Skip to main content
SpringerLink
Log in

Paint with stitches: a style definition and image-based rendering method for random-needle embroidery

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Random-needle Embroidery is a graceful Chinese art designated as Intangible Cultural Heritage, which “draws” beautiful images with thousands of free-form threads. In this paper, we explore techniques for automatically translating an input image into an art image with the random-needle style. The key idea is to generate rendering primitives of this art first, from which the corresponding dictionary is learned to further sparsely code the contents in the input image. To this end, we first define the artistic style of Random-needle Embroidery by introducing the notion of “stitch”, i.e., collection of threads arranged in a certain pattern, as the basic rendering primitive. Then, we adopt sparse coding to generate a stitch dictionary which gives a compact representation of the generated stitches. During runtime, new and more image content-adaptive stitches can be synthesized by optimizing a linear combination of stitch dictionary atoms via sparse representation. Then, the synthesized stitches are placed on the canvas sequentially and connected to adjacent stitches by stitch quilting. After placing all the stitches, a blank filling strategy is proposed and adopted to fill the uncovered areas on the canvas. The experimental results show our method can generate engaging images with the random-needle style. Moreover, our rendering image is better than those obtained by using two other state-of-the art methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25

Similar content being viewed by others

References

  1. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S (2012) Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282

    Article  Google Scholar 

  2. Bénard P, Bousseau A, Thollot J (2011) State-of-the-art report on temporal coherence for stylized animations. Computer Graphics Forum 30(8):2367–2386

    Article  Google Scholar 

  3. Chen, X, McCool, M, Kitamoto, A, Mann, S (2012) Embroidery modeling and rendering. In Proceedings of Graphics Interface 2012, pp. 131–139

  4. Darabi S, Shechtman E, Barnes C, Goldman DB, Sen P (2012) Image melding: Combining inconsistent images using patch-based synthesis. ACM Trans Graph 31(4):82:1–82:10

    Article  Google Scholar 

  5. DeCarlo D, Santella A (2002) Stylization and abstraction of photographs. ACM Trans Graph 21(3):769–776

    Article  Google Scholar 

  6. Delong A, Osokin A, Isack HN, Boykov Y (2010) Fast approximate energy minimization with label costs. In proceeding of IEEE computer vision and pattern recognition (CVPR), pp. 2173–2180

  7. Donoho D (2006) For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution. Commun Pure Appl Math 59(6):797–826

    Article  MathSciNet  MATH  Google Scholar 

  8. Efros AA, Freeman WT (2001) Image quilting for texture synthesis and transfer. In Proceedings of the 28th annual conference on computer graphics and interactive techniques, pp. 341–346

  9. Hegde S, Gatzidis C, Tian F (2013) Painterly rendering techniques: a state-of-the-art review of current approaches. Computer Animation & Virtual Worlds 24(1):43–64

    Article  Google Scholar 

  10. Hertzmann A (2003) A survey of stroke-based rendering. IEEE Comput Graph Appl 23(4):70–81

    Article  Google Scholar 

  11. Huang H, Fu TN, Li CF (2011) Painterly rendering with content-dependent natural paint strokes. Vis Comput 27(9):861–871

    Article  Google Scholar 

  12. Inglis TC, Vogel D, Kaplan CS (2013) Rasterizing and antialiasing vector line art in the pixel art style. In proceedings of the symposium on non-photorealistic animation and rendering, pp. 25–32.

  13. Khan TM, Bailey DG, Khan MA, Kong Y (2017) Efficient hardware implementation for fingerprint image enhancement using ani-sotropic gaussian filter. IEEE Trans Image Process 26(5):2116–2126

    Article  MathSciNet  Google Scholar 

  14. Kopf J, Lischinski D (2011) Depixelizing pixel art. ACM Transactions on Graphics 30(4):99:1–99:8

    Article  Google Scholar 

  15. Kopf J, Cohen-Or D, Deussen O, Lischinski D (2006) Recursive Wang tiles for real-time blue noise. ACM Transactions on Graphics (TOG) 25(3):509–518

    Article  Google Scholar 

  16. Krompiec P, Park K, Liang D, Lee C (2016) Deformable strokes towards temporally coherent video painting. Vis Comput 32(6–8):813–823

    Article  Google Scholar 

  17. Kwatra V, Schödl A, Essa I, Turk G, Bobick A (2003) Graphcut textures: image and video synthesis using graph cuts. ACM Transactions on Graphics (ToG) 22(3):277–286

    Article  Google Scholar 

  18. Kyprianidis JE, Collomosse J, Wang T, Isenberg T (2013) State of the "art": a taxonomy of artistic stylization techniques for images and video. IEEE Transaction on Visualization and Computer Graphics 19(5):866–885

    Article  Google Scholar 

  19. Lindemeier T, Metzner J, Pollak L, Deussen O (2015) Hardware-based non-photorealistic rendering using a painting robot. Computer Graphics Forum 32(2):311–323

    Article  Google Scholar 

  20. Lo KH, Wang YC, Hua KL (2016) Example-based image textural style transfer. IEEE Transaction on Multimedia 23(4):60–66

    Article  Google Scholar 

  21. Lu J, Barnes C, DiVerdi S, Finkelstein A (2013) Realbrush: painting with examples of physical media. ACM Transactions on Graphics 32(4):117:1–117:12

    Article  Google Scholar 

  22. Maciejewski R, Isenberg T, Andrews WM, Ebert DS, Sousa MC, Chen W (2008) Measuring stipple aesthetics in hand-drawn and computer-generated images. IEEE Comput Graph Appl 28(2):67–42

    Article  Google Scholar 

  23. Mairal J, Bach F, Ponce J, Sapiro G (2010) Online learning for matrix factorization and sparse coding. J Mach Learn res 11:19–60

    MathSciNet  MATH  Google Scholar 

  24. Martin D, Del Sol V, Romo C, Isenberg T (2015) Drawing characteristics for reproducing traditional handmade stippling. In Proceedings of the workshop on non-photorealistic animation and rendering, pp. 103–115

  25. O’Donovan P, Hertzmann A (2011) Anipaint: interactive painterly animation from video. IEEE Transactions on Visualization & Computer Graphics 18(3):475–487

    Article  Google Scholar 

  26. Ostromoukhov V (2001) A simple and efficient error-diffusion algorithm. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pp. 567–572

  27. Pang WM, Qu Y, Wong TT, Cohen-Or D, Heng PA (2008) Structure-aware halftoning. ACM Transactions on Graphics 27(3):89:1–89:8

    Article  MathSciNet  Google Scholar 

  28. Papyan V, Elad M (2016) Multi-scale patch-based image restoration. IEEE Trans Image Process 25(1):249–261

    Article  MathSciNet  Google Scholar 

  29. Qu Y, Pang WM, Wong TT, Heng PA (2008) Richness-preserving manga screening. ACM Transactions on Graphics 27(5):155:1–155:8

    Article  Google Scholar 

  30. Rosin P, Collomosse J (2013) Image and video-based artistic stylisation, vol. 42. Springer-Verlag, London. doi:10.1007/978-1-4471-4519-6

  31. Salisbury MP (1997) Orientable textures for image-based pen-and-ink illustration. In Proceedings of the 24th annual conference on Computer graphics and interactive techniques, pp. 401–406

  32. Schröder K, Zinke A, Klein R (2015) Image-based reverse engineering and visual prototyping of woven cloth. IEEE Trans Vis Comput Graph 21(2):188–200

    Article  Google Scholar 

  33. Semmo A, Limberger D, Kyprianidis JE, Döllner J (2016) Image stylization by interactive oil paint filtering. Comput Graph 55:157–171

    Article  Google Scholar 

  34. Song YZ, Pickup D, Li C, Rosin P, Hall P (2013) Abstract art by shape classification. IEEE Trans Vis Comput Graph 19(8):1252–1263

    Article  Google Scholar 

  35. Sonka M, Hlavac V, Boyle R (1993) Image processing, analysis and machine vision. Springer US. doi:10.1007/978-1-4899-3216-7

  36. Stone M (2016) A field guide to digital color. CRC Press, Boca Raton

  37. Tamura H, Mori S, Yamawaki T (1978) Textural features corresponding to visual perception. IEEE Transactions on Systems, Man, and Cybernetics 8(6):460–473

    Article  Google Scholar 

  38. Ulichney R (1987) Digital halftoning. MIT Press, Cambridge

  39. Wang Z, Sheikh HR, Bovik AC (2002) No-reference perceptual quality assessment of jpeg compressed images. In Proceedings of International conference on image processing, pp. 477–480

  40. Wang X, Georganas ND, Petriu EM (2011) Fabric texture analysis using computer vision techniques. IEEE Trans Instrum Meas 60(1):44–56

    Article  Google Scholar 

  41. Wei LY (2008) Parallel poisson disk sampling. ACM Transactions on Graphics 27(3):20:1–20:9

    MathSciNet  Google Scholar 

  42. Wexler Y, Shechtman E, Irani M (2007) Space-time completion of video. IEEE Trans Pattern Anal Mach Intell 29(3):463–476

    Article  Google Scholar 

  43. Xu X, Zhong L, Xie M, Qin J, Chen Y, Jin Q, Wong TT, Han G (2015) Texture-aware ascii art synthesis with proportional fonts. In Proceedings of the workshop on non-photorealistic animation and rendering, pp. 183–193

  44. Yang S, Liu Z, Wang M, Sun F, Jiao L (2011) Multitask dictionary learning and sparse representation based single-image super-resolution reconstruction. Neurocomputing 74(17):3193–3203

    Article  Google Scholar 

  45. Yang S, Wang M, Chen Y, Sun Y (2012) Single-image super-resolution reconstruction via learned geometric dictionaries and clustered sparse coding. IEEE Trans Image Process 21(9):4016–4028

    Article  MathSciNet  MATH  Google Scholar 

  46. Yang K, Sun Z, Ma C, Yang W (2016) Paint with stitches: a random-needle embroidery rendering method. In Proceedings of the 33rd computer graphics International, pp. 9–12

  47. Yue X, Miao D, Cao L, Wu Q, Chen Y (2014) An efficient color quantization based on generic roughness measure. Pattern Recogn 47(4):1777–1789

    Article  MATH  Google Scholar 

  48. Zeng K, Zhao M, Xiong C, Zhu SC (2009) From image parsing to painterly rendering. ACM Transaction on Graphics 29(1):2:1–2:11

    Article  Google Scholar 

  49. Zhang W, Cao C, Chen S, Liu J, Tang X (2013) Style transfer via image component analysis. IEEE Transactions on Multimedia 15(7):1594–1601

    Article  Google Scholar 

  50. Zhao M, Zhu SC (2010) Sisley the abstract painter. In Proceedings of the 8th International Symposium on non-photorealistic animation and rendering, pp. 99–107

  51. Zhao M, Zhu SC (2011) Customizing painterly rendering styles using stroke processes. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on non-photorealistic animation and rendering, pp. 137–146

  52. Zhao S, Luan F, Bala K (2016) Fitting procedural yarn models for realistic cloth rendering. ACM Transactions on Graphics 35(4):51:1–51:11

    Google Scholar 

  53. Zhou J, Sun Z, Yang K (2014) A controllable stitch layout strategy for random needle embroidery. Journal of Zhejiang University SCIENCE C 15(9):729–743

    Article  Google Scholar 

Download references

Acknowledgements

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 and ZZKT2016A11), and Program for New Century Excellent Talents in University of China (NCET-04-04605).

Author information

Authors and Affiliations

  1. State Key Lab for Novel Software Technology, Nanjing University, Nanjing, 210023, China

    Kewei Yang & Zhengxing Sun

Authors
  1. Kewei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhengxing Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhengxing Sun.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, K., Sun, Z. Paint with stitches: a style definition and image-based rendering method for random-needle embroidery. Multimed Tools Appl 77, 12259–12292 (2018). https://doi.org/10.1007/s11042-017-4882-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-4882-8

Keywords

Search

Navigation