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| 001 | ocn185027027 | ||
| 003 | OCoLC | ||
| 005 | 20250703144011.0 | ||
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| 008 | 071223s2007 gw a ob 101 0 eng d | ||
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_a10.1007/978-3-540-71027-1 _2doi |
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_a(OCoLC)185027027 _z(OCoLC)137242007 _z(OCoLC)228382157 _z(OCoLC)260242261 _z(OCoLC)320964184 _z(OCoLC)613445769 _z(OCoLC)648310741 _z(OCoLC)814490000 _z(OCoLC)964876711 _z(OCoLC)1005787154 _z(OCoLC)1035657886 _z(OCoLC)1056360556 _z(OCoLC)1066511981 _z(OCoLC)1086861170 _z(OCoLC)1097348567 _z(OCoLC)1105602454 _z(OCoLC)1112547286 _z(OCoLC)1125449608 _z(OCoLC)1132295703 _z(OCoLC)1136194808 _z(OCoLC)1167473573 _z(OCoLC)1238312226 _z(OCoLC)1262672331 |
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_a978-3-540-71026-4 _bSpringer _nhttp://www.springerlink.com |
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| 049 | _aMAIN | ||
| 111 | 2 |
_aVisual Information Expert Workshop _n(1st : _d2006 : _cParis, France) _923345 |
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| 245 | 1 | 0 |
_aPixelization paradigm : _bFirst Visual Information Expert Workshop, VIEW 2006, Paris, France, April 24-25, 2006 : revised selected papers / _cPierre P. Levy [and others]. |
| 246 | 3 | 0 | _aFirst Visual Information Expert Workshop |
| 246 | 3 | 0 | _aVisual Information Expert Workshop |
| 246 | 3 | 0 | _aVIEW 2006 |
| 260 |
_aBerlin ; _aNew York : _bSpringer, _c©2007. |
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| 300 |
_a1 online resource (xv, 277 pages) : _billustrations (some color) |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 | _atext file | ||
| 347 | _bPDF | ||
| 490 | 1 |
_aLecture notes in computer science, _x0302-9743 ; _v4370 |
|
| 504 | _aIncludes bibliographical references and index. | ||
| 588 | 0 | _aPrint version record. | |
| 505 | 0 | _aPixelization 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. | |
| 520 | _aThe 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. | ||
| 650 | 0 |
_aInformation visualization _vCongresses. _923216 |
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| 650 | 0 |
_aComputer vision _vCongresses. _915096 |
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| 650 | 0 |
_aImage processing _xDigital techniques _vCongresses. _915533 |
|
| 650 | 0 |
_aCognitive neuroscience _vCongresses. _920182 |
|
| 650 | 6 |
_aVisualisation de l'information _vCongrès. _927552 |
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| 650 | 6 |
_aVision par ordinateur _vCongrès. _919388 |
|
| 650 | 6 |
_aTraitement d'images _xTechniques numériques _vCongrès. _926851 |
|
| 650 | 6 |
_aNeurosciences cognitives _vCongrès. _920185 |
|
| 650 | 7 |
_aInformatique. _2eclas _914930 |
|
| 650 | 7 |
_aCognitive neuroscience _2fast _9839 |
|
| 650 | 7 |
_aComputer vision _2fast _91678 |
|
| 650 | 7 |
_aImage processing _xDigital techniques _2fast _92563 |
|
| 650 | 7 |
_aInformation visualization _2fast _92930 |
|
| 653 | 0 | 0 | _aalgoritmen |
| 653 | 0 | 0 | _aalgorithms |
| 653 | 0 | 0 | _acomputeranalyse |
| 653 | 0 | 0 | _acomputer analysis |
| 653 | 0 | 0 | _abioinformatics |
| 653 | 0 | 0 | _acomputergrafie |
| 653 | 0 | 0 | _acomputer graphics |
| 653 | 0 | 0 | _abeeldverwerking |
| 653 | 0 | 0 | _aimage processing |
| 653 | 0 | 0 | _amachine vision |
| 653 | 0 | 0 | _acomputerwetenschappen |
| 653 | 0 | 0 | _acomputer sciences |
| 653 | 0 | 0 | _akunstmatige intelligentie |
| 653 | 0 | 0 | _aartificial intelligence |
| 653 | 0 | 0 | _acomputational science |
| 653 | 0 | 0 | _apatroonherkenning |
| 653 | 0 | 0 | _apattern recognition |
| 653 | 1 | 0 | _aInformation and Communication Technology (General) |
| 653 | 1 | 0 | _aInformatie- en communicatietechnologie (algemeen) |
| 655 | 2 |
_aCongress _911670 |
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| 655 | 7 |
_aproceedings (reports) _2aat |
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| 655 | 7 |
_aConference papers and proceedings _2fast _96065 |
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| 655 | 7 |
_aConference papers and proceedings. _2lcgft _96065 |
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| 655 | 7 |
_aActes de congrès. _2rvmgf _9609890 |
|
| 700 | 1 |
_aLévy, Pierre P. _923346 |
|
| 776 | 1 |
_aVisual Information Expert Workshop (1st : 2006 : Paris, France). _tPixelization paradigm. _dBerlin ; New York : Springer, ©2007 _z9783540710264 _w(DLC) 2007920819 _w(OCoLC)84611482 |
|
| 830 | 0 |
_aLecture notes in computer science ; _v4370. _x0302-9743 |
|
| 856 | 4 | 0 | _uhttps://link.springer.com/10.1007/978-3-540-71027-1 |
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