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Spectral Geometry of Shapes. Computer Vision and Pattern Recognition

  • ID: 4482967
  • Book
  • October 2019
  • Region: Global
  • 152 Pages
  • Elsevier Science and Technology
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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.

  • 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

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1. Introduction 2. Spectral Geometry Operation 3. Spectral Geometric Features for Shapes 4. Isometric Shape Analysis Using Spectral Geometry 5. Near Isometric Shape Motion Analysis Using Spectral Geometry 6. Non-Isometric Shape Motion Analysis by Variation of Shape Spectrum 7. Machine Learning of Spectral Geometry 8. Conclusions

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Hua, Jing
Dr. Jing Hua is a Professor of Computer Science and the founding director of Computer Graphics and Imaging Lab (GIL) and Visualization Lab (VIS) at Computer Science at Wayne State University (WSU). He received his Ph.D. degree (2004) in Computer Science from the State University of New York at Stony Brook. He also received his M.S. degree (1999) in Pattern Recognition and Artificial Intelligence from the Institute of Automation, Chinese Academy of Sciences in Beijing, China and his B.S. degree (1996) in Electrical Engineering from the Huazhong University of Science & Technology in Wuhan, China. His research interests include Computer Graphics, Visualization, Image Analysis and Informatics, Computer Vision, etc. He received the Gaheon Award for the Best Paper of International Journal of CAD/CAM in 2009, the Best Paper Award at ACM Solid Modeling 2004, the WSU Faculty Research Award in 2005, the College of Liberal Arts and Sciences Excellence in Teaching Award in 2008, the K. C. Wong Research Award in 2010, and the Best Demo Awards at GENI Engineering Conference 21 (2014) and 23 (2015), respectively.
Zhong, Zichun
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.
Hu, Jiaxi
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 Computer Graphics in the university, Dr. Hu brought his expertise to the world through Google web services. He worked on Imagery Viewer which is a backend rendering engine to serve a broad range of Google products, such as Google Map, Street View, Local Search, etc., and third-party partners. After that, he found the new challenge in bringing Google Assistant to Next Billion Users.
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