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

Pierre P. Lévy (auth.), Pierre P Lévy, Bénédicte Le Grand, François Poulet, Michel Soto, Laszlo Darago, Laurent Toubiana, Jean-François Vibert (eds.)3540710264, 9783540710264

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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Table of contents :
Front Matter….Pages –
Front Matter….Pages 1-1
Pixelization Paradigm: Outline of a Formal Approach….Pages 3-11
Scalable Pixel Based Visual Data Exploration….Pages 12-24
High Dimensional Visual Data Classification….Pages 25-34
Using Biclustering for Automatic Attribute Selection to Enhance Global Visualization….Pages 35-47
Pixelisation-Based Statistical Visualisation for Categorical Datasets with Spreadsheet Software….Pages 48-54
Dynamic Display of Turnaround Time Via Interactive 2D Images….Pages 55-62
Pixelizing Data Cubes: A Block-Based Approach….Pages 63-76
Leveraging Layout with Dimensional Stacking and Pixelization to Facilitate Feature Discovery and Directed Queries….Pages 77-91
Online Data Visualization of Multidimensional Databases Using the Hilbert Space–Filling Curve….Pages 92-109
Pixel-Based Visualization and Density-Based Tabular Model….Pages 110-118
Front Matter….Pages 119-119
A Geometrical Approach to Multiresolution Management in the Fusion of Digital Images….Pages 121-136
Analysis and Visualization of Images Overlapping: Automated Versus Expert Anatomical Mapping in Deep Brain Stimulation Targeting….Pages 137-151
A Computational Method for Viewing Molecular Interactions in Docking….Pages 152-163
A Graphical Tool for Monitoring the Usage of Modules in Course Management Systems….Pages 164-172
Visu and Xtms: Point Process Visualisation and Analysis Tools….Pages 173-182
Visualizing Time-Course and Efficacy of In-Vivo Measurements of Uterine EMG Signals in Sheep….Pages 183-188
From Endoscopic Imaging and Knowledge to Semantic Formal Images….Pages 189-201
Multiscale Scatterplot Matrix for Visual and Interactive Exploration of Metabonomic Data….Pages 202-215
ICD-View: A Technique and Tool to Make the Morbidity Transparent….Pages 216-224
Front Matter….Pages 225-225
Time Frequency Representation for Complex Analysis of the Multidimensionality Problem of Cognitive Task….Pages 227-239
Front Matter….Pages 225-225
Instant Pattern Filtering and Discrimination in a Multilayer Network with Gaussian Distribution of the Connections….Pages 240-252
AC 3 – Automatic Cartography of Cultural Contents….Pages 253-263
Evaluation of the Mavigator….Pages 264-277
Back Matter….Pages –