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Pixelization Paradigm

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

  • Conference proceedings
  • © 2007

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Editors:
  1. Pierre P Lévy
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  2. Bénédicte Grand
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  3. François Poulet
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  4. Michel Soto
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  5. Laszlo Darago
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  6. Laurent Toubiana
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  7. Jean-François Vibert
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 4370)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: VIEW 2006.

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Table of contents (23 papers)

  1. Front Matter

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  2. Pixelization Theory

    1. Front Matter

      Pages 1-1
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    2. Pixelization Paradigm: Outline of a Formal Approach

      • Pierre P. Lévy
      Pages 3-11

    3. Pixelization

      1. Scalable Pixel Based Visual Data Exploration
        • Daniel A. Keim, Jörn Schneidewind, Mike Sips
        Pages 12-24
      2. High Dimensional Visual Data Classification
        • François Poulet
        Pages 25-34
      3. Using Biclustering for Automatic Attribute Selection to Enhance Global Visualization
        • Ahsan Abdullah, Amir Hussain
        Pages 35-47
      4. Pixelisation-Based Statistical Visualisation for Categorical Datasets with Spreadsheet Software
        • Gaj Vidmar
        Pages 48-54
      5. Dynamic Display of Turnaround Time Via Interactive 2D Images
        • Peter Gershkovich, Alexander Tselovalnikov
        Pages 55-62

    4. Pixelization and Multidimensional Data

      1. Pixelizing Data Cubes: A Block-Based Approach
        • Yeow Wei Choong, Anne Laurent, Dominique Laurent
        Pages 63-76
      2. Leveraging Layout with Dimensional Stacking and Pixelization to Facilitate Feature Discovery and Directed Queries
        • John T. Langton, Astrid A. Prinz, David K. Wittenberg, Timothy J. Hickey
        Pages 77-91
      3. Online Data Visualization of Multidimensional Databases Using the Hilbert Space–Filling Curve
        • Jose Castro, Steven Burns
        Pages 92-109
      4. Pixel-Based Visualization and Density-Based Tabular Model
        • Rodolphe Priam, Mohamed Nadif, François-Xavier Jollois
        Pages 110-118

  3. Pixelization Applications

    1. Front Matter

      Pages 119-119
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    2. Spatial Pixelization

      1. A Geometrical Approach to Multiresolution Management in the Fusion of Digital Images
        • Julien Montagner, Vincent Barra, Jean-Yves Boire
        Pages 121-136
      2. Analysis and Visualization of Images Overlapping: Automated Versus Expert Anatomical Mapping in Deep Brain Stimulation Targeting
        • Lemlih Ouchchane, Alice Villéger, Jean-Jacques Lemaire, Jacques Demongeot, Jean-Yves Boire
        Pages 137-151
      3. A Computational Method for Viewing Molecular Interactions in Docking
        • Vipin K. Tripathi, Bhaskar Dasgupta, Kalyanmoy Deb
        Pages 152-163

    3. Temporal Pixelization

      1. A Graphical Tool for Monitoring the Usage of Modules in Course Management Systems
        • Riccardo Mazza
        Pages 164-172
      2. Visu and Xtms: Point Process Visualisation and Analysis Tools
        • Jean-François Vibert, Fabián Alvarez, José Pedro Segundo
        Pages 173-182
      3. Visualizing Time-Course and Efficacy of In-Vivo Measurements of Uterine EMG Signals in Sheep
        • Gaj Vidmar, Branimir L. Leskošek, Drago Rudel
        Pages 183-188

    4. Qualitative Pixelization

      1. From Endoscopic Imaging and Knowledge to Semantic Formal Images
        • Clara Le Guillou, Jean-Michel Cauvin, Basel Solaiman, Michel Robaszkiewicz, Christian Roux
        Pages 189-201
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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.

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