Computer Vision and Machine Learning with RGB-D Sensors - Shao, Ling; Han, Jungong; Kohli, Pushmeet; Zhang, Zhengyou (Advances in Computer Vision and Pattern Recognition,, 2014)
The combination of high-resolution visual and depth sensing, supported by machine learning, opens up new opportunities to solve real-world problems in computer vision. This authoritative text/reference presents an interdisciplinary selection of important, cutting-edge research on RGB-D based computer vision. Divided into four sections, the book opens with a detailed survey of the field, followed by a focused examination of RGB-D based 3D reconstruction, mapping and synthesis. The work continues with a section devoted to novel techniques that employ depth data for object detection, segmentation and tracking, and concludes with examples of accurate human action interpretation aided by depth sensors. Topics and 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 innovative 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 Researchers and practitioners working in computer vision, HCI and machine learning will find this to be a must-read text. The book also serves as a useful reference for graduate students studying computer vision, pattern recognition or multimedia.