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Multimedia Tools and Applications

, Volume 78, Issue 23, pp 34065–34094 | Cite as

StitchGeneration: Modeling and creation of random-needle embroidery based on Markov chain model

  • Chen Ma
  • Zhengxing SunEmail author
Article
  • 41 Downloads

Abstract

Random-needle Embroidery is a new form of Chinese embroidery. The multi-layering of stitch is the notable characteristic of this embroidery art because the embroidery is generated by stacking single-layer stitch multiple times. To generate this kind of multi-layer stitch, the main challenge lies on the single-layer stitch modeling so that the multi-layer stitch can be generated based on stacking single-layer stitches. However, due to the complex structure of threads, single-layer stitch modeling is not easy, especially the long threads are intersected to each other. Besides, to avoid artificial in single-layer stitch, threads with different but similar line lengths and angles are intersected to each other. To tackle these challenges, we regards the single-layer stitch as a combination of many intersecting stitches which are the style primitives of our method, then we propose a method with two steps: intersecting stitches layout and connection to generate single-layer stitch. We introduce a Markov chain model which formulates the selection of intersecting stitches for layout as a sequential decision-making process, and can avoid artificial in single-layer stitch. Then a novel connectivity metrics and simulated annealing strategy based method is used to connect more intersecting stitch while maintain the structure of threads. Finally, this multi-layer stitch is obtained by stacking single-layer stitches multiple times to maintain the characteristic of multi-layering. We apply our method according to many reference images, and stitches with laying and aesthetics can be obtained in all case.

Keywords

Random-needle embroidery Multi-layer stitch Single-layer stitch The Markov chain Simulated annealing strategy Image-based artistic rendering 

Notes

Acknowledgments

This work was supported by National High Technology Research and Development Program of China (No.2007AA01Z334), National Natural Science Foundation of China (Nos.61321491 and 61272219), Innovation Fund of State Key Laboratory for Novel Software Technology (Nos. ZZKT2013A12, ZZKT2016A11 and ZZKT2018A09).

Supplementary material

11042_2019_8053_MOESM1_ESM.wmv (23.5 mb)
ESM 1 (WMV 24111 kb)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.National Key Laboratory for Novel Software TechnologyNanjing University, Xianlin CampusNanjingChina

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