Multimedia Tools and Applications

, Volume 77, Issue 3, pp 2973–2990 | Cite as

Artistic features extraction from chinese calligraphy works via regional guided filter with reference image

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Abstract

Chinese calligraphy is a unique visual art, and and is one of the material basis of China’s traditional cultural heritage. However, time had caused the old calligraphy works to weathering and damages, so it is necessary to utilize advanced technologies to protect those works. One of those technologies is digital imaging, and the obtained images by digital imaging can preserve the visual information of calligraphy works better, furthermore, they can be used in further researches. While the basic works for those researches are to extract the artistic features which include two elements, i.e., form and spirit. However, most of the existing methods only extract the form and ignore the characters’ spirit, especially they are insensitive to the slight variation in complex ink strokes. To solve these problems, this paper proposes an extraction method based on regional guided flter (RGF) with reference images, which is generated by KNN matting and used as the input image for RGF. Since RGF is sensitive to the slight variation of ink, so the detailed information of the inside of strokes can be detected better. Besides, unlike the past works, which filter the whole strokes, RGF filters the inside of strokes and edges in different windows respectively, which results in that the edges are preserved accurately. Results from a deployment of several famous Chinese calligraphy works demonstrate that our method can extract more accurate and complete form and spirit with lower error rate.

Keywords

Artistic features extraction Chinese calligraphy Reference images Guided filter Regional guided filter Spirit 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant No. 61202198), and Natural Science Basic Research Plan in Shaanxi Province of China (Grant No. 2016 JQ6068). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Lei Wang
    • 1
  • Xiaoqing Gong
    • 1
  • Yongqin Zhang
    • 1
  • Pengfei Xu
    • 1
  • Xiaojiang Chen
    • 1
  • Dingyi Fang
    • 1
  • Xia Zheng
    • 2
  • Jun Guo
    • 1
  1. 1.School of Information Science and TechnologyNorthwest UniversityXi’anChina
  2. 2.Department of Culture Heritage and MuseologyZhejiang UniversityHangzhouChina

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