Scientific Visualization: uncertainty, multifield, biomedical, and scalable visualization
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| Other Authors: | , , , , |
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| Format: | Book |
| Language: | English |
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Guildford, Surrey
Springer London
2016
Springer 2016 |
| Edition: | Softcover reprint of the original 1st ed. 2014 |
| Series: | Mathematics and Visualization
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| Subjects: | |
| Online Access: | http://www.springer.com/ Inhaltstext Inhaltsverzeichnis |
| Physical Description: | XVII, 400 Seiten Illustrationen, Diagramme 23.5 cm x 15.5 cm, 640 g |
| ISBN: | 9781447171041 1447171047 |
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| 653 | |a Scalable Visualization | ||
| 653 | |a Ultrasound Visualization | ||
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| adam_text | Contents Part I Uncertainty Visualization 1 Overview and State-of-the-Art of Uncertainty Visualization.......... Georges-Pierre Bonneau, Hans-Christian Hege, Chris R. Johnson, Manuel Μ. Oliveira, Kristin Potter, Penny Rheingans and Thomas Schultz 1.1 Introduction................................................................................... 1.1.1 Sources of Uncertainty.................................................. 1.2 Perceptual Uncertainty................................................................ 1.3 Formal Description....................................................................... 1.3.1 What is Uncertainty?.................................................... 1.3.2 Mathematical Modeling of Uncertainty........................ 1.4 Evaluation..................................................................................... 1.4.1 Theoretical Evaluation.................................................. 1.4.2 Low-Level Visual Evaluation......................................... 1.4.3 Task-Oriented User Study............................................. 1.5 Review of Current State of the Art.............................................. 1.5.1 Traditional Representations........................................... 1.5.2 Uncertainty Visualization............................................. 1.6 Examples........................................................................................ 1.6.1 Medical Visualization.................................................... 1.6.2 Weather and Climate.................................................... 1.6.3 Security
and Intelligence................................................ 1.7 Open Problems.............................................................................. 1.7.1 Perceptual and Cognitive Implications......................... 1.7.2 Comparative Visualizations........................................... References............................................................................................. 3 4 5 7 8 8 9 11 12 12 12 13 13 16 18 18 20 21 22 22 22 23 vii
viii 2 3 4 5 Contents Uncertainty Visualization and Color Vision Deficiency................... Manuel Μ. Oliveira 2.1 Introduction................................................................................... 2.1.1 Color Vision Deficiency............................................... 2.2 Tools for More Inclusive Visualizations.................................... 2.2.1 Open Research Questions............................................. 2.3 Conclusion..................................................................................... References............................................................................................. 29 29 30 31 32 33 33 Analysis of Uncertain Scalar Data with Hixels................................. Joshua A. Levine, David Thompson, Janine C. Bennett, Peer-Timo Bremer, Attila Gyulassy, Valerio Pascucci and Philippe P. Pébay 3.1 Foundations................................................................................... 3.1.1 Bucketing Hixels.......................................................... 3.2 Analysis of Hixel Data................................................................ 3.2.1 Sampled Topology........................................................ 3.2.2 Topological Analysis of Statistically Associated Buckets........................................................................... 3.2.3 Fuzzy Isosurfacing........................................................ 3.3 Discussion.....................................................................................
References................................................................................. 39 42 43 43 On the (Un)Suitability of Strict Feature Definitions for Uncertain Data........................................................................ 45 Tino Weinkauf 4.1 Introduction.................................................................................. 4.2 Strict Features Versus Noisy or Uncertain Data....................... 4.2.1 Fuzzy Analogs to Strict Feature Definitions................. 4.2.2 Aggregation of Features............................................... 4.2.3 Filtering of Features...................................................... 4.3 Conclusion.................................................................................... References............................................................................................. 45 46 46 47 47 49 49 The Haunted Swamps of Heuristics: Uncertainty in Problem Solving........................................................ Artem Amirkhanov, Stefan Bruckner, Christoph Heinzl and Μ. Eduard Gröller 5.1 Introduction.................................................................................. 5.2 Heuristics..................................................................................... 5.3 Objects of Desire in Science...................................................... 5.4 Parameter-Space Analysis........................................................... 5.5 Parameter Spaces and VisualizationAlgorithms.......................... 5.6 Algorithms, Parameters, Heuristics—Quo
Vadis?....................... References............................................................................................. 35 36 36 38 38 51 51 52 54 55 58 58 60
Contents 6 7 8 ix Visualizing Uncertainty in Predictive Models................................... Penny Rheingans, Marie desJardins, Wallace Brown, Alex Morrow, Doug Stull and Kevin Winner 6.1 Overview....................................................................................... 6.2 Models......................................................................................... 6.3 Approach....................................................................................... 6.3.1 Dimension Reduction................................................... 6.3.2 Display........................................................................... 6.4 Future Work and Conclusions.................................................... References............................................................................................. 62 62 63 64 65 68 68 Incorporating Uncertainty in Intrusion Detection to Enhance Decision Making....................................................... 71 Lane Harrison and Aidong Lu 7.1 Introduction.................................................................................. 7.2 Related Work................................................................................ 7.3 SybilVis System Overview......................................................... 7.4 SybilVis Uncertainty.................................................................... 7.4.1 Uncertainty Integration................................................. 7.4.2 Uncertainty VersusNo-Uncertainty Case Study........... 7.5 Discussion and
Conclusion......................................................... References............................................................................................. 71 72 73 74 74 75 76 77 Fuzzy Fibers: Uncertainty in dMRI Tractography.......................... Thomas Schultz, Anna Vilanova, Ralph Brecheisen and Gordon Kindlmann 8.1 Introduction.................................................................................. 8.2 Noise and Artifacts...................................................................... 8.2.1 Strategies for Probabilistic Tractography..................... 8.2.2 Rendering ProbabilisticTractograms............................... 8.3 Other Factors................................................................................ 8.3.1 Impact of Parameters.................................................... 8.3.2 Model Uncertainty and Selection................................. 8.3.3 Partial Voluming........................................................... 8.4 Perspectives.................................................................................. 8.4.1 Evidence for Model Selection..................................... 8.4.2 Reproducibility, Seeding, andPreprocessing............... 8.5 Conclusion.................................................................................... References............................................................................................. 61 79 79 80 80 82 83 83 85 86 87 87 87 88 89
Contents x 9 Mathematical Foundations of Uncertain Field Visualization......... Gerik Scheuermann, Mario Hlawitschka, Christoph Garth and Hans Hagen 9.1 Introduction................................................................................... 9.2 Stochastic Processes............................................................... 9.3 Gaussian Processes....................................................................... 9.4 Linear Interpolation on the Line as a Gaussian Process............ 9.5 General Interpolation.................................................................. 9.6 Conclusion..................................................................................... References............................................................................................. Part II 93 93 94 96 97 99 101 101 Multiheld Visualization 10 Definition of a Multifield...................................................................... Ingrid Hotz and Ronald Peikert 10.1 Motivation..................................................................................... 10.2 Definitions..................................................................................... 10.2.1 Fields............................................................................ 10.2.2 Definition of a Field...................................................... 10.2.3 Multifields........................ 10.2.4 Uniqueness of Representation..................................... 10.3 Multifields and Related Concepts...............................................
References............................................................................................. 105 11 Ill Categorization...................................................................................... Helwig Hauser and Hamish Can il. 1 Categorization by Data Type....................................................... 11.1.1 Multi-variate Data....................................................... 11.1.2 Spectral Data................................................................. 11.1.3 Multi-run/Ensemble Data.............................................. 11.1.4 Derived Fields Data..................................................... 11.1.5 Multi-scale Data............................................................ 11.1.6 Other Types of Multifield Data................................... 11.1.7 Summary........................................................................ 11.2 Categorization by Visualization Approach.................................. 11.2.1 Visual Channel Mapping.............................................. 11.2.2 Derived Fields.............................................................. 11.2.3 Interactive Exploration................................................ 11.2.4 Feature Detection and Analysis.................................. 11.2.5 Summary........................................................................ 11.3 Conclusion..................................................................................... 105 106 106 107 107 108 108 109 Ill 112 112 113 113 113 114 114 114 115 115 116 116 117 117
Contents 12 Fusion of Visual Channels................................................................. Min Chen, Klaus Mueller and Anders Ynnerman 12.1 Introduction.................................................................................. 12.2 Visual Channels in Multifield Visualization............................... 12.3 Constructive Operations on Visual Channels............................. 12.4 Composition of Time-Varying Fields........................................ 12.5 Compression of Multifields........................................................ References............................................................................................. 13 Glyph-Based Multi-field Visualization............................................... David H.S. Chung, Robert S. Laramee, Johannes Kehrer and Helwig Hauser 13.1 Introduction................................................................................. 13.2 State-of-the-Art........................................................................... 13.2.1 Spatial Dimensionality: 2D........................................... 13.2.2 Spatial Dimensionality: 2.5D......................................... 13.2.3 Spatial Dimensionality: 3D........................................... 13.3 Critical Design Aspects of Glyph-Based Visualization............ References............................................................................................. 14 Derived Fields...................................................................................... Eugene Zhang and Vijay Natarajan 14.1
Introduction................................................................................. 14.2 Pairwise Distancesand CorrelationMeasures............................... 14.2.1 Correlation Measures.................................................... 14.2.2 Gradient Comparison.................................................... 14.3 Alignment and DependencyMeasures......................................... 14.3.1 Local Gradient-Based Comparison Measures............... 14.3.2 Local Statistical Complexity........................................ 14.3.3 Multifield Comparison Measure.................................... 14.4 Decomposition and Componentization........................................ 14.4.1 Hodge Decomposition................................................... 14.4.2 Components of Tensor Field........................................ 14.4.3 Higher Order Tensor Fields.......................................... 14.5 Conclusions.................................................................................. References............................................................................................. 15 Interactive Visual Exploration and Analysis.................................... Gunther H. Weber and Helwig Hauser 15.1 Basic Concepts............................................................................ 15.2 Additional Concepts................................................................... 15.3 Levels of IVA............................................................................ 15.4 Relational
Analysis..................................................................... 15.5 Complex Analysis..................................................................... xi 119 119 120 122 124 125 126 129 129 130 131 132 133 134 136 139 139 140 140 141 144 144 146 146 150 150 151 156 157 158 161 162 164 165 166 167
xii Contents 15.6 Conclusions and Future Directions............................................. References............................................................................................. 16 Visual Exploration of Multivariate Volume Data Based on Clustering................................................................................. 175 Lars Linsen 16.1 Introduction................................................................................... 16.2 Automatic Clusteringof Attribute Space...................................... 16.3 User-GuidedClustering of Attribute Space................................. 16.4 Visual Encoding of Clustering Result........................................ 16.4.1 Object-Space Representation......................................... 16.4.2 Cluster Hierarchy........................................................... 16.4.3 Projection....................................................................... 16.4.4 Parallel Coordinates....................................................... 16.5 Coordinated Views for Visual Exploration of Clustering Result......................................................... 183 16.6 Interactive Modification of Clustering Result............................ 16.7 Conclusions and Future Directions............................................. References............................................................................................. 17 Feature-Based Visualization of Multifields........................................ Harald Obermaier and Ronald Peikert 17.1 Feature Extraction in Scientific
Visualization........... .. 17.2 Multifield Feature Definitions.................................................... 17.2.1 Single-Field Versus Multifield Features........................ 17.2.2 Classes of Multifield Feature Definitions.......... ........... 17.3 Classification of Visualization Techniques................................. 17.3.1 Isolated and Modulated Features.................................. 17.3.2 Locally Defined Features................................................ 17.3.3 Statistical Features......................................................... 17.3.4 Interactive Feature Specification.................................... References............................................................................................. 18 Feature Analysis in Multifields........................................................... Hamish Carr 18.1 Introduction................................................................................. 18.2 Scalar Features in Reduced Domains........................................ 18.3 Scalar Features in the Range..................................................... 18.4 Manifold Features........................................................................ 18.5 Feature Overlap.......................................................................... 18.6 Joint Feature Analysis................................................................. References............................................................................................. 171 171 175 177 179 179 179 180 180 182 184 184 185 189 189 190 190 190 191 191 191
193 194 195 197 197 198 199 200 201 201 202
Contents 19 xiii Future Challenges and Unsolved Problems in Multi-field Visualization................................................................................. 205 Robert S. Laramee, Hamish Carr, Min Chen, Helwig Hauser, Lars Linsen, Klaus Mueller, Vijay Natarajan, Harald Obermaier, Ronald Peikert and Eugene Zhang 19.1 Introduction................................................................................. 206 19.2 Challenges.................................................................................... 206 References............................................................................................. 210 Part III Biomedical Visualization 20 Overview of Visualization inBiology and Medicine......................... Arie E. Kaufman, Gerik Scheuermann and Jos B.T.M. Roerdink 215 21 Visualization in Connectomics............................................................. Hanspeter Pfister, Verena Kaynig, Charl P. Botha, Stefan Bruckner, Vincent J. Dercksen, Hans-Christian Hege and Jos B.T.M. Roerdink 21.1 Introduction................................................................................. 21.2 Biological Background.............................................................. 21.3 Imaging Modalities Employedin Connectomics........................ 21.4 Macroscale Connectivity............................................................ 21.4.1 EEG and MEG............................................................... 21.4.2 MRI............................................................................... 21.4.3 Functional
MRI............................................................. 21.5 Mesoscale Connectivity.............................................................. 21.6 Microscale Connectivity............................................................ 21.7 Data Integration and Neural Network Modeling........................ 21.7.1 Brain Mapping............................................................... 21.7.2 Neural Network Modeling............................................. 21.8 Network Analysis and ComparativeVisualization...................... 21.8.1 Network Measures........................................................ 21.8.2 Brain Network Comparison and Visualization............ 21.9 Conclusions................................................................................. References............................................................................................. 221 22 Visualization in Biology and Medicine............................................... Heike Leitte and Miriah Meyer 22.1 From Genomes to Cells: Visualization in Biology................... 22.2 Comparative Genomics.............................................................. 22.2.1 Data in Comparative Genomics................................... 22.2.2 Challenges for Visualization........................................ 22.2.3 Visualization for Comparative Genomics...................... 22.2.4 Case Study: MizBee..................................................... 222 223 224 224 225 226 229 229 232 233 234 236 237 238 238 240 240 247 247 248 248 249 249 251
xiv Contents 22.3 Functional Genomics.................................................................. 22.3.1 Data in Functional Genomics........................................ 22.3.2 Challenges for Visualization........................................ 22.3.3 Visualization for ComparativeGenomics....................... 22.3.4 Case Study: Pathline...................................................... 22.4 Evolutionary and Developmental Biology.................................. 22.4.1 Data Acquisition and Storage........................................ 22.4.2 Data Preprocessing........................................................ 22.4.3 Visualization and Data Analysis in Evo-devo.............. 22.4.4 Challenges for Visualization........................................ 22.4.5 Case Study: Visualization in Developmental Biology............................................................. 258 22.5 Conclusions.................................................................................. References............................................................................................. 23 From Individual to Population: Challenges in Medical Visualization................................................................................. C.P. Botha, B. Preim, A.E. Kaufman, S. Takahashi and A. Ynnerman 23.1 Introduction................................................................................... 23.2 Thirty-year Overview of MedicalVisualization......................... 23.2.1 Practical and Multi-modal Volume Visualization .... 23.2.2 Therapy Planning, Predictive
Simulation, and Diagnosis............................................................... 23.2.3 Multi-field Data........................... 23.2.4 Time-Varying Data........................................................ 23.2.5 Illustrative Visualization.............................................. 23.2.6 Multi-subject Data........................................................ 23.3 Challenges in Medical Visualization........................................... 23.3.1 Advances in Data Acquisition..................................... 23.3.2 Heterogeneous Display and Computing Devices......... 23.3.3 Interactive Image Segmentation................................... 23.3.4 Topological Methods................................................... 23.3.5 Integration of Simulation Models................................ 23.3.6 Mappings and Reformations.......................................... 23.3.7 Illustrative Visualization in Medicine......................... 23.3.8 Hyper-Realism............................................................... 23.3.9 Visual Analysis in Healthcare..................................... 23.3.10 Population Imaging........................................................ 23.4 Conclusions................................................................................... References.............................................................................................. 252 252 252 253 253 255 255 256 257 258 260 261 265 265 266 266 268 269 269 269 270 271 271 272 272 272 273 274 275 275 276 277 277 278
Contents 24 25 The Ultrasound Visualization Pipeline.............................................. Åsmund Birkeland, Veronika Šoltészová, Dieter Hönigmann, Odd Helge Gilja, Svein Brekke, Timo Ropinski and Ivan Viola 24.1 Introduction.................................................................................. 24.2 Taxonomy.................................................................................... 24.3 Pre-processing............................................................................. 24.3.1 Reconstruction................................................................ 24.3.2 Data Enhancement......................................................... 24.4 Segmentation................................................................................ 24.4.1 Clipping......................................................................... 24.5 Registration.................................................................................. 24.6 Rendering.................................................................................... 24.6.1 Transfer Function Design............................................. 24.6.2 Multi-modal Rendering.................................................. 24.6.3 Shading and Illumination............................................... 24.7 Ultrasound and Augmented Reality............................................. 24.8 Summary and Discussion............................................................. References............................................................................................. Visual Exploration of
Simulated and Measured Blood Flow......... A. Vilanova, Bernhard Preim, Roy van Pelt, Rocco Gasteiger, Mathias Neugebauer and Thomas Wischgoll 25.1 Introduction.................................................................................. 25.2 Blood Flow Simulation............................................................... 25.2.1 Grid Generation............................................................. 25.2.2 Computational Fluid Dynamics Model......................... 25.3 Blood Flow Measurement........................................................... 25.3.1 Acquisition Methods...................................................... 25.3.2 Noise and Artifacts........................................................ 25.4 Visual Exploration...................................................................... 25.4.1 Visualization of the Anatomical Context..................... 25.4.2 Localization of AnatomicalLandmarks......................... 25.4.3 Exploration of Surface Flow Scalar Features.............. 25.4.4 Blood Flow Probing..................................................... 25.4.5 Blood Flow Visualization............................................ 25.5 Discussion and Open Issues........................................................ References............................................................................................. XV 283 284 285 287 287 289 289 290 291 293 294 296 296 298 299 300 305 306 307 307 308 309 309 311 312 312 314 315 316 318 320 321
Contents xvi Part IV Scalable Visualization 26 Large-Scale Integration-Based Vector Field Visualization .......... Christoph Garth and Kelly Gaither 26.1 Introduction................................................................................. 26.2 Background................................................................................. 26.2.1 Vector Fields Visualization and Integral Curves.......... 26.2.2 Integration-Based Visualization.................................... 26.2.3 Parallel Integral Curve Computation............................. 26.2.4 Problem Description and Classification........................ 26.3 Parallelization Strategies.............................................................. 26.3.1 Parallelization Over Seeds............................................. 26.3.2 Parallelization Over Blocks........................................... 26.3.3 Adaptive Load Balancing............................................. 26.3.4 Hybrid Parallelism......................................................... 26.3.5 Extended Memory Hierarchies...................................... 26.3.6 Other Techniques........................................................... 26.4 Discussion and Future Directions.............................................. References............................................................................................. 27 28 327 327 328 328 329 330 330 332 332 333 334 335 336 336 337 337 339 Large Scale Data Analysis................................................................. Janine Bennett, Attila Gyulassy, Valerio Pascucci and
Peer-Timo Bremer 27.1 Scalable Analysis/Introduction................................................... 27.2 Augmented Feature Families..................................................... 27.3 Sample Feature Hierarchies....................................................... 27.4 Feature Attributes........................................................................ 27.5 Interactive Exploration of Feature-Based Statistics.................. 27.6 Results........................................................................................ 27.7 Conclusion................................................................................... References............................................................................................. 340 340 342 345 346 349 349 350 Cross-Scale, Multi-Scale, and Multi-Source Data Visualization and Analysis Issues and Opportunities..................................... 353 David Ebert, Kelly Gaither, Yun Jang and Sonia Lasher-Trapp 28.1 The Challenge of Multi-Scale Interactions................................. 28.1.1 Systems of Systems................................................... . . 28.1.2 Transformational Cross-Scale Science.......................... 28.1.3 Temporal Scalability...................................................... 28.2 Variety of Data........................................................................... 28.2.1 Visual Scalability........................................................... 28.2.2 Information Scalability.................................................. 28.2.3 Software
Scalability....................................................... 353 354 355 357 357 358 358 359
Contents 28.2.4 Information Fusion....................................................... 28.2.5 Technology Needs....................................................... References............................................................................................. 29 xvii 359 359 360 Scalable Devices.................................................................................... 361 Jens Krüger and Markus Hadwiger 29.1 Introduction.................................................................................. 361 29.2 Small Devices.............................................................................. 362 29.2.1 Mobile User Interfaces................................................. 363 29.2.2 Rendering Approaches................................................. 363 29.3 Large Displays............................................................................. 366 29.3.1 Middleware for Visualizationon Large Displays .... 367 29.3.2 Interaction with Large Displays................................... 369 29.4 Outlook......................................................................................... 370 References............................................................................................. 370 30 Scalable Representation....................................................................... Yun Jang 30.1 Functional Representations........................................................ 30.1.1 Radial Basis Functions................................................ 30.1.2
Wavelets........................................................................ 30.1.3 Spherical Harmonics..................................................... 30.1.4 Time Series Data Representations................................ References............................................................................................. 375 375 376 377 377 377 378 31 Distributed Post-processingand Rendering for Large-Scale Scientific Simulations.......................................................................... Markus Flatken, Christian Wagner and Andreas Gerndt 31.1 Motivation..................................................................................... 31.2 DistributedVisualization Infrastructure........................................ 31.2.1 Parallel Post-processing................................................ 31.2.2 In-situ Processing.......................................................... 31.2.3 Computational Steering................................................ 31.3 Techniques for Paralleland RemoteRendering.......................... 31.3.1 Parallel Rendering....................................................... 31.3.2 Remote Rendering....................................................... References............................................................................................. 381 383 384 385 387 389 390 391 396 Index............................................................................................................. 399 381
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Contents Part I Uncertainty Visualization 1 Overview and State-of-the-Art of Uncertainty Visualization. Georges-Pierre Bonneau, Hans-Christian Hege, Chris R. Johnson, Manuel Μ. Oliveira, Kristin Potter, Penny Rheingans and Thomas Schultz 1.1 Introduction. 1.1.1 Sources of Uncertainty. 1.2 Perceptual Uncertainty. 1.3 Formal Description. 1.3.1 What is Uncertainty?. 1.3.2 Mathematical Modeling of Uncertainty. 1.4 Evaluation. 1.4.1 Theoretical Evaluation. 1.4.2 Low-Level Visual Evaluation. 1.4.3 Task-Oriented User Study. 1.5 Review of Current State of the Art. 1.5.1 Traditional Representations. 1.5.2 Uncertainty Visualization. 1.6 Examples. 1.6.1 Medical Visualization. 1.6.2 Weather and Climate. 1.6.3 Security
and Intelligence. 1.7 Open Problems. 1.7.1 Perceptual and Cognitive Implications. 1.7.2 Comparative Visualizations. References. 3 4 5 7 8 8 9 11 12 12 12 13 13 16 18 18 20 21 22 22 22 23 vii
viii 2 3 4 5 Contents Uncertainty Visualization and Color Vision Deficiency. Manuel Μ. Oliveira 2.1 Introduction. 2.1.1 Color Vision Deficiency. 2.2 Tools for More Inclusive Visualizations. 2.2.1 Open Research Questions. 2.3 Conclusion. References. 29 29 30 31 32 33 33 Analysis of Uncertain Scalar Data with Hixels. Joshua A. Levine, David Thompson, Janine C. Bennett, Peer-Timo Bremer, Attila Gyulassy, Valerio Pascucci and Philippe P. Pébay 3.1 Foundations. 3.1.1 Bucketing Hixels. 3.2 Analysis of Hixel Data. 3.2.1 Sampled Topology. 3.2.2 Topological Analysis of Statistically Associated Buckets. 3.2.3 Fuzzy Isosurfacing. 3.3 Discussion.
References. 39 42 43 43 On the (Un)Suitability of Strict Feature Definitions for Uncertain Data. 45 Tino Weinkauf 4.1 Introduction. 4.2 Strict Features Versus Noisy or Uncertain Data. 4.2.1 Fuzzy Analogs to Strict Feature Definitions. 4.2.2 Aggregation of Features. 4.2.3 Filtering of Features. 4.3 Conclusion. References. 45 46 46 47 47 49 49 The Haunted Swamps of Heuristics: Uncertainty in Problem Solving. Artem Amirkhanov, Stefan Bruckner, Christoph Heinzl and Μ. Eduard Gröller 5.1 Introduction. 5.2 Heuristics. 5.3 Objects of Desire in Science. 5.4 Parameter-Space Analysis. 5.5 Parameter Spaces and VisualizationAlgorithms. 5.6 Algorithms, Parameters, Heuristics—Quo
Vadis?. References. 35 36 36 38 38 51 51 52 54 55 58 58 60
Contents 6 7 8 ix Visualizing Uncertainty in Predictive Models. Penny Rheingans, Marie desJardins, Wallace Brown, Alex Morrow, Doug Stull and Kevin Winner 6.1 Overview. 6.2 Models. 6.3 Approach. 6.3.1 Dimension Reduction. 6.3.2 Display. 6.4 Future Work and Conclusions. References. 62 62 63 64 65 68 68 Incorporating Uncertainty in Intrusion Detection to Enhance Decision Making. 71 Lane Harrison and Aidong Lu 7.1 Introduction. 7.2 Related Work. 7.3 SybilVis System Overview. 7.4 SybilVis Uncertainty. 7.4.1 Uncertainty Integration. 7.4.2 Uncertainty VersusNo-Uncertainty Case Study. 7.5 Discussion and
Conclusion. References. 71 72 73 74 74 75 76 77 Fuzzy Fibers: Uncertainty in dMRI Tractography. Thomas Schultz, Anna Vilanova, Ralph Brecheisen and Gordon Kindlmann 8.1 Introduction. 8.2 Noise and Artifacts. 8.2.1 Strategies for Probabilistic Tractography. 8.2.2 Rendering ProbabilisticTractograms. 8.3 Other Factors. 8.3.1 Impact of Parameters. 8.3.2 Model Uncertainty and Selection. 8.3.3 Partial Voluming. 8.4 Perspectives. 8.4.1 Evidence for Model Selection. 8.4.2 Reproducibility, Seeding, andPreprocessing. 8.5 Conclusion. References. 61 79 79 80 80 82 83 83 85 86 87 87 87 88 89
Contents x 9 Mathematical Foundations of Uncertain Field Visualization. Gerik Scheuermann, Mario Hlawitschka, Christoph Garth and Hans Hagen 9.1 Introduction. 9.2 Stochastic Processes. 9.3 Gaussian Processes. 9.4 Linear Interpolation on the Line as a Gaussian Process. 9.5 General Interpolation. 9.6 Conclusion. References. Part II 93 93 94 96 97 99 101 101 Multiheld Visualization 10 Definition of a Multifield. Ingrid Hotz and Ronald Peikert 10.1 Motivation. 10.2 Definitions. 10.2.1 Fields. 10.2.2 Definition of a Field. 10.2.3 Multifields. 10.2.4 Uniqueness of Representation. 10.3 Multifields and Related Concepts.
References. 105 11 Ill Categorization. Helwig Hauser and Hamish Can il. 1 Categorization by Data Type. 11.1.1 Multi-variate Data. 11.1.2 Spectral Data. 11.1.3 Multi-run/Ensemble Data. 11.1.4 Derived Fields Data. 11.1.5 Multi-scale Data. 11.1.6 Other Types of Multifield Data. 11.1.7 Summary. 11.2 Categorization by Visualization Approach. 11.2.1 Visual Channel Mapping. 11.2.2 Derived Fields. 11.2.3 Interactive Exploration. 11.2.4 Feature Detection and Analysis. 11.2.5 Summary. 11.3 Conclusion. 105 106 106 107 107 108 108 109 Ill 112 112 113 113 113 114 114 114 115 115 116 116 117 117
Contents 12 Fusion of Visual Channels. Min Chen, Klaus Mueller and Anders Ynnerman 12.1 Introduction. 12.2 Visual Channels in Multifield Visualization. 12.3 Constructive Operations on Visual Channels. 12.4 Composition of Time-Varying Fields. 12.5 Compression of Multifields. References. 13 Glyph-Based Multi-field Visualization. David H.S. Chung, Robert S. Laramee, Johannes Kehrer and Helwig Hauser 13.1 Introduction. 13.2 State-of-the-Art. 13.2.1 Spatial Dimensionality: 2D. 13.2.2 Spatial Dimensionality: 2.5D. 13.2.3 Spatial Dimensionality: 3D. 13.3 Critical Design Aspects of Glyph-Based Visualization. References. 14 Derived Fields. Eugene Zhang and Vijay Natarajan 14.1
Introduction. 14.2 Pairwise Distancesand CorrelationMeasures. 14.2.1 Correlation Measures. 14.2.2 Gradient Comparison. 14.3 Alignment and DependencyMeasures. 14.3.1 Local Gradient-Based Comparison Measures. 14.3.2 Local Statistical Complexity. 14.3.3 Multifield Comparison Measure. 14.4 Decomposition and Componentization. 14.4.1 Hodge Decomposition. 14.4.2 Components of Tensor Field. 14.4.3 Higher Order Tensor Fields. 14.5 Conclusions. References. 15 Interactive Visual Exploration and Analysis. Gunther H. Weber and Helwig Hauser 15.1 Basic Concepts. 15.2 Additional Concepts. 15.3 Levels of IVA. 15.4 Relational
Analysis. 15.5 Complex Analysis. xi 119 119 120 122 124 125 126 129 129 130 131 132 133 134 136 139 139 140 140 141 144 144 146 146 150 150 151 156 157 158 161 162 164 165 166 167
xii Contents 15.6 Conclusions and Future Directions. References. 16 Visual Exploration of Multivariate Volume Data Based on Clustering. 175 Lars Linsen 16.1 Introduction. 16.2 Automatic Clusteringof Attribute Space. 16.3 User-GuidedClustering of Attribute Space. 16.4 Visual Encoding of Clustering Result. 16.4.1 Object-Space Representation. 16.4.2 Cluster Hierarchy. 16.4.3 Projection. 16.4.4 Parallel Coordinates. 16.5 Coordinated Views for Visual Exploration of Clustering Result. 183 16.6 Interactive Modification of Clustering Result. 16.7 Conclusions and Future Directions. References. 17 Feature-Based Visualization of Multifields. Harald Obermaier and Ronald Peikert 17.1 Feature Extraction in Scientific
Visualization. . 17.2 Multifield Feature Definitions. 17.2.1 Single-Field Versus Multifield Features. 17.2.2 Classes of Multifield Feature Definitions. . 17.3 Classification of Visualization Techniques. 17.3.1 Isolated and Modulated Features. 17.3.2 Locally Defined Features. 17.3.3 Statistical Features. 17.3.4 Interactive Feature Specification. References. 18 Feature Analysis in Multifields. Hamish Carr 18.1 Introduction. 18.2 Scalar Features in Reduced Domains. 18.3 Scalar Features in the Range. 18.4 Manifold Features. 18.5 Feature Overlap. 18.6 Joint Feature Analysis. References. 171 171 175 177 179 179 179 180 180 182 184 184 185 189 189 190 190 190 191 191 191
193 194 195 197 197 198 199 200 201 201 202
Contents 19 xiii Future Challenges and Unsolved Problems in Multi-field Visualization. 205 Robert S. Laramee, Hamish Carr, Min Chen, Helwig Hauser, Lars Linsen, Klaus Mueller, Vijay Natarajan, Harald Obermaier, Ronald Peikert and Eugene Zhang 19.1 Introduction. 206 19.2 Challenges. 206 References. 210 Part III Biomedical Visualization 20 Overview of Visualization inBiology and Medicine. Arie E. Kaufman, Gerik Scheuermann and Jos B.T.M. Roerdink 215 21 Visualization in Connectomics. Hanspeter Pfister, Verena Kaynig, Charl P. Botha, Stefan Bruckner, Vincent J. Dercksen, Hans-Christian Hege and Jos B.T.M. Roerdink 21.1 Introduction. 21.2 Biological Background. 21.3 Imaging Modalities Employedin Connectomics. 21.4 Macroscale Connectivity. 21.4.1 EEG and MEG. 21.4.2 MRI. 21.4.3 Functional
MRI. 21.5 Mesoscale Connectivity. 21.6 Microscale Connectivity. 21.7 Data Integration and Neural Network Modeling. 21.7.1 Brain Mapping. 21.7.2 Neural Network Modeling. 21.8 Network Analysis and ComparativeVisualization. 21.8.1 Network Measures. 21.8.2 Brain Network Comparison and Visualization. 21.9 Conclusions. References. 221 22 Visualization in Biology and Medicine. Heike Leitte and Miriah Meyer 22.1 From Genomes to Cells: Visualization in Biology. 22.2 Comparative Genomics. 22.2.1 Data in Comparative Genomics. 22.2.2 Challenges for Visualization. 22.2.3 Visualization for Comparative Genomics. 22.2.4 Case Study: MizBee. 222 223 224 224 225 226 229 229 232 233 234 236 237 238 238 240 240 247 247 248 248 249 249 251
xiv Contents 22.3 Functional Genomics. 22.3.1 Data in Functional Genomics. 22.3.2 Challenges for Visualization. 22.3.3 Visualization for ComparativeGenomics. 22.3.4 Case Study: Pathline. 22.4 Evolutionary and Developmental Biology. 22.4.1 Data Acquisition and Storage. 22.4.2 Data Preprocessing. 22.4.3 Visualization and Data Analysis in Evo-devo. 22.4.4 Challenges for Visualization. 22.4.5 Case Study: Visualization in Developmental Biology. 258 22.5 Conclusions. References. 23 From Individual to Population: Challenges in Medical Visualization. C.P. Botha, B. Preim, A.E. Kaufman, S. Takahashi and A. Ynnerman 23.1 Introduction. 23.2 Thirty-year Overview of MedicalVisualization. 23.2.1 Practical and Multi-modal Volume Visualization . 23.2.2 Therapy Planning, Predictive
Simulation, and Diagnosis. 23.2.3 Multi-field Data. 23.2.4 Time-Varying Data. 23.2.5 Illustrative Visualization. 23.2.6 Multi-subject Data. 23.3 Challenges in Medical Visualization. 23.3.1 Advances in Data Acquisition. 23.3.2 Heterogeneous Display and Computing Devices. 23.3.3 Interactive Image Segmentation. 23.3.4 Topological Methods. 23.3.5 Integration of Simulation Models. 23.3.6 Mappings and Reformations. 23.3.7 Illustrative Visualization in Medicine. 23.3.8 Hyper-Realism. 23.3.9 Visual Analysis in Healthcare. 23.3.10 Population Imaging. 23.4 Conclusions. References. 252 252 252 253 253 255 255 256 257 258 260 261 265 265 266 266 268 269 269 269 270 271 271 272 272 272 273 274 275 275 276 277 277 278
Contents 24 25 The Ultrasound Visualization Pipeline. Åsmund Birkeland, Veronika Šoltészová, Dieter Hönigmann, Odd Helge Gilja, Svein Brekke, Timo Ropinski and Ivan Viola 24.1 Introduction. 24.2 Taxonomy. 24.3 Pre-processing. 24.3.1 Reconstruction. 24.3.2 Data Enhancement. 24.4 Segmentation. 24.4.1 Clipping. 24.5 Registration. 24.6 Rendering. 24.6.1 Transfer Function Design. 24.6.2 Multi-modal Rendering. 24.6.3 Shading and Illumination. 24.7 Ultrasound and Augmented Reality. 24.8 Summary and Discussion. References. Visual Exploration of
Simulated and Measured Blood Flow. A. Vilanova, Bernhard Preim, Roy van Pelt, Rocco Gasteiger, Mathias Neugebauer and Thomas Wischgoll 25.1 Introduction. 25.2 Blood Flow Simulation. 25.2.1 Grid Generation. 25.2.2 Computational Fluid Dynamics Model. 25.3 Blood Flow Measurement. 25.3.1 Acquisition Methods. 25.3.2 Noise and Artifacts. 25.4 Visual Exploration. 25.4.1 Visualization of the Anatomical Context. 25.4.2 Localization of AnatomicalLandmarks. 25.4.3 Exploration of Surface Flow Scalar Features. 25.4.4 Blood Flow Probing. 25.4.5 Blood Flow Visualization. 25.5 Discussion and Open Issues. References. XV 283 284 285 287 287 289 289 290 291 293 294 296 296 298 299 300 305 306 307 307 308 309 309 311 312 312 314 315 316 318 320 321
Contents xvi Part IV Scalable Visualization 26 Large-Scale Integration-Based Vector Field Visualization . Christoph Garth and Kelly Gaither 26.1 Introduction. 26.2 Background. 26.2.1 Vector Fields Visualization and Integral Curves. 26.2.2 Integration-Based Visualization. 26.2.3 Parallel Integral Curve Computation. 26.2.4 Problem Description and Classification. 26.3 Parallelization Strategies. 26.3.1 Parallelization Over Seeds. 26.3.2 Parallelization Over Blocks. 26.3.3 Adaptive Load Balancing. 26.3.4 Hybrid Parallelism. 26.3.5 Extended Memory Hierarchies. 26.3.6 Other Techniques. 26.4 Discussion and Future Directions. References. 27 28 327 327 328 328 329 330 330 332 332 333 334 335 336 336 337 337 339 Large Scale Data Analysis. Janine Bennett, Attila Gyulassy, Valerio Pascucci and
Peer-Timo Bremer 27.1 Scalable Analysis/Introduction. 27.2 Augmented Feature Families. 27.3 Sample Feature Hierarchies. 27.4 Feature Attributes. 27.5 Interactive Exploration of Feature-Based Statistics. 27.6 Results. 27.7 Conclusion. References. 340 340 342 345 346 349 349 350 Cross-Scale, Multi-Scale, and Multi-Source Data Visualization and Analysis Issues and Opportunities. 353 David Ebert, Kelly Gaither, Yun Jang and Sonia Lasher-Trapp 28.1 The Challenge of Multi-Scale Interactions. 28.1.1 Systems of Systems. . . 28.1.2 Transformational Cross-Scale Science. 28.1.3 Temporal Scalability. 28.2 Variety of Data. 28.2.1 Visual Scalability. 28.2.2 Information Scalability. 28.2.3 Software
Scalability. 353 354 355 357 357 358 358 359
Contents 28.2.4 Information Fusion. 28.2.5 Technology Needs. References. 29 xvii 359 359 360 Scalable Devices. 361 Jens Krüger and Markus Hadwiger 29.1 Introduction. 361 29.2 Small Devices. 362 29.2.1 Mobile User Interfaces. 363 29.2.2 Rendering Approaches. 363 29.3 Large Displays. 366 29.3.1 Middleware for Visualizationon Large Displays . 367 29.3.2 Interaction with Large Displays. 369 29.4 Outlook. 370 References. 370 30 Scalable Representation. Yun Jang 30.1 Functional Representations. 30.1.1 Radial Basis Functions. 30.1.2
Wavelets. 30.1.3 Spherical Harmonics. 30.1.4 Time Series Data Representations. References. 375 375 376 377 377 377 378 31 Distributed Post-processingand Rendering for Large-Scale Scientific Simulations. Markus Flatken, Christian Wagner and Andreas Gerndt 31.1 Motivation. 31.2 DistributedVisualization Infrastructure. 31.2.1 Parallel Post-processing. 31.2.2 In-situ Processing. 31.2.3 Computational Steering. 31.3 Techniques for Paralleland RemoteRendering. 31.3.1 Parallel Rendering. 31.3.2 Remote Rendering. References. 381 383 384 385 387 389 390 391 396 Index. 399 381 |
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| id | DE-604.BV048394150 |
| illustrated | Illustrated |
| index_date | 2024-07-03T20:21:18Z |
| indexdate | 2024-07-10T09:36:56Z |
| institution | BVB |
| institution_GND | (DE-588)1065358059 |
| isbn | 9781447171041 1447171047 |
| language | English |
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| physical | XVII, 400 Seiten Illustrationen, Diagramme 23.5 cm x 15.5 cm, 640 g |
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| spelling | Scientific Visualization uncertainty, multifield, biomedical, and scalable visualization Softcover reprint of the original 1st ed. 2014 Guildford, Surrey Springer London 2016 Springer 2016 XVII, 400 Seiten Illustrationen, Diagramme 23.5 cm x 15.5 cm, 640 g txt rdacontent n rdamedia nc rdacarrier Mathematics and Visualization Mathematik (DE-588)4037944-9 gnd rswk-swf Visualisierung (DE-588)4188417-6 gnd rswk-swf Volumendaten (DE-588)4534768-2 gnd rswk-swf Biological Visualization Biomedical Visualization Multifield Visualization Representations for Visualization Scalable Visualization Ultrasound Visualization Uncertainty Visualization Volume Visualization Visualisierung (DE-588)4188417-6 s Mathematik (DE-588)4037944-9 s Volumendaten (DE-588)4534768-2 s DE-604 Hansen, Charles D. 1962- (DE-588)13698861X edt Chen, Min 1960- (DE-588)121504298 edt Johnson, Christopher R. ca. 20./21. Jh. (DE-588)1265156549 edt Kaufman, Arie E. ca. 20./21. Jh. (DE-588)1265157103 edt Hagen, Hans (DE-588)17155941X edt Springer-Verlag London Ltd. (DE-588)1065358059 pbl Erscheint auch als Druck-Ausgabe 9781447164968 http://www.springer.com/ Verlag X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=33a9e06e85d840469a354fd6069b12b1&prov=M&dok_var=1&dok_ext=htm Inhaltstext Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033772850&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p vlb 20160929 DE-101 https://d-nb.info/provenance/plan#vlb |
| spellingShingle | Scientific Visualization uncertainty, multifield, biomedical, and scalable visualization Mathematik (DE-588)4037944-9 gnd Visualisierung (DE-588)4188417-6 gnd Volumendaten (DE-588)4534768-2 gnd |
| subject_GND | (DE-588)4037944-9 (DE-588)4188417-6 (DE-588)4534768-2 |
| title | Scientific Visualization uncertainty, multifield, biomedical, and scalable visualization |
| title_auth | Scientific Visualization uncertainty, multifield, biomedical, and scalable visualization |
| title_exact_search | Scientific Visualization uncertainty, multifield, biomedical, and scalable visualization |
| title_exact_search_txtP | Scientific Visualization uncertainty, multifield, biomedical, and scalable visualization |
| title_full | Scientific Visualization uncertainty, multifield, biomedical, and scalable visualization |
| title_fullStr | Scientific Visualization uncertainty, multifield, biomedical, and scalable visualization |
| title_full_unstemmed | Scientific Visualization uncertainty, multifield, biomedical, and scalable visualization |
| title_short | Scientific Visualization |
| title_sort | scientific visualization uncertainty multifield biomedical and scalable visualization |
| title_sub | uncertainty, multifield, biomedical, and scalable visualization |
| topic | Mathematik (DE-588)4037944-9 gnd Visualisierung (DE-588)4188417-6 gnd Volumendaten (DE-588)4534768-2 gnd |
| topic_facet | Mathematik Visualisierung Volumendaten |
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