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Visualization and Processing of Higher Order Descriptors for Multi-Valued Data

Visualization and Processing of Higher Order Descriptors for Multi-Valued Data
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Field name Details
Dewey Class 004
Title Visualization and Processing of Higher Order Descriptors for Multi-Valued Data ([EBook]) / edited by Ingrid Hotz, Thomas Schultz.
Added Personal Name Hotz, Ingrid editor.
Schultz, Thomas editor.
Other name(s) SpringerLink (Online service)
Publication Cham : : Springer International Publishing : : Imprint: Springer, , 2015.
Physical Details X, 383 p. 173 illus., 149 illus. in color. : online resource.
Series Mathematics and visualization 1612-3786
ISBN 9783319150901
Summary Note Modern imaging techniques and computational simulations yield complex multi-valued data that require higher-order mathematical descriptors. This book addresses topics of importance when dealing with such data, including frameworks for image processing, visualization, and statistical analysis of higher-order descriptors. It also provides examples of the successful use of higher-order descriptors in specific applications and a glimpse of the next generation of diffusion MRI. To do so, it combines contributions on new developments, current challenges in this area, and state-of-the-art surveys.   Compared to the increasing importance of higher-order descriptors in a range of applications, tools for analysis and processing are still relatively hard to come by. Even though application areas such as medical imaging, fluid dynamics, and structural mechanics are very different in nature they face many shared challenges. This book provides an interdisciplinary perspective on this topic with contributions from key researchers in disciplines ranging from visualization and image processing to applications. It is based on the 5th Dagstuhl seminar on Visualization and Processing of Higher Order Descriptors for Multi-Valued Data.   This book will appeal to scientists who are working to develop new analysis methods in the areas of image processing and visualization, as well as those who work with applications that generate higher-order data or could benefit from higher-order models and are searching for novel analytical tools.:
Contents note Part I: Mathematical Foundations: 1 Cem Yolcu and Evren Özarslan: Diffusion-Weighted Magnetic Resonance Signal for General Gradient Waveforms: Multiple Correlation Function Framework, Path Integrals, and Parallels Between Them -- 2 T.C.J. Dela Haije, A. Fuster and L.M.J. Florack: Finslerian Diffusion and the Bloch-Torrey Equation -- 3 M. Moakher and P. J. Basser: Fiber Orientation Distribution Functions and Orientation Tensors for Different Material Symmetries -- 4 Yue Zhang, Jonathan Palacios and Eugene Zhang: Topology of 3D Linear Symmetric Tensor Fields -- 5 Carmeliza Navasca and Deonnia N. Pompey: Random Projections for Low Multilinear Rank Tensors -- Part II: Processing, Filtering and Interpolation: 6 Jasper J. van de Gronde, Mikola Lysenko and Jos B.T.M. Roerdink: Path-Based Mathematical Morphology on Tensor Fields -- 7 Andreas Kleefeld and Bernhard Burgeth: Processing Multispectral Images via Mathematical Morphology -- 8 Luc Florack, Tom Dela Haije and Andrea Fuster: Direction-Controlled DTI Interpolation -- 9 Daniel Jörgens and Rodrigo Moreno: Tensor Voting: Current State, Challenges and New Trends in the Context of Medical Image Analysis -- Part III: Visualization: 10 Olivier Vaillancourt, Maxime Chamberland, Jean-Christophe Houde and Maxime Descoteaux: Visualization of Diffusion Propagator and Multiple Parameter Diffusion Signal -- 11 Sujal Bista, Jiachen Zhuo, Rao P. Gullapalli and Amitabh Varshney: Visual Knowledge Discovery for Diffusion Kurtosis Datasets of the Human Brain -- 12 Tobias Isenberg: A Survey of Illustrative Visualization Techniques for Diffusion-Weighted MRI Tractography -- 13 Valentin Zobel, Jan Reininghaus and Ingrid Hotz: Visualizing Symmetric Indefinite 2D Tensor Fields sing the Heat Kernel Signature -- Part IV: Statistical Analysis: 14 Maxime Taquet, Benoit Scherrer and Simon K. Warfield: A Framework for the Analysis of Diffusion Compartment Imaging (DCT) -- 15 Lauren J. O’Donnell and Thomas Schultz: Statistical and Machine Learning Methods for Neuroimaging: Examples, Challenges, and Extensions to Diffusion Imaging Data -- Part V: Applications: 16 A. aries, T. Luciani, P.H. Pisciuneri, M.B. Nik, S.L. Yilmaz, P. Givi and G.E. Marai A Clustering Method for Identifying Regions of Interest in Turbulent Combustion Tensor Fields -- 17 Mark Schöneich, Andrea Kratz, Valentin Zobel, Gerik Scheuermann, Markus Stommel, and Ingrid Hotz: Tensor Lines in Engineering – Success, Failure, and Open Questions -- 18 Vesna Prchkovska, Magí Andorrà , Pablo Villoslada, Eloy Martinez-Heras, Remco Duits, David Fortin, Paulo Rodrigues and Maxime Descoteaux: Contextual Diffusion Image Post-Processing Aids Clinical Applications.
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