Neural recognition of dashed curves with Gestalt law of continuity
Author(s)
Author(s)
Liu, H.
Wong, T.-T.
Date Issued
2022
Publisher
IEEE
Related Publication(s)
Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVRP)
Start page
1373
End page
1382
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.
SFU Affiliated Publication
Yes
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