| Description |
1 online resource (xii, 99 pages) : illustrations (some color) |
| Series |
SpringerBriefs in computer science, 2191-5768 |
|
SpringerBriefs in computer science. 2191-5768
|
| Contents |
Foreword 1; Foreword 2; Preface; Contents; 1 Introduction; References; 2 Classical Local Descriptors; 2.1 Scale-Invariant Feature Transform (SIFT); 2.1.1 Scale Space Representation in SIFT; 2.1.2 Keypoint Detection; 2.1.3 Feature Description; 2.2 Speeded Up Robust Feature (SURF); 2.2.1 Integral Image; 2.2.2 Scale Space Representation in SURF; 2.2.3 Scale-Invariant Interest Point Detection; 2.2.4 Orientation Assignment and Descriptor Construction; 2.3 Local Binary Pattern and Its Variants; References; 3 Intensity Order-Based Local Descriptors |
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3.1 Ordinal and Spatial Intensity Distribution Descriptor (OSID)3.2 Intensity Order-Based Pooling for Feature Description; 3.2.1 An Analysis of the Geometric-Based Spatial Pooling; 3.2.2 Intensity Order-Based Patch Division; 3.2.3 Construction of MROGH and MRRID Descriptors; 3.3 Local Intensity Order Pattern for Feature Description; 3.3.1 Construction of the LIOP Descriptor; 3.4 Intensity Order-Based Binary Descriptor; 3.4.1 Subregions Generation; 3.4.2 Regional Invariants and Pairwise Comparisons; 3.4.3 Learning Good Binary Descriptor; 3.4.4 Using Multiple Support Regions |
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3.4.5 Cascade Filtering for Speeding up MatchingReferences; 4 Burgeoning Methods: Binary Descriptors; 4.1 BRIEF: Binary Robust Independent Elementary Features; 4.2 ORB: Oriented FAST and Rotated BRIEF; 4.2.1 Scale Invariant FAST Detector; 4.2.2 Orientation Computation by Intensity Centriod; 4.2.3 Learning Good Binary Features; 4.3 BRISK: Binary Robust and Invariant Scalable Keypoints; 4.3.1 Keypoint Detection; 4.3.2 Orientation Assignment and Keypoint Description; 4.4 FREAK: Fast Retina Keypoint; 4.4.1 Descriptor Construction; 4.4.2 Saccadic Matching with FREAK |
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4.5 FRIF: Fast Robust Invariant Feature4.5.1 FALoG Detector; 4.5.2 Mixed Binary Descriptor; 4.6 Learning Binary Descriptors by Supervised Information; 4.6.1 From Raw Image Patch; 4.6.2 From an Intermediate Representation; References; 5 Visual Applications; 5.1 Structure from Motion and 3D Reconstruction; 5.2 Object Recognition; 5.3 Content-Based Image Retrieval; 5.4 Simultaneous Localization and Mapping (SLAM); References; 6 Resources and Future Work; 6.1 Dataset and Evaluation Protocol; 6.1.1 Benchmarks for Image Matching; 6.1.2 Benchmarks for Object Recognition |
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6.1.3 Benchmarks for Image Retrieval6.2 Conclusion Remarks and Future Work; References |
| Summary |
This book covers a wide range of local image descriptors, from the classical ones to the state of the art, as well as the burgeoning research topics on this area. The goal of this effort is to let readers know what are the most popular and useful methods in the current, what are the advantages and the disadvantages of these methods, which kind of methods is best suitable for their problems or applications, and what is the future of this area. What is more, hands-on exemplars supplied in this book will be of great interest to Computer Vision engineers and practitioners, as well as those want to begin their research in this area. Overall, this book is suitable for graduates, researchers and engineers in the related areas both as a learning text and as a reference book |
| Bibliography |
Includes bibliographical references |
| Subject |
Computer vision.
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| Form |
Electronic book
|
| Author |
Wang, Zhenhua, author
|
|
Wu, Fuchao, author
|
| ISBN |
3662491737 (electronic bk.) |
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9783662491737 (electronic bk.) |
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