Guan, X, Luo, L, Li, H et al. (4 more authors) (2021) Automatic embroidery texture synthesis for garment design and online display. Visual Computer, 37 (9-11). pp. 2553-2565. ISSN 0178-2789
Abstract
We introduce an automatic texture synthesis-based framework to convert an arbitrary input image into embroidery style art for garment design and online display. Given an input image and some reference textures, we first extract key embroidery regions from the input image using image segmentation. Each segmented region is single-colored and labeled with a stitch style automatically. We then fill these regions with embroidery reference textures via a stitch-style-based texture synthesis method. For each region, our approach maintains color similarity before and after synthesis, along with stitch style consistency. Compared to existing approaches, our method is able to generate digital embroidery patterns with faithful details automatically. Moreover, it can accept diverse input images effectively, enabling a fast preview of the embroidery patterns synthesized on digital garments interactively, and therefore accelerating the workflow from design to production. We validate our method through extensive experimentation and comparison.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. This is an author produced version of an article published in Visual Computer. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Embroidery; Non-photorealistic rendering; Image-based artistic rendering |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 20 Aug 2021 12:34 |
Last Modified: | 06 Jul 2022 00:13 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/s00371-021-02216-0 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177275 |