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Data visualization : principles and practice

Author: Alexandru Telea
Publisher: Wellesley, Mass. : A K Peters, ©2008.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
"Data Visualization: Principles and Practice provides a concise introduction to the field of data visualization. The material focuses on those techniques and methods that have a broad applicability in visualization applications, occur in most practical problems in various guises, and do not demand a specialized background to be understood. A number of less mainstream visualization techniques are also  Read more...
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Details

Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Alexandru Telea
ISBN: 9781568813066 1568813066
OCLC Number: 148733261
Description: ix, 502 pages : illustrations (chiefly color) ; 24 cm
Contents: 1. Introduction --
1.1. How Visualization Works --
1.2. Book Structure --
1.3. Notation --
2. From Graphics to Visualization --
2.1. A Simple Example --
2.2. Graphics-Rendering Basics --
2.3. Rendering the Height Plot --
2.4. Texture Mapping --
2.5. Transparency and Blending --
2.6. Conclusion --
3. Data Representation --
3.1. Continuous Data --
3.2. Sampled Data --
3.3. Discrete Datasets --
3.4. Cell Types --
3.5. Grid Types --
3.6. Attributes --
3.7. Computing Derivatives of Sampled Data --
3.8. Implementation --
3.9. Advanced Data Representation --
3.10. Conclusion --
4. The Visualization Pipeline --
4.1. Conceptual Perspective --
4.2. Implementation Perspective --
4.3. Algorithm Classification --
4.4. Conclusion --
5. Scalar Visualization --
5.1. Color Mapping --
5.2. Designing Effective Colormaps --
5.3. Contouring --
5.4. Height Plots --
5.5. Conclusion --
6. Vector Visualization --
6.1. Divergence and Vorticity --
6.2. Vector Glyphs --
6.3. Vector Color Coding --
6.4. Displacement Plots --
6.5. Stream Objects --
6.6. Texture-Based Vector Visualization --
6.7. Simplified Representation of Vector Fields --
6.8. Conclusion --
7. Tensor Visualization --
7.1. Principal Component Analysis --
7.2. Visualizing Components --
7.3. Visualizing Scalar PCA Information --
7.4. Visualizing Vector PCA Information --
7.5. Tensor Glyphs --
7.6. Fiber Tracking --
7.7. Hyperstreamlines --
7.8. Conclusion --
8. Domain-Modeling Techniques --
8.1. Cutting --
8.2. Selection --
8.3. Grid Construction from Scattered Points --
8.4. Grid-Processing Techniques --
8.5. Conclusion --
9. Image Visualization --
9.1. Image Data Representation --
9.2. Image Processing and Visualization --
9.3. Basic Imaging Algorithms --
9.4. Shape Representation and Analysis --
9.5. Conclusion --
10. Volume Visualization --
10.1. Motivation --
10.2. Volume Visualization Basics --
10.3. Image Order Techniques --
10.4. Object-Order Techniques --
10.5. Volume Rendering versus Geometric Rendering --
10.6. Conclusion --
11. Information Visualization --
11.1. Introduction --
11.2. What Is Infovis? --
11.3. Infovis versus Scivis: A Technical Comparison --
11.4. Table Visualization --
11.5. Visualization of Relations --
11.6. Multivariate Data Visualization --
11.7. Text Visualization --
11.8. Conclusion --
12. Conclusion --
A. Visualization Software --
A.1. Taxonomies of Visualization Systems --
A.2. Scientific Visualization Software --
A.3. Medical Imaging Software --
A.4. Information Visualization Software.
Responsibility: Alexandru Telea.
More information:

Abstract:

The goal of data visualization is to use images to improve our understanding of a dataset, drawing on techniques from mathematics, computer science, cognitive and perception science, and physics.  Read more...

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" ""An excellent reference for any advanced, college-level holding strong in computer graphics or data visualization techniques."" -The Midwest Book Review, April 2008""For advanced undergraduate or Read more...

 
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