  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.
