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RGB-D Image Analysis and Processing

  • Paul L. Rosin
  • Yu-Kun Lai
  • Ling Shao
  • Yonghuai Liu
Book

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

Table of contents

  1. Front Matter
    Pages i-xi
  2. RGB-D Data Acquisition and Processing

    1. Front Matter
      Pages 1-1
    2. Michael Zollhöfer
      Pages 3-13
    3. Jingyu Yang, Zhongyu Jiang, Xinchen Ye, Kun Li
      Pages 51-65
    4. Pablo Rodríguez-Gonzálvez, Gabriele Guidi
      Pages 67-86
    5. Charles Malleson, Jean-Yves Guillemaut, Adrian Hilton
      Pages 87-115
    6. Javier Civera, Seong Hun Lee
      Pages 117-144
  3. RGB-D Data Analysis

    1. Front Matter
      Pages 167-167
    2. Isaac Ronald Ward, Hamid Laga, Mohammed Bennamoun
      Pages 169-201
    3. Tongwei Ren, Ao Zhang
      Pages 203-220
    4. Runmin Cong, Hao Chen, Hongyuan Zhu, Huazhu Fu
      Pages 221-241
    5. Caner Sahin, Guillermo Garcia-Hernando, Juil Sock, Tae-Kyun Kim
      Pages 243-265
    6. Song-Hai Zhang, Yu-Kun Lai
      Pages 267-282
  4. RGB-D Applications

    1. Front Matter
      Pages 283-283
    2. Max Schwarz, Sven Behnke
      Pages 285-307
    3. Gabriel Moyà-Alcover, Ines Ayed, Javier Varona, Antoni Jaume-i-Capó
      Pages 335-353
    4. Liuhao Ge, Junsong Yuan, Nadia Magnenat Thalmann
      Pages 355-376
    5. Cristiano Premebida, Gledson Melotti, Alireza Asvadi
      Pages 377-395
    6. Emanuele Frontoni, Marina Paolanti, Rocco Pietrini
      Pages 397-425
  5. Back Matter
    Pages 427-524

About this book

Introduction

This book focuses on the fundamentals and recent advances in RGB-D imaging as well as covering a range of RGB-D applications. The topics covered include: data acquisition, data quality assessment, filling holes, 3D reconstruction, SLAM, multiple depth camera systems, segmentation, object detection, salience detection, pose estimation, geometric modelling, fall detection, autonomous driving, motor rehabilitation therapy, people counting and cognitive service robots.

The availability of cheap RGB-D sensors has led to an explosion over the last five years in the capture and application of colour plus depth data. The addition of depth data to regular RGB images vastly increases the range of applications, and has resulted in a demand for robust and real-time processing of RGB-D data. There remain many technical challenges, and RGB-D image processing is an ongoing research area. This book covers the full state of the art, and consists of a series of chapters by internationally renowned experts in the field. Each chapter is written so as to provide a detailed overview of that topic. RGB-D Image Analysis and Processing will enable both students and professional developers alike to quickly get up to speed with contemporary techniques, and apply RGB-D imaging in their own projects.

Keywords

RGB-D Images Computer Vision 3D Shape Analysis RGB-D Applications 3D Image Analysis

Editors and affiliations

  • Paul L. Rosin
    • 1
  • Yu-Kun Lai
    • 2
  • Ling Shao
    • 3
  • Yonghuai Liu
    • 4
  1. 1.School of Computer Science and InformaticsCardiff UniversityCardiffUK
  2. 2.School of Computer Science and InformaticsCardiff UniversityCardiffUK
  3. 3.IEEEUniversity of East AngliaNorwichUK
  4. 4.Department of Computer ScienceEdge Hill UniversityOrmskirkUK

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-28603-3
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-28602-6
  • Online ISBN 978-3-030-28603-3
  • Series Print ISSN 2191-6586
  • Series Online ISSN 2191-6594
  • Buy this book on publisher's site