Low-quality vector sketch optimization with latent structure

Yiwei Jia; Xueming Li; Xianlin Zhang

doi:10.1117/12.3035069

11 July 2024 Low-quality vector sketch optimization with latent structure

Yiwei Jia, Xueming Li, Xianlin Zhang

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 1321024 (2024) https://doi.org/10.1117/12.3035069
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

It is not a valid asseration that everyone can paint as the difference of people's drawing ability. Thus, there is a common problem of 'bad painting' . Therefore, how to automatically optimize a large number of real and low-quality hand-drawn sketches produced in actual situations such as people 'can't draw' and 'can't draw well', so as to help most people reduce the pressure of painting, and achieve their own painting expectations when doodling, and more importantly, enhance the discriminability, interpretability and applicability of such hand-drawn sketches in vector style, is a critical problem to be solved in the field of freehand sketch processing. In this paper, we intend to address the issue of optimizing low-quality vector sketches based on diffusion model. Concretely, we first propose a criterion of information entropy (IE) to define sketch quality and use it as a preprocessing method to preliminarily screen sketch data, a latent structure which based on diffusion model is then utilized to optimize the low-quality sketch. Extensive experiments demonstrate the effectiveness of the proposed method, furthermore, the optimized sketch can be used for downstream tasks such as image recognition and understanding.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yiwei Jia, Xueming Li, and Xianlin Zhang "Low-quality vector sketch optimization with latent structure", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 1321024 (11 July 2024); https://doi.org/10.1117/12.3035069

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available