Shao / Han / Kohli Computer Vision and Machine Learning with RGB-D Sensors

1. Auflage 2014
ISBN: 978-3-319-08651-4
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 316 Seiten, eBook

Reihe: 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.

Zielgruppe

Research

Autoren/Hrsg.

Shao, Ling

Han, Jungong

Kohli, Pushmeet

Zhang, Zhengyou

Inhaltsverzeichnis

Part I: Surveys.- 3D Depth Cameras in Vision: Benefits and Limitations of the Hardware.- 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.- Calibration Between Depth and Color Sensors for Commodity Depth Cameras.- Depth Map Denoising via CDT-Based Joint Bilateral Filter.- Human Performance Capture Using Multiple Handheld Kinects.- Human Centered 3D Home Applications via Low-Cost RGBD Cameras.- Matching of 3D Objects Based on 3D Curves.- Using Sparse Optical Flow for Two-Phase Gas Flow Capturing with Multiple Kinects.- Part III: Detection, Segmentation and Tracking.- RGB-D Sensor-Based Computer Vision Assistive Technology for Visually Impaired Persons.- RGB-D Human Identification and Tracking in a Smart Environment.- Part IV: Learning-Based Recognition.- Feature Descriptors for Depth-Based Hand Gesture Recognition.- Hand Parsing and Gesture Recognition with a Commodity Depth Camera.- Learning Fast Hand Pose Recognition.- Real time Hand-Gesture Recognition Using RGB-D Sensor.

Über Autor(innen)

Dr. Ling Shao is a Senior Lecturer (Associate Professor) in the Department of Electronic and Electrical Engineering at the University of Sheffield, UK. His publications include the Springer title Multimedia Interaction and Intelligent User Interfaces . Dr. Jungong Han is a Senior Scientist at Civolution Technology, Eindhoven, and a Guest Researcher at the Eindhoven University of Technology, Netherlands. Dr. Pushmeet Kohli is a Senior Researcher in the Machine Learning and Perception Group at Microsoft Research Cambridge and an Associate in the Psychometrics Centre at the University of Cambridge, UK. Dr. Zhengyou Zhang , IEEE Fellow and ACM Fellow, is a Principal Researcher and Research Manager of the Multimedia, Interaction, and Communication Group at Microsoft Research Redmond, WA, USA.