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Image Matching: Foundations, State of the Art, and Future Directions
Ming Yang1, Rui Wu1, Yunxuan Yang2
1Department of Information Technology, Kennesaw State University, Kennesaw, GA 30060, USA.
Journal of Imaging
|October 28, 2025
Summary
This review covers the evolution of image matching techniques, from traditional methods to deep learning. It highlights current trends, challenges, and future research directions in computer vision.
Area of Science:
- Computer Vision
- Artificial Intelligence
Background:
- Image matching is crucial for applications like object recognition, 3D reconstruction, and surveillance.
- Techniques have evolved from handcrafted features to deep learning and attention mechanisms over 30 years.
Purpose of the Study:
- To provide a comprehensive review of image-matching techniques.
- To offer insights into the historical development and current state of the field.
- To identify future research directions and persistent challenges.
Main Methods:
- Historical analysis of feature-based methods.
- Examination of neural network-based approaches.
- Discussion of recent contributions and benchmarks.
Main Results:
- Detailed overview of image-matching algorithm evolution.
- Identification of key algorithms, benchmarks, and applications.
- Highlighting of challenges like viewpoint and illumination invariance.
Conclusions:
- The field is rapidly advancing with deep learning.
- Future research should focus on H-matrix optimization, LoFTR speedup, and performance improvements.
- Addressing robustness and scalability remains critical for real-world deployment.
