Abstract

Dashed curve is a frequently used curve form and is widely used in various drawing and illustration applications. While humans can intuitively recognize dashed curves from disjoint curve segments based on the law of continuity in Gestalt psychology, it is extremely difficult for computers to model the Gestalt law of continuity and recognize the dashed curves since high-level semantic understanding is needed for this task. The various appearances and styles of the dashed curves posed on a potentially noisy background further complicate the task. In this paper, we propose an innovative Transformer-based framework to recognize dashed curves based on both high-level features and low-level clues. The framework manages to learn the computational analogy of the Gestalt Law in various domains to locate and extract instances of dashed curves in both raster and vector representations. Qualitative and quantitative evaluations demonstrate the efficiency and robustness of our framework over all existing solutions.

Video

BibTex:

@inproceedings{liu2022-dashed-curve,
    author = {Hanyuan Liu and Chengze Li and Xueting Liu and Tien-Tsin Wong},
    title = {Neural Recognition of Dashed Curves with Gestalt Law of Continuity},
    booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 2022 (CVPR 2022)},
    year      = {2022},
    month     = {June},
    pages     = {1373-1382}
}