Computer Vision and Machine Learning with RGB-D Sensors (Advances in Computer Vision and Pattern Recognition)

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.

本書介紹了基於RGB-D的計算機視覺的跨學科前沿研究。特點包括：討論了彩色和深度相機的校準、深度圖噪聲的減少以及捕捉3D人體表現的方法；回顧了一些應用，這些應用使用RGB-D信息重建人體形象、評估能源消耗並獲得準確的動作分類；提出了一種用於從多個Kinect相機重建氣體流動的3D物體檢索方法；描述了一個旨在幫助視覺障礙者的RGB-D計算機視覺系統，以及一個旨在幫助老年人和殘障人士的智能環境感知系統；檢查了表徵靜態手勢的有效特徵，並引入了一個統一的框架，以強制實施手勢解析的時間和空間約束；提出了一種用於實時手勢識別的新的分類器架構，以及一種新的手部分割和手勢識別系統。