Research of image detection and matching algorithms

Research Article
Open access

Research of image detection and matching algorithms

Yufei Bai 1*
  • 1 Dublin International College, Beijing University of Industry, Beijing, China    
  • *corresponding author yufei.bai@ucdconnect.ie
ACE Vol.5
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-57-7
ISBN (Online): 978-1-915371-58-4

Abstract

Image matching, a fundamental computer vision method, serves as a crucial pillar for more complex vision applications. The general adoption of feature-based image registration technologies has been accelerated by advances in computing hardware and vision theory. As the current research in this field is not very sufficient, this paper gives an overview of the relevant aspects. At the beginning, this article first introduces the research background, the research achievements and the application in different fields of image feature detection and matching. The main body discusses the most current advancements in this subject, including feature points, local features, global features, matching, and optimization, after examining the classical detection algorithms from recent decades and referencing the most recent machine learning algorithm headed by depth learning, and shows the advantages and disadvantages of the algorithms. Finally, the paper summarizes and prospects the full text.

Keywords:

feature detection algorithms, local descriptor, deep learning, machine learning

Bai,Y. (2023). Research of image detection and matching algorithms. Applied and Computational Engineering,5,519-526.
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References

[1]. Zitova, B., Flusser, J. 2003. Image registration methods: a survey. Imag. Vis. Comput., 21(11), 977–1000.

[2]. Marr, D., & Hildreth, E. 1980. Theory of edge detection. Royal Soc. London Biol. Sci., 207(1167), 187-217.

[3]. Lowe, D. G. 2004. Distinctive image features from scale-invariant keypoints. Inter. J. Com. Vis., 60(2), 91-110.

[4]. Tola, E., Lepetit, V., & Fua, P. 2008. A fast local descriptor for dense matching. Conf. Com. Vis. Pat. Rec. 1-8.

[5]. Calonder, M., Lepetit, V., Strecha, C., & Brief, F. P. 2019 Binary robust independent elementary features. Euro. Conf. Comput. Vis. 778-792.

[6]. Ke, Y., & Sukthankar, R. 2004. PCA-SIFT: A more distinctive representation for local image descriptors. Conf. Com. Vis. Pat. Rec. 2, 506-513.

[7]. Barroso-Laguna, A., Riba, E.，Ponsa, D.，et al. 2019 Key．Net: keypoint detection by handcrafted and learned CNN filters．arXiv preprint，arXiv: 1904. 00889

[8]. Tian, Y R., Fan, B., Wu, F C. 2017 L2-Net: deep learning of discriminative patch descriptor in euclidean space. Conf. Com. Vis. Pat. Rec. 661-669.

[9]. Sivic, J., Zisserman, A. 2003 Video Google: a text retrieval approach to object matching in video. Inter. Conf. Comput. Vis. 1470.

[10]. Jégou, H., Douze, M., Schmid, C., et al．2010 Aggregating local descriptors into a compact image representation. Inter. Conf. Comput. Vis. 3304-3311.

[11]. Babenko, A., Slesarev, A., Chigorin, A., et al．2014. Neural codes for image retrieval. Euro. Conf. Comput. Vis. 584-599.

Cite this article

Bai,Y. (2023). Research of image detection and matching algorithms. Applied and Computational Engineering,5,519-526.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.5
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Zitova, B., Flusser, J. 2003. Image registration methods: a survey. Imag. Vis. Comput., 21(11), 977–1000.

[2]. Marr, D., & Hildreth, E. 1980. Theory of edge detection. Royal Soc. London Biol. Sci., 207(1167), 187-217.

[3]. Lowe, D. G. 2004. Distinctive image features from scale-invariant keypoints. Inter. J. Com. Vis., 60(2), 91-110.

[4]. Tola, E., Lepetit, V., & Fua, P. 2008. A fast local descriptor for dense matching. Conf. Com. Vis. Pat. Rec. 1-8.

[5]. Calonder, M., Lepetit, V., Strecha, C., & Brief, F. P. 2019 Binary robust independent elementary features. Euro. Conf. Comput. Vis. 778-792.

[6]. Ke, Y., & Sukthankar, R. 2004. PCA-SIFT: A more distinctive representation for local image descriptors. Conf. Com. Vis. Pat. Rec. 2, 506-513.

[7]. Barroso-Laguna, A., Riba, E.，Ponsa, D.，et al. 2019 Key．Net: keypoint detection by handcrafted and learned CNN filters．arXiv preprint，arXiv: 1904. 00889

[8]. Tian, Y R., Fan, B., Wu, F C. 2017 L2-Net: deep learning of discriminative patch descriptor in euclidean space. Conf. Com. Vis. Pat. Rec. 661-669.

[9]. Sivic, J., Zisserman, A. 2003 Video Google: a text retrieval approach to object matching in video. Inter. Conf. Comput. Vis. 1470.

[10]. Jégou, H., Douze, M., Schmid, C., et al．2010 Aggregating local descriptors into a compact image representation. Inter. Conf. Comput. Vis. 3304-3311.

[11]. Babenko, A., Slesarev, A., Chigorin, A., et al．2014. Neural codes for image retrieval. Euro. Conf. Comput. Vis. 584-599.

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