TY - BOOK AU - Shao,Ling AU - Han,Jungong AU - Kohli,Pushmeet AU - Zhang,Zhengyou TI - Computer vision and machine learning with RGB-D sensors T2 - Advances in computer vision and pattern recognition SN - 9783319086507 U1 - 006.37 23 PY - 2014/// CY - Switzerland : PB - Springer, KW - Computer vision. KW - Machine learning N1 - 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 N2 - 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 ER -