Description
Spectral Geometry of Shapes
Principles and Applications
Computer Vision and Pattern Recognition Series
Authors: Hua Jing, Zhong Zichun, Hu Jiaxi
Language: EnglishSubjects for Spectral Geometry of Shapes:
Keywords
B-Rep graph; Bunny model; conformal method; convolutional layer; convolutional neural network; deep learning; extended Gaussian image; finite element method; fully connected layer; iterative closest point algorithm; Laplace matrix; Laplace–Beltrami operator; Laplacian spectrum; linear interpolation; matrix eigenvalue variation; multiview CNN; nonisometric motion analysis; nonisometric shape registration; pooling layer; projective CNN; quadratic programming problem; Reeb graph; Riemannian manifold; salient feature points; shape analysis; shape clustering and classification; shape representation; shape spectrum; skeletal graph; smoothness constraints; spectral geometry; spectral optimization; spectrum alignment algorithm; variation of the eigenvalues and eigenfunctions; Voronoi area; Voronoi region
108.13 €
In Print (Delivery period: 14 days).
Add to cart the book of Hua Jing, Zhong Zichun, Hu Jiaxi152 p. · 15x22.8 cm · Paperback
Description
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Spectral Geometry of Shapes presents unique shape analysis approaches based on shape spectrum in differential geometry. It provides insights on how to develop geometry-based methods for 3D shape analysis. The book is an ideal learning resource for graduate students and researchers in computer science, computer engineering and applied mathematics who have an interest in 3D shape analysis, shape motion analysis, image analysis, medical image analysis, computer vision and computer graphics. Due to the rapid advancement of 3D acquisition technologies there has been a big increase in 3D shape data that requires a variety of shape analysis methods, hence the need for this comprehensive resource.
1. Introduction2. Spectral Geometry Operation3. Spectral Geometric Features for Shapes4. Isometric Shape Analysis Using Spectral Geometry5. Near Isometric Shape Motion Analysis Using Spectral Geometry6. Non-Isometric Shape Motion Analysis by Variation of Shape Spectrum7. Machine Learning of Spectral Geometry8. Conclusions
Zichun Zhong is Assistant Professor of Computer Science at Wayne State University (WSU) since August 2015. He was a postdoctoral fellow in Department of Radiation Oncology at UT Southwestern Medical Center at Dallas (UTSW) from August 2014 to August 2015. He received Ph.D. degree in Computer Science at The University of Texas at Dallas (UTD) in Summer 2014, a B.S. degree in Computer Science and Technology (Software Engineering) and a M.S. degree in Computer Science in The University of Electronic Science and Technology of China (UESTC) in 2006 and 2009, respectively.
Dr. Jiaxi Hu is a software engineer at Google LLC, Mountain View, California. He received his B.S. and M.S. from Huazhong University of Science and Technology, Wuhan, China, and his Ph.D. from Wayne State University, Detroit, Michigan before he joined Google LLC in 2014. After a decade of research of C
- Presents the latest advances in spectral geometric processing for 3D shape analysis applications, such as shape classification, shape matching, medical imaging, etc.
- Provides intuitive links between fundamental geometric theories and real-world applications, thus bridging the gap between theory and practice
- Describes new theoretical breakthroughs in applying spectral methods for non-isometric motion analysis
- Gives insights for developing spectral geometry-based approaches for 3D shape analysis and deep learning of shape geometry