Image Processing, Computer Vision, Pattern Recognition, and Graphics

Pixelization Paradigm

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

Editors: Lévy, P.P., Le Grand, B., Poulet, F., Soto, M., Darago, L., Toubiana, L., Vibert, J.-F. (Eds.)

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About this book

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.

Table of contents (23 chapters)

Table of contents (23 chapters)
  • Pixelization Paradigm: Outline of a Formal Approach

    Pages 3-11

    Lévy, Pierre P.

  • Scalable Pixel Based Visual Data Exploration

    Pages 12-24

    Keim, Daniel A. (et al.)

  • High Dimensional Visual Data Classification

    Pages 25-34

    Poulet, François

  • Using Biclustering for Automatic Attribute Selection to Enhance Global Visualization

    Pages 35-47

    Abdullah, Ahsan (et al.)

  • Pixelisation-Based Statistical Visualisation for Categorical Datasets with Spreadsheet Software

    Pages 48-54

    Vidmar, Gaj

Buy this book

eBook $74.99
price for USA in USD
  • ISBN 978-3-540-71027-1
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $99.00
price for USA in USD
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Bibliographic Information

Bibliographic Information
Book Title
Pixelization Paradigm
Book Subtitle
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-Francois Vibert
Series Title
Image Processing, Computer Vision, Pattern Recognition, and Graphics
Series Volume
4370
Copyright
2007
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-540-71027-1
DOI
10.1007/978-3-540-71027-1
Softcover ISBN
978-3-540-71026-4
Edition Number
1
Number of Pages
XV, 288
Topics