Electro-Optic Technology Application, Volume. 37, Issue 5, 62(2022)

Research on Local Feature Extraction Algorithm for Polarized Images Based on Deep Learning (Invited)

LI Yingchao1,2, YANG Shuai1,2, FU Qiang1,2, SHI Haodong1,2, and ZOU Zhihui1,2
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  • 1[in Chinese]
  • 2[in Chinese]
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    References(5)

    [13] [13] MA J, JIANG X, FAN A, et al. Image matching from hand-crafted to deep features a survey[J]. International Journal of Computer Vision, 2021, 129(1): 23-79.

    [14] [14] SARLIN P E, DETONE D, MALISIEWICZ T, et al. Super glue learning feature matching with graph neural networks [C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 4938-4947.

    [15] [15] ZHANG Junchao, SHAO Jianbo, CHEN Jianlai, et al. PFNet: an unsupervised deep network for polarization image fusion[J]. Optics Letters, 2020, 45(6): 1507-1510.

    [16] [16] LAVIGNE D A, BRETON M. A new fusion algorithm for shadow penetration using visible and midwave infrared polarimetric images[EB/OL]. [2021-09-11]. https://www.researchgate.net/publication/224218630 A new fusion algorithm for shadow penetration using visible and midwave infrared polarimetric images.

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    LI Yingchao, YANG Shuai, FU Qiang, SHI Haodong, ZOU Zhihui. Research on Local Feature Extraction Algorithm for Polarized Images Based on Deep Learning (Invited)[J]. Electro-Optic Technology Application, 2022, 37(5): 62

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    Paper Information

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    Received: Sep. 9, 2022

    Accepted: --

    Published Online: Dec. 7, 2022

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    DOI:

    CSTR:32186.14.

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