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Ricci Flow for Shape Analysis and Surface Registration introduces the beautiful and profound Ricci flow theory in a discrete setting. By using basic tools in linear algebra and multivariate calculus, readers can deduce all the major theorems in surface Ricci flow by themselves. The authors adapt the Ricci flow theory to practical computational algorithms, apply Ricci flow for shape analysis and surface registration, and demonstrate the power of Ricci flow in many applications in medical imaging, computer graphics, computer vision and wireless sensor network. Due to minimal pre-requisites, this book is accessible to engineers and medical experts, including educators, researchers, students and industry engineers who have an interest in solving real problems related to shape analysis and surface registration.
This book introduces the theory and applications of Ricci flow for shape analysis and surface registration, adapting complex mathematical concepts to practical computational algorithms. The authors, Wei Zeng and Xianfeng David Gu, leverage basic tools from linear algebra and multivariate calculus to make the profound Ricci flow theory accessible in a discrete setting. The text focuses on adapting this theory for computational algorithms, applying it to shape analysis and surface registration, and showcasing its utility across various fields. Its minimal prerequisites ensure accessibility for engineers, medical experts, researchers, and students interested in solving real-world problems.
The book is designed to be accessible to a broad audience, including engineers and medical experts, due to its minimal prerequisites. The authors aim to empower readers to deduce major theorems themselves, fostering a deeper understanding of Ricci flow theory. Its practical applications in fields like medical imaging and computer graphics are highlighted, suggesting a focus on bridging theoretical mathematics with real-world problem-solving. The text appears to offer a comprehensive approach to understanding and applying Ricci flow for complex analytical tasks.
Page Count:
139
Publication Date:
2013-10-18
Publisher:
Springer New York
ISBN-10:
1461487803
ISBN-13:
9781461487807
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