| Description |
1 online resource (374 pages) |
| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Summary |
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 literature. It also establishes links between solutions proposed by different communities that studied 3D shape, such as mathematics and statistics, medical imaging, computer vision, and computer graphics. The first part of 3D Shape Analysis: Fundamentals, Theory, and Applications provides a review of the background concepts such as methods for the acquisition and representation of 3D geometries, and the fundamentals of geometry and topology. It specifically covers stereo matching, structured light, and intrinsic vs. extrinsic properties of shape. Parts 2 and 3 present a range of mathematical and algorithmic tools (which are used for e.g., global descriptors, keypoint detectors, local feature descriptors, and algorithms) that are commonly used for the detection, registration, recognition, classification, and retrieval of 3D objects. Both also place strong emphasis on recent techniques motivated by the spread of commodity devices for 3D acquisition. Part 4 demonstrates the use of these techniques in a selection of 3D shape analysis applications. It covers 3D face recognition, object recognition in 3D scenes, and 3D shape retrieval. It also discusses examples of semantic applications and cross domain 3D retrieval, i.e. how to retrieve 3D models using various types of modalities, e.g. sketches and/or images. The book concludes with a summary of the main ideas and discussions of the future trends. 3D Shape Analysis: Fundamentals, Theory, and Applications is an excellent reference for graduate students, researchers, and professionals in different fields of mathematics, computer science, and engineering. It is also ideal for courses in computer vision and computer graphics, as well as for those seeking 3D industrial/commercial solutions |
| Notes |
Print version record |
| Subject |
Three-dimensional imaging.
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Pattern recognition systems.
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Shapes -- Computer simulation
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Machine learning.
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three-dimensional.
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MATHEMATICS -- Geometry -- Algebraic.
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Machine learning
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Pattern recognition systems
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Three-dimensional imaging
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| Form |
Electronic book
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| Author |
Laga, Hamid
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Guo, Yulan
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Fisher, Robert B
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Bennamoun, Mohammed
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| ISBN |
9781119405191 |
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111940519X |
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9781119405184 |
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1119405181 |
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