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Superiorization for Image Analysis

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Combinatorial Image Analysis (IWCIA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8466))

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Abstract

Many scientific, engineering and medical applications of image analysis use constrained optimization, with the constraints arising from the desire to produce a solution that is constraints-compatible. It is typically the case that a large number of solutions would be considered good enough from the point of view of being constraints-compatible. In such a case, an optimization criterion is introduced that helps us to distinguish the “better” constraints-compatible solutions. The superiorization methodology is a recently-developed heuristic approach to constrained optimization. The underlying idea is that in many applications there exist computationally-efficient iterative algorithms that produce solutions that are constraints-compatible. Often the algorithm is perturbation resilient in the sense that, even if certain kinds of changes are made at the end of each iterative step, the algorithm still produces a constraints-compatible solution. This property is exploited in superiorization by using such perturbations to steer the algorithm to a solution that is not only constraints-compatible, but is also desirable according to a specified optimization criterion. The approach is very general, it is applicable to many iterative procedures and optimization criteria. Most importantly, superiorization is a totally automatic procedure that turns an iterative algorithm into its superiorized version. This, and its practical consequences in various application areas, have been investigated for a variety of constrained optimization tasks.

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Author information

Authors and Affiliations

  1. Computer Science PhD Program, Graduate Center, City University of New York, 365 Fifth Avenue, New York, NY, 10016, U.S.A.

    Gabor T. Herman

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  1. Gabor T. Herman
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Editors and Affiliations

  1. Department of Computer and Information Sciences, State University of New York at Fredonia, 280 Central Ave, 14063, Fredonia, NY, USA

    Reneta P. Barneva

  2. Mathematics Department, SUNY Buffalo State College, 14222, Buffalo, NY, USA

    Valentin E. Brimkov

  3. Department of Mathematics, Brno University of Technology, 616 69, Brno, Czech Republic

    Josef Šlapal

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© 2014 Springer International Publishing Switzerland

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Herman, G.T. (2014). Superiorization for Image Analysis. In: Barneva, R.P., Brimkov, V.E., Šlapal, J. (eds) Combinatorial Image Analysis. IWCIA 2014. Lecture Notes in Computer Science, vol 8466. Springer, Cham. https://doi.org/10.1007/978-3-319-07148-0_1

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  • DOI: https://doi.org/10.1007/978-3-319-07148-0_1

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07147-3

  • Online ISBN: 978-3-319-07148-0

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