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Bio-inspired feature cascade network for edge detection

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Abstract

Edge detection is a key step in various image processing tasks. Edge detection based on deep learning is usually composed of encoding and decoding networks. Encoding networks are usually built based on classifiers (e.g., VGG16) while focusing on the construction of decoding networks. In this paper, an encoding–decoding network is proposed by simulating the visual pathway of the retina-lateral geniculate nucleus (LGN)- the primary visual cortex (V1)-V2-V4- the inferior temporal cortex. Bio-inspired Feature Cascade Network (BFCN) was designed to simulate the transmission modes of feedforward propagation, horizontal connection, and feedback propagation among neurons in the IT, which is conducive to enhancing the characteristic analysis ability of the decoding network. Firstly, to simulate the information processing model of feedforward propagation, a Feedforward Propagation Network is designed to fully fuse the underlying information. Secondly, to simulate the information processing model of the horizontal connection between neurons, the Inter-Layer Information module (ILI) is designed to process the interlayer information of FPNet, which is beneficial to enhancing the feature extraction ability. Finally, to simulate the feedback propagation, the Proximity Combination Network (PCNet) is designed to integrate the feature prediction of each stage and strengthen the generalization ability of the network. Experimental results show that the proposed contour detection model outperforms current similar models.

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Acknowledgements

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, Grant No. 2015GXNSFAA139293), and Innovation Project of Guangxi University of Science and Technology Graduate Education (Grant No. GKYC202005).

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  1. School of Electrical, Electronic and Computer Science, Guangxi University of Science and Technology, Liuzhou, 545006, China

    Shenghui Pan, Ruixing Wang & Chuan Lin

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  1. Shenghui Pan
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Correspondence to Chuan Lin.

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Pan, S., Wang, R. & Lin, C. Bio-inspired feature cascade network for edge detection. Vis Comput 39, 4149–4164 (2023). https://doi.org/10.1007/s00371-022-02581-4

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