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Information recombination network for contour detection

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Abstract

Convolutional Neural Network (CNN) has been widely used in various tasks of computer vision. For contour detection, the encoding network and decoding network structure are mainly used. The encoding network mainly uses existing networks such as VGG, and the decoding network that effectively integrates the features extracted from the encoding network has become a research hotspot in this field. To effectively integrate features, this paper proposes a Center-Far periphery and Center-Near periphery information recombination network (CFCN) based on the VGG encoding network. The decoding network is divided into three parts, Center-Far Periphery Layer (CF), Center-Near Periphery Layer (CN) and Central Base Layer (CB), the CF part aims to highlight the contour features of the main body, the CN part aims to highlight the contour details, and the CB provides benchmark information for the decoding network. Ultimately, the CF, CN, and CB are combined. On the basis of ensuring the complete contour of the main body, some detailed features are further retained. We used the F-measure for performance evaluation, and have achieved good performance on the BSDS and NYUD datasets. ODS = 0.818 on the BSDS dataset; the experimental result on the NYUD dataset is ODS = 0.764. The results show that it surpassed the human-level performance in each dataset.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 61866002), Guangxi Natural Science Foundation (Grant No.2020GXNSFDA297006, Grant No. 2018GXNSFAA138122 and Grant No. 2015GXNSFAA139293).

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Authors and Affiliations

  1. College of Electric and Information Engineering, Guangxi University of Science and Technology, Liuzhou, 545006, People’s Republic of China

    Zeqi Wen, Chuan Lin, Fuzhang Li & Linhao Cui

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  1. Zeqi Wen
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  2. Chuan Lin
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  3. Fuzhang Li
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  4. Linhao Cui
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Correspondence to Chuan Lin.

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Wen, Z., Lin, C., Li, F. et al. Information recombination network for contour detection. Multimed Tools Appl 82, 3895–3910 (2023). https://doi.org/10.1007/s11042-022-13430-w

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