Fast Accurate and Automatic Brushstroke Extraction

Yunfei Fu, Hongchuan Yu, Chih Kuo Yeh, Tong Yee Lee, Jian J. Zhang

研究成果: Article同行評審

摘要

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.

原文English
文章編號44
期刊ACM Transactions on Multimedia Computing, Communications and Applications
17
發行號2
DOIs
出版狀態Published - 2021 六月

All Science Journal Classification (ASJC) codes

  • 硬體和架構
  • 電腦網路與通信

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