Laser & Optoelectronics Progress, Volume. 59, Issue 4, 0410006(2022)

Heterogeneous Noise Iris Segmentation Based on Attention Mechanism and Dense Multiscale Features

Xuanang You1、*, Peng Zhao1、**, Xiaodong Mu1, Kun Bai1, and Sai Lian2
Author Affiliations
  • 1College of Operational Support, Rocket Force University of Engineering, Xi'an , Shaanxi 710025, China
  • 2College of Microelectronics, Xi'an Jiaotong University, Xi'an , Shaanxi 710049, China
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    References(24)

    [15] Zhang W X, Zhu Z C, Zhang Y H et al. Cell image segmentation method based on residual block and attention mechanism[J]. Acta Optica Sinica, 40, 1710001(2020).

    [17] Li D X, Zhang Z. Improved U-Net segmentation algorithm for the retinal blood vessel images[J]. Acta Optica Sinica, 40, 1010001(2020).

    [22] Wang C Y, Sun Z N. A benchmark for iris segmentation[J]. Journal of Computer Research and Development, 57, 395-412(2020).

    [23] Wang Y, Liu L B. Bilinear residual attention networks for fine-grained image classification[J]. Laser & Optoelectronics Progress, 57, 121011(2020).

    [24] Zhou R Y, Shen W Z. PI-Unet: a neural network model for heterogeneous iris segmentation[J]. Computer Engineering and Application, 57, 223-229(2021).

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    Xuanang You, Peng Zhao, Xiaodong Mu, Kun Bai, Sai Lian. Heterogeneous Noise Iris Segmentation Based on Attention Mechanism and Dense Multiscale Features[J]. Laser & Optoelectronics Progress, 2022, 59(4): 0410006

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    Paper Information

    Category: Image Processing

    Received: Jan. 27, 2021

    Accepted: Mar. 25, 2021

    Published Online: Jan. 25, 2022

    The Author Email: Xuanang You (youxuanang@163.com), Peng Zhao (zpxhh@163.com)

    DOI:10.3788/LOP202259.0410006

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