JOURNAL ARTICLE

Local Feature Descriptor for Image Matching: A Survey

Chengcai LengHai ZhangBo LiGuorong CaiZhao PeiLi He

Year: 2018 Journal:   IEEE Access Vol: 7 Pages: 6424-6434   Publisher: Institute of Electrical and Electronics Engineers

Abstract

Image registration is an important technique in many computer vision applications such as image fusion, image retrieval, object tracking, face recognition, change detection and so on. Local feature descriptors, i.e., how to detect features and how to describe them, play a fundamental and important role in image registration process, which directly influence the accuracy and robustness of image registration. This paper mainly focuses on the variety of local feature descriptors including some theoretical research, mathematical models, and methods or algorithms along with their applications in the context of image registration. The existing local feature descriptors are roughly classified into six categories to demonstrate and analyze comprehensively their own advantages. The current and future challenges of local feature descriptors are discussed. The major goal of the paper is to present a unique survey of the state-of-the-art image matching methods based on feature descriptor, from which future research may benefit.

Keywords:
Artificial intelligence Computer science Robustness (evolution) Feature (linguistics) Feature detection (computer vision) Pattern recognition (psychology) Computer vision Feature extraction Image registration Feature matching Matching (statistics) Image (mathematics) Context (archaeology) Image processing Mathematics Geography

Metrics

116
Cited By
8.66
FWCI (Field Weighted Citation Impact)
167
Refs
0.97
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Robotics and Sensor-Based Localization
Physical Sciences →  Engineering →  Aerospace Engineering
Advanced Neural Network Applications
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
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