Computer Vision and Machine Learning with RGB-D Sensors

1. Front Matter

   Pages i-x

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2. Surveys

   1. Front Matter

      Pages 1-1

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   2. 3D Depth Cameras in Vision: Benefits and Limitations of the Hardware

      • Achuta Kadambi, Ayush Bhandari, Ramesh Raskar

      Pages 3-26

   3. A State of the Art Report on Multiple RGB-D Sensor Research and on Publicly Available RGB-D Datasets

      • Kai Berger

      Pages 27-44

3. Reconstruction, Mapping and Synthesis

   1. Front Matter

      Pages 45-45

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   2. Calibration Between Depth and Color Sensors for Commodity Depth Cameras

      • Cha Zhang, Zhengyou Zhang

      Pages 47-64

   3. Depth Map Denoising via CDT-Based Joint Bilateral Filter

      • Andreas Koschan, Mongi Abidi

      Pages 65-89

   4. Human Performance Capture Using Multiple Handheld Kinects

      • Yebin Liu, Genzhi Ye, Yangang Wang, Qionghai Dai, Christian Theobalt

      Pages 91-108

   5. Human-Centered 3D Home Applications via Low-Cost RGBD Cameras

      • Zhenbao Liu, Shuhui Bu, Junwei Han

      Pages 109-135

   6. Matching of 3D Objects Based on 3D Curves

      • Christian Feinen, Joanna Czajkowska, Marcin Grzegorzek, Longin Jan Latecki

      Pages 137-155

   7. Using Sparse Optical Flow for Two-Phase Gas Flow Capturing with Multiple Kinect

      • Kai Berger, Marc Kastner, Yannic Schroeder, Stefan Guthe

      Pages 157-169

4. Detection, Segmentation and Tracking

   1. Front Matter

      Pages 171-171

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   2. RGB-D Sensor-Based Computer Vision Assistive Technology for Visually Impaired Persons

      • Yingli Tian

      Pages 173-194

   3. RGB-D Human Identification and Tracking in a Smart Environment

      • Jungong Han, Junwei Han

      Pages 195-211

5. Learning-based Recognition

   1. Front Matter

      Pages 213-213

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   2. Feature Descriptors for Depth-Based Hand Gesture Recognition

      • Fabio Dominio, Giulio Marin, Mauro Piazza, Pietro Zanuttigh

      Pages 215-237

   3. Hand Parsing and Gesture Recognition with a Commodity Depth Camera

      • Hui Liang, Junsong Yuan

      Pages 239-265

   4. Learning Fast Hand Pose Recognition

      • Eyal Krupka, Alon Vinnikov, Ben Klein, Aharon Bar-Hillel, Daniel Freedman, Simon Stachniak et al.

      Pages 267-287

   5. Real-Time Hand Gesture Recognition Using RGB-D Sensor

      • Yuan Yao, Fan Zhang, Yun Fu

      Pages 289-313

6. Back Matter

   Pages 315-316

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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.

• Computer Vision
• Consumer Electronics
• Human-Computer Interaction
• Intelligent Systems
• Machine Learning
• Pattern Recognition
• RGB-D Sensors

• University of Sheffield, United Kingdom

  Ling Shao

• Civolution Technology, Eindhoven, The Netherlands

  Jungong Han

• Microsoft Research, Cambridge, United Kingdom

  Pushmeet Kohli

• Microsoft Research, Redmond, USA

  Zhengyou Zhang

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.