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Research on Eliminating Mismatched Feature Points: A Review

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Proceedings of the 13th International Conference on Computer Engineering and Networks (CENet 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1127))

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Abstract

The mismatch point elimination algorithm is a commonly used method in the field of computer vision and image processing to deal with the presence of mismatches or outliers in matched point pairs. These mismatch points may be caused by noise, occlusion, illumination changes or image distortion. In this paper, we first explain why there is a need to eliminate the mismatch points and the current state of research, and then introduce various types of feature points and describe the extraction methods of various feature points. Next, we review several methods of false match feature point elimination, such as geometric consistency verification-based methods, graph optimization-based methods, motion statistics-based methods, and learning-based methods, analyze their advantages and disadvantages as well as make comparisons, and give an outlook on future research directions. In the conclusion, we summarize the full paper and discuss the application trends of the mismatching feature point elimination algorithms. The purpose of this paper is to provide readers with a clearer and deeper understanding of false match feature point elimination algorithms, and hopefully give some reference significance to later researchers.

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Funding

The authors are highly thankful to the National Natural Science Foundation of China(NO.62063006), the Natural Science Foundation of Guangxi Province (NO.2023GXNSFAA026025), to the Innovation Fund of Chinese Universities Industry-University-Research (ID:2021RYC06005), to the Research Project for Young and Middle-aged Teachers in Guangxi Universities (ID: 2020KY15013), and to the Special research project of Hechi University (ID:2021GCC028). This research was financially supported by the project of outstanding thousand young teachers’ training in higher education institutions of Guangxi, Guangxi Colleges and Universities Key Laboratory of AI and Information Processing (Hechi University), Education Department of Guangxi Zhuang Autonomous Region.

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Authors and Affiliations

  1. College of Automation, Guangxi University of Science and Technology, Liuzhou, 545000, China

    Dunhua Chen, Jiansheng Peng & Qing Yang

  2. Department of Artificial Intelligence and Manufacturing, Hechi University, Hechi, 547000, China

    Jiansheng Peng

Authors
  1. Dunhua Chen
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  2. Jiansheng Peng
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  3. Qing Yang
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Corresponding author

Correspondence to Jiansheng Peng .

Editor information

Editors and Affiliations

  1. Wuxi University, Wuxi, Jiangsu, China

    Yonghong Zhang

  2. College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, Shandong, China

    Lianyong Qi

  3. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

    Qi Liu

  4. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Guangqiang Yin

  5. School of Computing, Edinburgh Napier University, Edinburgh, UK

    Xiaodong Liu

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Chen, D., Peng, J., Yang, Q. (2024). Research on Eliminating Mismatched Feature Points: A Review. In: Zhang, Y., Qi, L., Liu, Q., Yin, G., Liu, X. (eds) Proceedings of the 13th International Conference on Computer Engineering and Networks. CENet 2023. Lecture Notes in Electrical Engineering, vol 1127. Springer, Singapore. https://doi.org/10.1007/978-981-99-9247-8_3

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  • DOI: https://doi.org/10.1007/978-981-99-9247-8_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9246-1

  • Online ISBN: 978-981-99-9247-8

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