Local Image Descriptor: Modern Approaches

  • Bin Fan
  • Zhenhua Wang
  • Fuchao Wu

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Bin Fan, Zhenhua Wang, Fuchao Wu
    Pages 1-3
  3. Bin Fan, Zhenhua Wang, Fuchao Wu
    Pages 5-24
  4. Bin Fan, Zhenhua Wang, Fuchao Wu
    Pages 25-41
  5. Bin Fan, Zhenhua Wang, Fuchao Wu
    Pages 43-67
  6. Bin Fan, Zhenhua Wang, Fuchao Wu
    Pages 69-87
  7. Bin Fan, Zhenhua Wang, Fuchao Wu
    Pages 89-99

About this book

Introduction

This book covers a wide range of local image descriptors, from the classical ones to the state of the art, as well as the burgeoning research topics on this area. The goal of this effort is to let readers know what are the most popular and useful methods in the current, what are the advantages and the disadvantages of these methods, which kind of methods is best suitable for their problems or applications, and what is the future of this area. What is more, hands-on exemplars supplied in this book will be of great interest to Computer Vision engineers and practitioners, as well as those want to begin their research in this area. Overall, this book is suitable for graduates, researchers and engineers in the related areas both as a learning text and as a reference book.

Keywords

Local feature Binary descriptor Computer vision Image matching Invariant descriptor

Authors and affiliations

  • Bin Fan
    • 1
  • Zhenhua Wang
    • 2
  • Fuchao Wu
    • 3
  1. 1.Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.School of EEENanyang Technological UniversitySingaporeSingapore
  3. 3.Chinese Academy of SciencesBeijingChina

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-662-49173-7
  • Copyright Information Springer-Verlag Berlin Heidelberg 2015
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-662-49171-3
  • Online ISBN 978-3-662-49173-7
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • About this book
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