Computer vision and machine learning with RGB-D sensors

Author(s)

• Shao, Ling

Bibliographic Information

Computer vision and machine learning with RGB-D sensors

Ling Shao ... [et al.], editors

（Advances in computer vision and pattern recognition / Sameer Singh, Sing Bing Kang, series editors）

Springer, c2014

Description and Table of Contents

Description

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.

Table of Contents

Part I: Surveys 3D Depth Cameras in Vision: Benefits and Limitations of the Hardware Achuta Kadambi, Ayush Bhandari and Ramesh Raskar A State-of-the-Art Report on Multiple RGB-D Sensor Research and on Publicly Available RGB-D Datasets Kai Berger Part II: Reconstruction, Mapping and Synthesis Calibration Between Depth and Color Sensors for Commodity Depth Cameras Cha Zhang and Zhengyou Zhang Depth Map Denoising via CDT-Based Joint Bilateral Filter Andreas Koschan and Mongi Abidi Human Performance Capture Using Multiple Handheld Kinects Yebin Liu, Genzhi Ye, Yangang Wang, Qionghai Dai and Christian Theobalt Human Centered 3D Home Applications via Low-Cost RGBD Cameras Zhenbao Liu, Shuhui Bu and Junwei Han Matching of 3D Objects Based on 3D Curves Christian Feinen, Joanna Czajkowska, Marcin Grzegorzek and Longin Jan Latecki Using Sparse Optical Flow for Two-Phase Gas Flow Capturing with Multiple Kinects Kai Berger, Marc Kastner, Yannic Schroeder and Stefan Guthe Part III: Detection, Segmentation and Tracking RGB-D Sensor-Based Computer Vision Assistive Technology for Visually Impaired Persons Yingli Tian RGB-D Human Identification and Tracking in a Smart Environment Jungong Han and Junwei Han Part IV: Learning-Based Recognition Feature Descriptors for Depth-Based Hand Gesture Recognition Fabio Dominio, Giulio Marin, Mauro Piazza and Pietro Zanuttigh Hand Parsing and Gesture Recognition with a Commodity Depth Camera Hui Liang and Junsong Yuan Learning Fast Hand Pose Recognition Eyal Krupka, Alon Vinnikov, Ben Klein, Aharon Bar Hillel, Daniel Freedman, Simon Stachniak and Cem Keskin Realtime Hand-Gesture Recognition Using RGB-D Sensor Yuan Yao, Fan Zhang and Yun Fu

by "Nielsen BookData"

Details

• NCID: BB17037072
• ISBN:
  • 9783319086507
• LCCN: 2014943409
• Country Code: sz
• Title Language Code: eng
• Text Language Code: eng
• Place of Publication: Cham
• Pages/Volumes: x, 316 p.
• Size: 24 cm
• Parent Bibliography ID:
  • BB07744302