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130626 ||| eng |
| 020 |
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|a 9783540312727
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| 100 |
1 |
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|a Weickert, Joachim
|e [editor]
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| 245 |
0 |
0 |
|a Visualization and Processing of Tensor Fields
|h Elektronische Ressource
|c edited by Joachim Weickert, Hans Hagen
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| 250 |
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|a 1st ed. 2006
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| 260 |
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|a Berlin, Heidelberg
|b Springer Berlin Heidelberg
|c 2006, 2006
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| 300 |
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|a XV, 481 p
|b online resource
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| 505 |
0 |
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|a An Introduction to Tensors -- Feature Detection with Tensors -- Adaptive Structure Tensors and their Applications -- On the Concept of a Local Greyvalue Distribution and the Adaptive Estimation of a Structure Tensor -- Low-level Feature Detection Using the Boundary Tensor -- Diffusion Tensor Imaging -- An Introduction to Computational Diffusion MRI: the Diffusion Tensor and Beyond -- Random Noise in Diffusion Tensor Imaging, its Destructive Impact and Some Corrections -- An Introduction to Visualization of Diffusion Tensor Imaging and Its Applications -- Anatomy-Based Visualizations of Diffusion Tensor Images of Brain White Matter -- Variational Regularization of Multiple Diffusion Tensor Fields -- Higher Rank Tensors in Diffusion MRI -- Visualization of Tensor Fields -- Strategies for Direct Visualization of Second-Rank Tensor Fields -- Tensor Invariants and their Gradients -- Visualizing the Topology of Symmetric, Second-Order, Time-Varying Two-Dimensional Tensor Fields -- Degenerate 3D Tensors -- Locating Closed Hyperstreamlines in Second Order Tensor Fields -- Tensor Field Visualization Using a Metric Interpretation -- Tensor Field Transformations -- Symmetric Positive-Definite Matrices: From Geometry to Applications and Visualization -- Continuous Tensor Field Approximation of Diffusion Tensor MRI data -- Tensor Field Interpolation with PDEs -- Diffusion-Tensor Image Registration -- Image Processing Methods for Tensor Fields -- Tensor Median Filtering and M-Smoothing -- Mathematical Morphology on Tensor Data Using the Loewner Ordering -- A Local Structure Measure for Anisotropic Regularization of Tensor Fields -- Tensor Field Regularization using Normalized Convolution and Markov Random Fields in a Bayesian Framework -- PDEs for Tensor Image Processing
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| 653 |
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|a Geometry, Differential
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| 653 |
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|a Image processing / Digital techniques
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| 653 |
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|a Computer vision
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| 653 |
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|a Mathematical analysis
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| 653 |
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|a Radiology
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| 653 |
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|a Computer Vision
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| 653 |
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|a Information visualization
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| 653 |
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|a Computer Imaging, Vision, Pattern Recognition and Graphics
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| 653 |
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|a Analysis
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| 653 |
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|a Differential Geometry
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| 653 |
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|a Data and Information Visualization
|
| 700 |
1 |
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|a Hagen, Hans
|e [editor]
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a Mathematics and Visualization
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| 028 |
5 |
0 |
|a 10.1007/3-540-31272-2
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/3-540-31272-2?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 515
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| 520 |
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|a Matrix-valued data sets - so-called second order tensor fields - have gained significant importance in scientific visualization and image processing due to recent developments such as diffusion tensor imaging. This book is the first edited volume that presents the state-of-the-art in the visualization and processing of tensor fields. It contains some longer chapters dedicated to surveys and tutorials of specific topics, as well as a great deal of original work by leading experts that has not been published before. It serves as an overview for the inquiring scientist, as a basic foundation for developers and practitioners, and as as a textbook for specialized classes and seminars for graduate and doctoral students
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