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Authors: Rhythm Vohra ; Amanda Dash and Alexandra Branzan Albu

Affiliation: University of Victoria, Canada

Keyword(s): Segmentation, Visual Attention, Image Vectorization.

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 segme ntation 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. (More)

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Paper citation in several formats:
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; ISSN 2184-4321, SciTePress, pages 215-226. DOI: 10.5220/0012465900003660

@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},
issn={2184-4321},
}

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
IS - 2184-4321
AU - Vohra, R.
AU - Dash, A.
AU - Branzan Albu, A.
PY - 2024
SP - 215
EP - 226
DO - 10.5220/0012465900003660
PB - SciTePress

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