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Mathematical Methods for Signal and Image Analysis and Representation

  • Book
  • © 2012

Overview

  • Provides a unique compilation of mathematical methodologies, both statistical as well as deterministic, for a variety of applications in signal and image analysis, and offers insight by emphasizing conceptual relations and formal analogies
  • May serve as a source of inspiration for anyone seeking a solid foundation for applications in computer vision or medical imaging, or models of visual perception
  • Originates from a EURANDOM workshop and thus presents state-of-the-art quality research
  • Includes supplementary material: sn.pub/extras

Part of the book series: Computational Imaging and Vision (CIVI, volume 41)

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Table of contents (16 chapters)

Keywords

About this book

Mathematical Methods for Signal and Image Analysis and Representation presents the mathematical methodology for generic image analysis tasks. In the context of this book an image may be any m-dimensional empirical signal living on an n-dimensional smooth manifold (typically, but not necessarily, a subset of spacetime). The existing literature on image methodology is rather scattered and often limited to either a deterministic or a statistical point of view. In contrast, this book brings together these seemingly different points of view in order to stress their conceptual relations and formal analogies.

Furthermore, it does not focus on specific applications, although some are detailed for the sake of illustration, but on the methodological frameworks on which such applications are built, making it an ideal companion for those seeking a rigorous methodological basis for specific algorithms as well as for those interested in the fundamental methodology per se.

Covering many topics at the forefront of current research, including anisotropic diffusion filtering of tensor fields, this book will be of particular interest to graduate and postgraduate students and researchers in the fields of computer vision, medical imaging and visual perception.

Editors and Affiliations

  • Dept. Mathematics & Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

    Luc Florack, Remco Duits

  • Dept. Applied Mathematics, Delft University of Technology, Delft, The Netherlands

    Geurt Jongbloed

  • Probability & Stochastic Networks (PNA), Centrum Wiskunde & Informatica, Amsterdam, The Netherlands

    Marie-Colette Lieshout

  • Fakultät für Mathematik, Universität Duisburg-Essen, Essen, Germany

    Laurie Davies

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