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Pixelization Paradigm

First Visual Information Expert Workshop, VIEW 2006, Paris, France, April 24-25, 2006, Revised Selected Papers

  • Editors
  • Pierre P Lévy
  • Bénédicte Le Grand
  • François Poulet
  • Michel Soto
  • Laszlo Darago
  • Laurent Toubiana
  • Jean-François Vibert

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4370)

Table of contents

  1. Front Matter
  2. Pixelization Theory

    1. Front Matter
      Pages 1-1
    2. Pixelization

    3. Pixelization and Multidimensional Data

      1. Yeow Wei Choong, Anne Laurent, Dominique Laurent
        Pages 63-76
      2. John T. Langton, Astrid A. Prinz, David K. Wittenberg, Timothy J. Hickey
        Pages 77-91
      3. Rodolphe Priam, Mohamed Nadif, François-Xavier Jollois
        Pages 110-118
  3. Pixelization Applications

    1. Front Matter
      Pages 119-119
    2. Spatial Pixelization

      1. Julien Montagner, Vincent Barra, Jean-Yves Boire
        Pages 121-136
      2. Lemlih Ouchchane, Alice Villéger, Jean-Jacques Lemaire, Jacques Demongeot, Jean-Yves Boire
        Pages 137-151
      3. Vipin K. Tripathi, Bhaskar Dasgupta, Kalyanmoy Deb
        Pages 152-163
    3. Temporal Pixelization

      1. Jean-François Vibert, Fabián Alvarez, José Pedro Segundo
        Pages 173-182
      2. Gaj Vidmar, Branimir L. Leskošek, Drago Rudel
        Pages 183-188
    4. Qualitative Pixelization

      1. Clara Le Guillou, Jean-Michel Cauvin, Basel Solaiman, Michel Robaszkiewicz, Christian Roux
        Pages 189-201
      2. Fabien Jourdan, Alain Paris, Pierre-Yves Koenig, Guy Melançon
        Pages 202-215
      3. László Daragó, Pierre P. Lévy, Anett Veres, Zsolt Kristóf
        Pages 216-224
  4. Pixelization and Cognition

    1. Front Matter
      Pages 225-225
    2. Jean-Gabriel Ganascia
      Pages 253-263
    3. Mariusz Trzaska, Kazimierz Subieta
      Pages 264-277
  5. Back Matter

About these proceedings

Introduction

The pixelization paradigm states as a postulate that pixelization methods are rich and are worth exploring as far as possible. In fact, we think that the strength of these methods lies in their simplicity, in their high-density way of information representation property and in their compatibility with neurocognitive processes. • Simplicity, because pixelization belongs to two-dimensional information visualization methods and its main idea is identifying a “pixel” with an informational entity in order to translate a set of informational entities into an image. • High-density way of information representation property, firstly because pixelization representation contains a third dimension—each pixel’s color—and secondly because pixelization is a “compact” (two-dimensional) way of representing information compared with linear one-dimensional representations (Ganascia, p.255) . • Compatibility with neurocognitive processes, firstly because we are thr- dimensional beings and thus we are intrinsically better at grasping one- or two-dimensional data, and secondly because the cerebral cortex is typically a bi-dimensional structure where metaphorically the neurons can be assimilated to “pixels,” whose activity plays the role of color (Lévy, p.3). The pixelization paradigm may be studied along two related directions: pixelization and its implementation and pixelization and cognition. The first direction—pixelization and its implementation—may be divided into two parts: pixelization theory and pixelization application.

Keywords

2D view 3D vision Layout Mapping bioinformatics classification cognition data mining databases feature extraction filtering image retrieval pattern analysis pattern recognition visualization

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-71027-1
  • Copyright Information Springer-Verlag Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-71026-4
  • Online ISBN 978-3-540-71027-1
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site