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Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 1: Handbook of Numerical Analysis, cartea 19

Ron Kimmel, Xue-Cheng Tai
en Limba Engleză Hardback – 7 noi 2018
Processing, Analyzing and Learning of Images, Shapes, and Forms: Volume 19, Part One provides a comprehensive survey of the contemporary developments related to the analysis and learning of images, shapes and forms. It covers mathematical models as well as fast computational techniques, and includes new chapters on Alternating diffusion: a geometric approach for sensor fusion, Shape Correspondence and Functional Maps, Geometric models for perception-based image processing, Decomposition schemes for nonconvex composite minimization: theory and applications, Low rank matrix recovery: algorithms and theory, Geometry and learning for deformation shape correspondence, and Factoring scene layout from monocular images in presence of occlusion.


  • Presents a contemporary view on the topic, comprehensively covering the newest developments and content
  • Provides a comprehensive survey of the contemporary developments related to the analysis and learning of images, shapes and forms
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Specificații

ISBN-13: 9780444642059
ISBN-10: 0444642056
Pagini: 157
Dimensiuni: 152 x 229 x 16 mm
Greutate: 0.38 kg
Editura: ELSEVIER SCIENCE
Seria Handbook of Numerical Analysis


Public țintă

Research scientists and graduate students specialising in mathematics, as well as engineers with a basic knowledge in partial differential equations and their numerical approximations.

Cuprins

Section One 1. Compressed Learning for Image Classification: A Deep Neural Network Approach E. Zisselman, A. Adler and M. Elad
Section Two 2. Exploiting the Structure Effectively and Efficiently in Low Rank Matrix Recovery Jian-Feng Cai and Ke Wei
Section Three 3. Partial Single- and Multi-Shape Dense Correspondence Using Functional Maps Alex Bronstein 4. Shape Correspondence and Functional Maps Maks Ovsjanikov 5. Factoring Scene Layout From Monocular Images in Presence of Occlusion Niloy J. Mitra

Recenzii

"It ranges from a novel attempt to put deep learning within the framework of compressed sensing and sparse models, reconstruction of low rank matrices, shifting into learning geometry, shape representation that has the potential to migrate geometry analysis into that of deep learning, and pure geometric problems dealt in a novel, yet axiomatic, manner." --zbMATH