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© 2015

Local Image Descriptor: Modern Approaches

Book

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

  1. 1.Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.School of EEENanyang Technological UniversitySingaporeSingapore
  3. 3.Chinese Academy of SciencesBeijingChina

About the authors

Dr. Bin Fan is an Associate Professor in the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences where he got his PhD degree in 2011 and acted as an Assistant Professor from 2011 to 2014. He has been a visiting professor at the Computer Vision Laboratory, Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland, during 2014.5 to 2014.6 and 2015.3 to 2016.3. His research interests include computer vision and pattern recognition. Particularly, he is focusing on designing and matching of local features and their visual applications. He has published over 20 papers in prestigious international journals and conferences such as IEEE TPAMI, IEEE TIP, PR, CVPR, ICCV, ECCV, AAAI. He serves as an Associate Editor of Neurocomputing, Area Chair of WACV’16 and regular reviewer for top-ranking journals as well as on program committee member for major vision conferences. In CVPR’15, he was awarded the Outstanding Reviewer Awards and is currently a member of IEEE. He gave a tutorial about local invariant descriptors in the Vision and Learning Seminar (VALSE) 2014.

Dr. Zhenhua Wang is a research fellow in the Rapid-Rich Object Search (ROSE) Lab, School of EEE, Nanyang Technological University, Singapore since 2014.8. He received his BS degree in software engineering from Sichuan University in 2008, and the PhD degree in pattern analysis and machine intelligence from the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. His research interests are in the fields of Computer Vision related topics, including feature detection, feature description, 3D reconstruction.

Prof. Fuchao Wu is a Professor in the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. Previously, he acted as a Lecturer and then as an Associate Professor in Anqing Teacher’s College from 1984 to 1994. From 1995 to 2000, he acted as a Professor in Anhui University. His research interests are now in computer vision, which includes multi-view geometry, 3D reconstruction, active vision, and image based modeling. He has published two books and over 100 papers in prestigious international journals and conferences such as IJCV, IEEE TPAMI, IEEE TIP, CVIU, PR, CVPR, ICCV, ECCV etc. He has received several honors and awards, including the Second Best Award of Natural Science of Anhui province in 2000.

Bibliographic information

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