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Mathematical Morphology and Its Applications to Signal and Image Processing - 14th International Symposium, ISMM 2019, Saarbrucken, Germany, July 8-10, 2019, Proceedings (Paperback, 1st ed. 2019)
Bernhard Burgeth, Andreas Kleefeld, Benoit Naegel, Nicolas Passat, Benjamin Perret
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R1,640
Discovery Miles 16 400
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Ships in 10 - 15 working days
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This book contains the refereed proceedings of the 14th
International Symposium on Mathematical Morphology, ISMM 2019, held
in Saarbrucken, Germany, in July 2019. The 40 revised full papers
presented together with one invited talk were carefully reviewed
and selected from 54 submissions. The papers are organized in
topical sections on Theory, Discrete Topology and Tomography, Trees
and Hierarchies, Multivariate Morphology, Computational Morphology,
Machine Learning, Segmentation, Applications in Engineering, and
Applications in (Bio)medical Imaging.
Arising from the fourth Dagstuhl conference entitled Visualization
and Processing of Tensors and Higher Order Descriptors for
Multi-Valued Data (2011), this book offers a broad and vivid view
of current work in this emerging field. Topics covered range from
applications of the analysis of tensor fields to research on their
mathematical and analytical properties. Part I, Tensor Data
Visualization, surveys techniques for visualization of tensors and
tensor fields in engineering, discusses the current state of the
art and challenges, and examines tensor invariants and glyph
design, including an overview of common glyphs. The second Part,
Representation and Processing of Higher-order Descriptors,
describes a matrix representation of local phase, outlines
mathematical morphological operations techniques, extended for use
in vector images, and generalizes erosion to the space of diffusion
weighted MRI. Part III, Higher Order Tensors and Riemannian-Finsler
Geometry, offers powerful mathematical language to model and
analyze large and complex diffusion data such as High Angular
Resolution Diffusion Imaging (HARDI) and Diffusion Kurtosis Imaging
(DKI). A Part entitled Tensor Signal Processing presents new
methods for processing tensor-valued data, including a novel
perspective on performing voxel-wise morphometry of diffusion
tensor data using kernel-based approach, explores the free-water
diffusion model, and reviews proposed approaches for computing
fabric tensors, emphasizing trabecular bone research. The last
Part, Applications of Tensor Processing, discusses metric and
curvature tensors, two of the most studied tensors in geometry
processing. Also covered is a technique for diagnostic prediction
of first-episode schizophrenia patients based on brain diffusion
MRI data. The last chapter presents an interactive system
integrating the visual analysis of diffusion MRI tractography with
data from electroencephalography.
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