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Pixelization paradigm : visual information expert workshop, view 2006, paris, france, april 24-25, 2006, revised selected papers / edited by Pierre P. Lévy [and six others].
Corporate author
Visual Information Expert Workshop
[Browse]
Visual Information Expert Workshop (1st : 2006 : Paris, France)
[Browse]
Format
Book
Language
English
Εdition
1st ed. 2007.
Published/Created
Berlin, Heidelberg : Springer-Verlag, [2007]
©2007
Description
1 online resource (XV, 288 p.)
Availability
Available Online
Springer Nature - Springer Lecture Notes in Computer Science eBooks
Online Content
Details
Subject(s)
Artificial intelligence
[Browse]
Optical pattern recognition
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Computer vision
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Editor
Lévy, Benny, 1945-2003
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Series
LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics ; 4370.
[More in this series]
Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 4370
Subseries of
Lecture Notes in Computer Science
Summary note
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.
Notes
International conference proceedings.
Bibliographic references
Includes bibliographical references and index.
Source of description
Description based on print version record.
Language note
English
Contents
Pixelization Theory
Pixelization Paradigm: Outline of a Formal Approach
Scalable Pixel Based Visual Data Exploration
High Dimensional Visual Data Classification
Using Biclustering for Automatic Attribute Selection to Enhance Global Visualization
Pixelisation-Based Statistical Visualisation for Categorical Datasets with Spreadsheet Software
Dynamic Display of Turnaround Time Via Interactive 2D Images
Pixelizing Data Cubes: A Block-Based Approach
Leveraging Layout with Dimensional Stacking and Pixelization to Facilitate Feature Discovery and Directed Queries
Online Data Visualization of Multidimensional Databases Using the Hilbert Space–Filling Curve
Pixel-Based Visualization and Density-Based Tabular Model
Pixelization Applications
A Geometrical Approach to Multiresolution Management in the Fusion of Digital Images
Analysis and Visualization of Images Overlapping: Automated Versus Expert Anatomical Mapping in Deep Brain Stimulation Targeting
A Computational Method for Viewing Molecular Interactions in Docking
A Graphical Tool for Monitoring the Usage of Modules in Course Management Systems
Visu and Xtms: Point Process Visualisation and Analysis Tools
Visualizing Time-Course and Efficacy of In-Vivo Measurements of Uterine EMG Signals in Sheep
From Endoscopic Imaging and Knowledge to Semantic Formal Images
Multiscale Scatterplot Matrix for Visual and Interactive Exploration of Metabonomic Data
ICD-View: A Technique and Tool to Make the Morbidity Transparent
Pixelization and Cognition
Time Frequency Representation for Complex Analysis of the Multidimensionality Problem of Cognitive Task
Instant Pattern Filtering and Discrimination in a Multilayer Network with Gaussian Distribution of the Connections
AC3 – Automatic Cartography of Cultural Contents
Evaluation of the Mavigator.
Show 23 more Contents items
ISBN
1-280-90220-5
9786610902200
3-540-71027-2
OCLC
1135611348
Doi
10.1007/978-3-540-71027-1
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Princeton University Library aims to describe library materials in a manner that is respectful to the individuals and communities who create, use, and are represented in the collections we manage.
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Other versions
Pixelization paradigm : First Visual Information Expert Workshop, VIEW 2006, Paris, France, April 24-25, 2006 : revised selected papers / Pierre P. Levy [and others].
id
9954037873506421
Pixelization paradigm : First Visual Information Expert Workshop, VIEW 2006, Paris, France, April 24-25, 2006 : revised selected papers / Pierre P. Levy [and others].
id
SCSB-5324884