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结合模糊支持向量机的曲线类比在书法风格仿真中的研究

陈 颉, 朱福喜(武汉大学计算机学院,武汉 430072)

摘 要
通过学习有限的输入字帖,实时全自动地独立创作出风格迥异的书法作品是计算机艺术仿真研究领域中的一个重要方向。提出了一种结合模糊支持向量机(FSVM)的曲线类比学习算法,能够根据用户设定的参数生成各种风格的书法作品。首先将字帖图像转换为层次化的笔画结构模型,通过FSVM检索骨架结构相似点的序列进而对其进行曲线类比与演化,最后经过处理选择得到新风格的字体。仿真实验结果表明,基于FSVM的曲线类比算法能根据输入的不同风格书法图像和用户的参数设定生成大量新颖的书法风格。
关键词
Novel Chinese Calligraphy Style Generation Based on Curve Analogy with FSVM

CHEN Jie,, ZHU Fuxi(College of Computer Science and Technology, Wuhan University, Wuhan 430072)

Abstract
By learning the various character image samples, the automatic and synchronistic generation of new Chinese calligraphy styles is a key problem in the computer artistic simulating. A curve analogy method based on FSVM is proposed which can generate new calligraphy styles by users definition. Firstly, the input character image samples are transferred into a hierarchical stroke structural model. Secondly, the matching points in the various skeleton structure are retrieved with FSVM,which provides the control parameter of the curve analogy process. Lastly, the new skeletons are reconstructed into the calligraphy characters. The efficiency of our approach is manifested by the preliminary experiment with the generation of quantity of novel calligraphy styles,which can be manipulated by the user defined setting and input characters styles.
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