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Computer vision and machine learning with RGB-D sensors / [edited by] Ling Shao...[et al.].

Contributor(s): Material type: TextTextSeries: Advances in computer vision and pattern recognitionPublication details: Switzerland : Springer, 2014.Description: x, 316 p. : illustrations (chiefly color) ; 24 cmISBN:
  • 9783319086507
Subject(s): DDC classification:
  • 006.37 23 Sh528
Contents:
Part I: Surveys -- 1. 3D Depth Cameras in Vision: Benefits and Limitations of the Hardware -- 2. A State-of-the-Art Report on Multiple RGB-D Sensor Research and on Publicly Available RGB-D Datasets -- Part II: Reconstruction, Mapping and Synthesis -- 3. Calibration Between Depth and Color Sensors for Commodity Depth Cameras -- 4. Depth Map Denoising via CDT-Based Joint Bilateral Filter -- 5. Human Performance Capture Using Multiple Handheld Kinects -- 6. Human Centered 3D Home Applications via Low-Cost RGBD Cameras -- 7. Matching of 3D Objects Based on 3D Curves -- 8. Using Sparse Optical Flow for Two-Phase Gas Flow Capturing with Multiple Kinects -- Part III: Detection, Segmentation and Tracking -- 9. RGB-D Sensor-Based Computer Vision Assistive Technology for Visually Impaired Persons -- 10. RGB-D Human Identification and Tracking in a Smart Environment -- Part IV: Learning-Based Recognition -- 11. Feature Descriptors for Depth-Based Hand Gesture Recognition -- 12. Hand Parsing and Gesture Recognition with a Commodity Depth Camera -- 13. Learning Fast Hand Pose Recognition -- 14. Real time Hand-Gesture Recognition Using RGB-D Sensor-- Index.
Summary: This book presents an interdisciplinary selection of cutting-edge research on RGB-D based computer vision. Features: discusses the calibration of color and depth cameras, the reduction of noise on depth maps and methods for capturing human performance in 3D; reviews a selection of applications which use RGB-D information to reconstruct human figures, evaluate energy consumption and obtain accurate action classification; presents an approach for 3D object retrieval and for the reconstruction of gas flow from multiple Kinect cameras; describes an RGB-D computer vision system designed to assist the visually impaired and another for smart-environment sensing to assist elderly and disabled people; examines the effective features that characterize static hand poses and introduces a unified framework to enforce both temporal and spatial constraints for hand parsing; proposes a new classifier architecture for real-time hand pose recognition and a novel hand segmentation and gesture recognition system.
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Includes index.

Part I: Surveys --
1. 3D Depth Cameras in Vision: Benefits and Limitations of the Hardware --
2. A State-of-the-Art Report on Multiple RGB-D Sensor Research and on Publicly Available RGB-D Datasets --

Part II: Reconstruction, Mapping and Synthesis --
3. Calibration Between Depth and Color Sensors for Commodity Depth Cameras --
4. Depth Map Denoising via CDT-Based Joint Bilateral Filter --
5. Human Performance Capture Using Multiple Handheld Kinects --
6. Human Centered 3D Home Applications via Low-Cost RGBD Cameras --
7. Matching of 3D Objects Based on 3D Curves --
8. Using Sparse Optical Flow for Two-Phase Gas Flow Capturing with Multiple Kinects --

Part III: Detection, Segmentation and Tracking --
9. RGB-D Sensor-Based Computer Vision Assistive Technology for Visually Impaired Persons --
10. RGB-D Human Identification and Tracking in a Smart Environment --

Part IV: Learning-Based Recognition --
11. Feature Descriptors for Depth-Based Hand Gesture Recognition --
12. Hand Parsing and Gesture Recognition with a Commodity Depth Camera --
13. Learning Fast Hand Pose Recognition --
14. Real time Hand-Gesture Recognition Using RGB-D Sensor--
Index.

This book presents an interdisciplinary selection of cutting-edge research on RGB-D based computer vision. Features: discusses the calibration of color and depth cameras, the reduction of noise on depth maps and methods for capturing human performance in 3D; reviews a selection of applications which use RGB-D information to reconstruct human figures, evaluate energy consumption and obtain accurate action classification; presents an approach for 3D object retrieval and for the reconstruction of gas flow from multiple Kinect cameras; describes an RGB-D computer vision system designed to assist the visually impaired and another for smart-environment sensing to assist elderly and disabled people; examines the effective features that characterize static hand poses and introduces a unified framework to enforce both temporal and spatial constraints for hand parsing; proposes a new classifier architecture for real-time hand pose recognition and a novel hand segmentation and gesture recognition system.

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