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Image matching algorithm of defects on navel orange surface based on compressed sensing

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Abstract

The surface defect of navel orange is one of the significant factors that affects its price. At present, most of surface defect detection algorithms for navel orange have disadvantages of slow speed, massive calculation and low efficiency, making it difficult to meet the needs of automated detection. This article proposes an improved image matching method on navel orange surface defect detection which combines wavelet transform (WT) and speeded up robust features (SURF) based on compressed sensing (CS). Firstly, do some pre-treatment on the navel orange images such as de-noising, compression and so on, then decompose the image by wavelet transform based on compressed sensing technology, and obtain the low frequency sub-image and extract SURF features of the image, next compare the extracted SURF feature with feature library, search for the maximum matching value of the similarity measurement values, and output the recognition results. The algorithm ensures better recognition accuracy and efficiency, and achieves rapid identification of navel orange defects.

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Acknowledgements

This work is supported by the National Natural Science Foundation, under Grant Nos. 61762037, 61640217, 61462028, Science and Technology Support Program of Jiangxi Province, under Grant No. 20151BBE50055, and Science and Technology Project supported by education department of Jiangxi Province, under Grant No. GJJ150541, and Nanchang City Knowledge Innovation Team, under Grant No. 2016T75.

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Authors and Affiliations

  1. School of Information Engineering, East China Jiaotong University, Nanchang, People’s Republic of China

    Xin Xie, Songlin Ge, Nan Jiang, Tijian Cai & Bo Li

  2. School of Information Science Technology, East China Normal University, Shanghai, People’s Republic of China

    Mingye Xie

  3. School of Civil Engineering, East China Jiaotong University, Nanchang, People’s Republic of China

    Fengping Hu

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  1. Xin Xie
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  2. Songlin Ge
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  3. Mingye Xie
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  4. Fengping Hu
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  5. Nan Jiang
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  7. Bo Li
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Correspondence to Xin Xie.

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Xie, X., Ge, S., Xie, M. et al. Image matching algorithm of defects on navel orange surface based on compressed sensing. J Ambient Intell Human Comput 15, 1229–1237 (2024). https://doi.org/10.1007/s12652-018-0833-0

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  • DOI: https://doi.org/10.1007/s12652-018-0833-0

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