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Fast Accurate and Automatic Brushstroke Extraction.

Fu, Y., Yu, H., Yeh, C-K., Lee, T.Y. and Zhang, J. J., 2021. Fast Accurate and Automatic Brushstroke Extraction. ACM Transactions on Multimedia Computing, Communications, and Applications, 17 (2), 44.

Full text available as:

TOMM1702-44_LR.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1145/3429742


Brushstrokes are viewed as the artist’s “handwriting” in a painting. In many applications such as style learning and transfer, mimicking painting, and painting authentication, it is highly desired to quantitatively and accurately identify brushstroke characteristics from old masters’ pieces using computer programs. However, due to the nature of hundreds or thousands of intermingling brushstrokes in the painting, it still remains challenging. This article proposes an efficient algorithm for brush Stroke extraction based on a Deep neural network, i.e., DStroke. Compared to the state-of-the-art research, the main merit of the proposed DStroke is to automatically and rapidly extract brushstrokes from a painting without manual annotation, while accurately approximating the real brushstrokes with high reliability. Herein, recovering the faithful soft transitions between brushstrokes is often ignored by the other methods. In fact, the details of brushstrokes in a master piece of painting (e.g., shapes, colors, texture, overlaps) are highly desired by artists since they hold promise to enhance and extend the artists’ powers, just like microscopes extend biologists’ powers. To demonstrate the high efficiency of the proposed DStroke, we perform it on a set of real scans of paintings and a set of synthetic paintings, respectively. Experiments show that the proposed DStroke is noticeably faster and more accurate at identifying and extracting brushstrokes, outperforming the other methods.

Item Type:Article
Uncontrolled Keywords:Computing methodologies; Image manipulation; brushstroke extraction; painting authentication,;hard and soft segmentation,; Pix2Pix network
Group:Faculty of Media & Communication
ID Code:35490
Deposited By: Symplectic RT2
Deposited On:14 May 2021 11:16
Last Modified:14 Mar 2022 14:27


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