the so-called steady-state image processing. The approach reminds a steady-state genetic processing of images by considering each pixel of the image as an individual. So, some pixels are selected, processed and copied back into the image. This differs from the standard approach, where all image pixels are processed at once. The proposed approach offers many choices for variation, and allows for the assignment of dynamic measures to images. This will serve new families of soft computing methods as, e.g. immune-based algorithms, which need images as non-static objects in order to fulfill reasonable tasks. This paper also introduces some basic steady-state operators and exemplifies the analysis of an image by means of a small example. Also, it is shown how steady-state image processing can be applied in the context of texture segmentation. Steady-state image processing can be considered a way of processing images, which is deeply inspired by genetic algorithms.
  • Publication
  • Publication
    Automatisierung der Rohr- und Kanalinspektion
    ( 1997)
    Köppen, M.
    ;
    Nowack, C.