Computer Vision and Machine Learning with RGB-D Sensors Gebunden

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 t he 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.