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Cited by 47Advanced Image and Video Retrieval TechniquesImage Retrieval and Classification TechniquesImage and Object Detection Techniques

A Review of Keypoints’ Detection and Feature Description in Image Registration

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Liu, Cuiyin, Xu, Jishang, Wang, Feng·2021·Scientific Programming
DOI 10.1155/2021/8509164
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Abstract

For image registration, feature detection and description are critical steps that identify the keypoints and describe them for the subsequent matching to estimate the geometric transformation parameters between two images. Recently, there has been a large increase in the research methods of detection operators and description operators, from traditional methods to deep learning methods. To solve the problem, that is, which operator is suitable for specific application problems under different imaging conditions, the paper systematically reviewed commonly used descriptors and detectors from artificial methods to deep learning methods, and the corresponding principle, analysis, and comparative experiments are given as well. We introduce the handcrafted detectors including FAST, BRISK, ORB, SURF, SIFT, and KAZE and the handcrafted descriptors including BRISK, FREAK, BRIEF, SURF, ORB, SIFT, KAZE. At the same time, we review detectors based on deep learning technology including DetNet, TILDE, LIFT, multiscale detector, SuperPoint, and descriptors based on deep learning including pretrained descriptor, Siamese descriptor, LIFT, triplet network, and SuperPoint. Two group of comparison experiments are compared comprehensively and objectively on representative datasets. Finally, we concluded with insightful discussions and conclusions of descriptor and detector selection for specific application problem and hope this survey can be a reference for researchers and engineers in image registration and related fields.

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Cited by

Sorted by citations

Authors
Liu, C., Xu, J., & Wang, F.
Affiliations
  • Kunming University of Science and Technology (CN)
  • Guangzhou University (CN)
Corresponding author
  • Liu, Cuiyin
  • Wang, Feng
ORCID
Venue
Scientific Programming
Publisher
Wiley
Published
2021-12-01
Volume
2021
Pages
1-25
ISSN
  • 1058-9244 (ISSN-L)
  • 1875-919X
DOI
10.1155/2021/8509164
OpenAlex
W3215884545
Cited by
47
References
10
Citations by year
2026: 5 · 2025: 9 · 2024: 14 · 2023: 14 · 2022: 5
FWCI
2.97
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Programming (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Basic Research Program of China (2018YFA0404603, 11773012, 11903009, 11961141001, U1831204, U1931141, 2018IA054)
  • National Natural Science Foundation of China (2018YFA0404603, 11773012, 11903009, 11961141001, U1831204, U1931141, 2018IA054)
  • Joint Research Fund in Astronomy (2018YFA0404603, 11773012, 11903009, 11961141001, U1831204, U1931141, 2018IA054)
  • Chinese Academy of Sciences (2018YFA0404603, 11773012, 11903009, 11961141001, U1831204, U1931141, 2018IA054)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Object Detection Techniques
Keywords
image registration, keypoint detection, feature description, spark plasma sintering, BRISK, ORB, SURF, SIFT, KAZE, SuperPoint, lift force, triplet network, descriptor comparison

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