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＜電子ブック＞
Anisotropy Across Fields and Scales / edited by Evren Özarslan, Thomas Schultz, Eugene Zhang, Andrea Fuster
(Mathematics and Visualization. ISSN:2197666X)

版	1st ed. 2021.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2021
本文言語	英語
大きさ	X, 280 p. 109 illus., 91 illus. in color : online resource
著者標目	Özarslan, Evren editor Schultz, Thomas editor Zhang, Eugene editor Fuster, Andrea editor SpringerLink (Online service)
件　名	LCSH:Information visualization LCSH:Algebras, Linear LCSH:Mathematics -- Data processing  全ての件名で検索 LCSH:Image processing -- Digital techniques  全ての件名で検索 LCSH:Computer vision LCSH:Mathematical physics FREE:Data and Information Visualization FREE:Linear Algebra FREE:Computational Science and Engineering FREE:Computer Imaging, Vision, Pattern Recognition and Graphics FREE:Theoretical, Mathematical and Computational Physics
一般注記	Tensor approximation for multidimensional and multivariate data -- Tensor field topology without symmetrization using Hermitian tensors -- Continuous histograms for Anisotropy of 2D symmetric piece-wise linear tensor fields -- Riemann-DTI geodesic tractography revisited -- Fourth-order anisotropic diffusion for inpainting and image compression -- Advanced deep learning for processing orientation-dependent diffusion magnetic resonance imaging data - A review -- On the variance measure of diffusion tensors in two dimensions -- Magnetic resonance assessment of effective confinement anisotropy with powder-averaged single and double diffusion encoding -- Merge trees, neutral surfaces, and tensor field topology -- Asymmetric tensor analysis -- Anisotropy issues in shape-based object analysis -- Tractogram filtering -- Multispectral image processing in astronomy -- Anisotropy in the human placenta in pregnancies complicated by fetal growth restriction -- The case for spatially homogeneous models in high gradient strength diffusion-weighted MRI: A position paper on the potential applicability of stochastic geometry Open Access This open access book focuses on processing, modeling, and visualization of anisotropy information... HTTP:URL=https://doi.org/10.1007/978-3-030-56215-1

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