Zhu, Haokun, Chong, Juang Ian, Hu, Teng, Yi, Ran, Lai, Yukun  ORCID: https://orcid.org/0000-0002-2094-5680 and Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884
2024.
SAMVG: A multi-stage image vectorization model with the segment-anything model.
Presented at: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
Seoul, Korea,
14-19 April 2024.
Proceedings of the 2024 IEEE International Conference on Acoustics, Speech and Signal Processing.
IEEE,
pp. 4350-4354.
10.1109/ICASSP48485.2024.10447396
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Abstract
Vector graphics are widely used in graphical designs and have received more and more attention. However, unlike raster images which can be easily obtained, acquiring high-quality vector graphics, typically through automatically converting from raster images, remains a significant challenge, especially for more complex images such as photos or artworks. In this paper, we propose SAMVG, a multi-stage model to vectorize raster images into SVG (Scalable Vector Graphics). Firstly, SAMVG uses general image segmentation provided by the Segment-Anything Model and uses a novel filtering method to identify the best dense segmentation map for the entire image. Secondly, SAMVG then identifies missing components and adds more detailed components to the SVG. Through a series of extensive experiments, we demonstrate that SAMVG can produce high quality SVGs in any domain while requiring less computation time and complexity compared to previous state-of-the-art methods.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Date Type: | Published Online |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | IEEE |
| ISBN: | 979-8-3503-4486-8 |
| ISSN: | 1520-6149 |
| Date of First Compliant Deposit: | 21 March 2024 |
| Date of Acceptance: | 13 December 2023 |
| Last Modified: | 13 May 2025 14:22 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/167441 |
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