Research on image matching methods in computer vision

Authors

  • Zizhou Shi

DOI:

https://doi.org/10.54097/hset.v23i.3267

Keywords:

Computer vision, image matching, attention methods, development trend.

Abstract

Today, computer vision has shown a variety of roles in our lives, making people’s life more convenient. Also, a variety of artificial intelligence models and algorithms have emerged for computer vision. Image matching is an important technique in the field of computer vision to find the similarities between two images or multiple images with the help of matching algorithms to achieve scientific and accurate processing of images. Therefore, summarizing the effective approximate solution to this problem as well as the future research is the main part for current research. The paper firstly describes the basic elements of the image matching technique. Then, some representative traditional image matching algorithms proposed in the field of computer vision research in recent years are summarized and reviewed. Finally, the future research directions and research ideas of image matching are discussed, providing reliable guidance and reference for subsequent research.

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References

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J Liu, X He, W Chen, “A9 SIFT feature image matching algorithm based on Wavelet Transform.” Computer simulation, 28 (1), 257-260 (2011).

X Huang, W Bao, “Image feature point matching algorithm based on improved Hopfield neural network”, 31 (9), 1961 - 1964 (2010).

Y Zhao, “Research on image semantic representation and metric learning,” Henan: The PLA Information Engineering University, 1-123 (2015).

David G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Computer Science Department University of British Columbia, (2004).

J Zhang, J Li, S Zhang, “A hybrid image matching algorithm based on line feature and SIFT point feature”.

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Published

03-12-2022

How to Cite

Shi, Z. (2022). Research on image matching methods in computer vision. Highlights in Science, Engineering and Technology, 23, 198-201. https://doi.org/10.54097/hset.v23i.3267