3D Shape Analysis : Fundamentals, Theory and Applications.

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An in-depth description of the state-of-the-art of 3D shape analysis techniques and their applications This book discusses the different topics that come under the title of "3D shape analysis". It covers the theoretical foundations and the major solutions that have been presented in the li...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Tabia, Hedi
Otros Autores: Laga, Hamid, Guo, Yulan, Fisher, Robert B., Bennamoun, Mohammed
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2018.
Temas:
Three-dimensional imaging.
Pattern recognition systems.
Shapes > Computer simulation.
Machine learning.
Imagerie tridimensionnelle.
Reconnaissance des formes (Informatique)
Formes > Simulation par ordinateur.
Apprentissage automatique.
three-dimensional.
MATHEMATICS > Geometry > Algebraic.
Machine learning
Pattern recognition systems
Three-dimensional imaging
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro; Table of Contents; Preface; Acknowledgments; 1 Introduction; 1.1 Motivation; 1.2 The 3D Shape Analysis Problem; 1.3 About This Book; 1.4 Notation; Part I: Foundations; 2 Basic Elements of 3D Geometry and Topology; 2.1 Elements of Differential Geometry; 2.2 Shape, Shape Transformations, and Deformations; 2.3 Summary and Further Reading; 3 3D Acquisition and Preprocessing; 3.1 Introduction; 3.2 3D Acquisition; 3.3 Preprocessing 3D Models; 3.4 Summary and Further Reading; Part II: 3D Shape Descriptors; 4 Global Shape Descriptors; 4.1 Introduction; 4.2 Distribution-Based Descriptors
  • 4.3 View-Based 3D Shape Descriptors4.4 Spherical Function-Based Descriptors; 4.5 Deep Neural Network-Based 3D Descriptors; 4.6 Summary and Further Reading; 5 Local Shape Descriptors; 5.1 Introduction; 5.2 Challenges and Criteria; 5.3 3D Keypoint Detection; 5.4 Local Feature Description; 5.5 Feature Aggregation Using Bag of Feature Techniques; 5.6 Summary and Further Reading; Part III: 3D Correspondence and Registration; 6 Rigid Registration; 6.1 Introduction; 6.2 Coarse Registration; 6.3 Fine Registration; 6.4 Summary and Further Reading; 7 Nonrigid Registration; 7.1 Introduction
  • 7.2 Problem Formulation7.3 Mathematical Tools; 7.4 Isometric Correspondence and Registration; 7.5 Nonisometric (Elastic) Correspondence and Registration; 7.6 Summary and Further Reading; 8 Semantic Correspondences; 8.1 Introduction; 8.2 Mathematical Formulation; 8.3 Graph Representation; 8.4 Energy Functions for Semantic Labeling; 8.5 Semantic Labeling; 8.6 Examples; 8.7 Summary and Further Reading; Part IV: Applications; 9 Examples of 3D Semantic Applications; 9.1 Introduction; 9.2 Semantics: Shape or Status; 9.3 Semantics: Class or Identity; 9.4 Semantics: Behavior; 9.5 Semantics: Position
  • 9.6 Summary and Further Reading10 3D Face Recognition; 10.1 Introduction; 10.2 3D Face Recognition Tasks, Challenges and Datasets; 10.3 3D Face Recognition Methods; 10.4 Summary; 11 Object Recognition in 3D Scenes; 11.1 Introduction; 11.2 Surface Registration-Based Object Recognition Methods; 11.3 Machine Learning-Based Object Recognition Methods; 11.4 Summary and Further Reading; 12 3D Shape Retrieval; 12.1 Introduction; 12.2 Benchmarks and Evaluation Criteria; 12.3 Similarity Measures; 12.4 3D Shape Retrieval Algorithms; 12.5 Summary and Further Reading; 13 Cross-domain Retrieval
  • 13.1 Introduction13.2 Challenges and Datasets; 13.3 Siamese Network for Cross-domain Retrieval; 13.4 3D Shape-centric Deep CNN; 13.5 Summary and Further Reading; 14 Conclusions and Perspectives; References; Index; End User License Agreement

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