Local image descriptor : modern approaches /: modern approaches. (2015)
- Record Type:
- Book
- Title:
- Local image descriptor : modern approaches /: modern approaches. (2015)
- Main Title:
- Local image descriptor : modern approaches
- Further Information:
- Note: Bin Fan, Zhenhua Wang, Fuchao Wu.
- Authors:
- Fan, Bin
Wang, Zhenhua
Wu, Fuchao - 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. 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. 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 InvariantForeword 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. 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. 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. 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. 6.1.3 Benchmarks for Image Retrieval6.2 Conclusion Remarks and Future Work; References. … (more)
- Publisher Details:
- Berlin : Springer
- Publication Date:
- 2015
- Extent:
- 1 online resource (xii, 99 pages), illustrations (some color)
- Subjects:
- 006.6
Computer vision
COMPUTERS -- General
Computer vision
Computer Science
Image Processing and Computer Vision
Pattern Recognition
Artificial Intelligence (incl. Robotics)
Electronic books - Languages:
- English
- ISBNs:
- 9783662491737
3662491737 - Related ISBNs:
- 3662491710
9783662491713 - Notes:
- Note: Includes bibliographical references.
Note: Online resource; title from PDF title page (SpringerLink, viewed January 14, 2016). - Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.401348
- Ingest File:
- 02_443.xml