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• W2954037163 abstract "Deep learning has been used as a powerful tool for various tasks in computer vision, such as image segmentation, object recognition and data generation. A key part of end-to-end training is designing the appropriate encoder to extract specific features from the input data. However, few encoders maintain the topological properties of data, such as connection structures and global contours. In this paper, we introduce a Voronoi Diagram encoder based on convex set distance (CSVD) and apply it in edge encoding. The boundaries of Voronoi cells is related to detected edges of structures and contours. The CSVD model improves contour extraction in CNN and structure generation in GAN. We also show the experimental results and demonstrate that the proposed model has great potentiality in different visual problems where topology information should be involved." @default.
• W2954037163 created "2019-07-12" @default.
• W2954037163 creator A5018778822 @default.
• W2954037163 date "2019-06-25" @default.
• W2954037163 modified "2023-10-18" @default.
• W2954037163 title "Topology Maintained Structure Encoding" @default.
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• W2954037163 doi "https://doi.org/10.48550/arxiv.1906.10823" @default.
• W2954037163 hasPublicationYear "2019" @default.
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