Single-Class Instance Segmentation for Vectorization of Line Drawings

Rhythm Vohra, Amanda Dash, Alexandra Branzan Albu

2024

Abstract

Images can be represented and stored either in raster or in vector formats. Raster images are most ubiquitous and are defined as matrices of pixel intensities/colours, while vector images consist of a finite set of geometric primitives, such as lines, curves, and polygons. Since geometric shapes are expressed via mathematical equations and defined by a limited number of control points, they can be manipulated in a much easier way than by directly working with pixels; hence, the vector format is much preferred to raster for image editing and understanding purposes. The conversion of a raster image into its vector correspondent is a non-trivial process, called image vectorization. This paper presents a vectorization method for line drawings, which is much faster and more accurate than the state-of-the-art. We propose a novel segmentation method that processes the input raster image by labeling each pixel as belonging to a particular stroke instance. Our contributions consist of a segmentation model (called Multi-Focus Attention UNet), as well as a loss function that handles well infrequent labels and yields outputs which capture accurately the human drawing style.

Paper Citation

in Harvard Style

Vohra R., Dash A. and Branzan Albu A. (2024). Single-Class Instance Segmentation for Vectorization of Line Drawings. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 215-226. DOI: 10.5220/0012465900003660

in Bibtex Style

@conference{visapp24,
author={Rhythm Vohra and Amanda Dash and Alexandra Branzan Albu},
title={Single-Class Instance Segmentation for Vectorization of Line Drawings},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={215-226},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012465900003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Single-Class Instance Segmentation for Vectorization of Line Drawings
SN - 978-989-758-679-8
AU - Vohra R.
AU - Dash A.
AU - Branzan Albu A.
PY - 2024
SP - 215
EP - 226
DO - 10.5220/0012465900003660
PB - SciTePress