Computer Vision and Machine Learning with RGB-D Sensors

  • Ling Shao
  • Jungong Han
  • Pushmeet Kohli
  • Zhengyou Zhang

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

Table of contents

  1. Front Matter
    Pages i-x
  2. Surveys

  3. Reconstruction, Mapping and Synthesis

    1. Front Matter
      Pages 45-45
    2. Andreas Koschan, Mongi Abidi
      Pages 65-89
    3. Yebin Liu, Genzhi Ye, Yangang Wang, Qionghai Dai, Christian Theobalt
      Pages 91-108
    4. Zhenbao Liu, Shuhui Bu, Junwei Han
      Pages 109-135
    5. Christian Feinen, Joanna Czajkowska, Marcin Grzegorzek, Longin Jan Latecki
      Pages 137-155
    6. Kai Berger, Marc Kastner, Yannic Schroeder, Stefan Guthe
      Pages 157-169
  4. Detection, Segmentation and Tracking

  5. Learning-based Recognition

    1. Front Matter
      Pages 213-213
    2. Fabio Dominio, Giulio Marin, Mauro Piazza, Pietro Zanuttigh
      Pages 215-237
    3. Eyal Krupka, Alon Vinnikov, Ben Klein, Aharon Bar-Hillel, Daniel Freedman, Simon Stachniak et al.
      Pages 267-287
    4. Yuan Yao, Fan Zhang, Yun Fu
      Pages 289-313
  6. Back Matter
    Pages 315-316

About this book


The combination of high-resolution visual and depth sensing, supported by machine learning, opens up new opportunities to solve real-world problems in computer vision.

This authoritative text/reference presents an interdisciplinary selection of important, cutting-edge research on RGB-D based computer vision. Divided into four sections, the book opens with a detailed survey of the field, followed by a focused examination of RGB-D based 3D reconstruction, mapping and synthesis. The work continues with a section devoted to novel techniques that employ depth data for object detection, segmentation and tracking, and concludes with examples of accurate human action interpretation aided by depth sensors.

Topics and 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 innovative 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

Researchers and practitioners working in computer vision, HCI and machine learning will find this to be a must-read text. The book also serves as a useful reference for graduate students studying computer vision, pattern recognition or multimedia.


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

Editors and affiliations

  • Ling Shao
    • 1
  • Jungong Han
    • 2
  • Pushmeet Kohli
    • 3
  • Zhengyou Zhang
    • 4
  1. 1.University of SheffieldUnited Kingdom
  2. 2.Civolution TechnologyEindhovenThe Netherlands
  3. 3.Microsoft ResearchCambridgeUnited Kingdom
  4. 4.Microsoft ResearchRedmondUSA

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-08650-7
  • Online ISBN 978-3-319-08651-4
  • Series Print ISSN 2191-6586
  • Series Online ISSN 2191-6594
  • About this book