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Local Feature Descriptor for Image Matching: A Survey
- Source :
- IEEE Access, Vol 7, Pp 6424-6434 (2019)
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
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.
- Subjects :
- point pattern matching
General Computer Science
Computer science
Feature extraction
0211 other engineering and technologies
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Image registration
Context (language use)
02 engineering and technology
image matching
Facial recognition system
Local feature descriptor
Histogram
0202 electrical engineering, electronic engineering, information engineering
Feature descriptor
General Materials Science
Image retrieval
021101 geological & geomatics engineering
Image fusion
business.industry
pattern recognition
General Engineering
Pattern recognition
Feature (computer vision)
Video tracking
020201 artificial intelligence & image processing
Artificial intelligence
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
Change detection
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....e1cdec4f74503bed5fce686cfb1544f9