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Computer Vision and Machine Learning with RGB-D Sensors

  • Book
  • © 2014

Overview

Editors:
  1. Ling Shao

    1. University of Sheffield, United Kingdom

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  2. Jungong Han

    1. Civolution Technology, Eindhoven, The Netherlands

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  3. Pushmeet Kohli

    1. Microsoft Research, Cambridge, United Kingdom

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  4. Zhengyou Zhang

    1. Microsoft Research, Redmond, USA

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  • Describes recent advances in RGB-D based computer vision algorithms, with an emphasis on advanced machine learning techniques for interpreting the RGBD information
  • Covers a range of different techniques from computer vision, machine learning, audio, speech and signal processing, communications, artificial intelligence and media technology
  • Includes contributions from leading researchers in this area, with strong industrial-research experience of the practical issues
  • Includes supplementary material: sn.pub/extras

Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)

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Table of contents (14 chapters)

  1. Front Matter

    Pages i-x
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  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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Keywords

About this book

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.

Editors and Affiliations

  • 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

About the editors

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.

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