Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 1: Handbook of Numerical Analysis, cartea 19
Ron Kimmel, Xue-Cheng Taien Limba Engleză Hardback – 7 noi 2018
- 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
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
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