Advertisement

Computational Visual Media

, Volume 3, Issue 2, pp 177–188 | Cite as

User-guided line abstraction using coherence and structure analysis

  • Hui-Chi Tsai
  • Ya-Hsuan Lee
  • Ruen-Rone Lee
  • Hung-Kuo Chu
Open Access
Research Article
  • 308 Downloads

Abstract

Line drawing is a style of image abstraction where the perceptual content of the image is conveyed using distinct straight or curved lines. However, extracting semantically salient lines is not trivial and mastered only by skilled artists. While many parametric filters have successfully extracted accurate and coherent lines, their results are sensitive to parameter choice and easily lead to either an excessive or insufficient number of lines. In this work, we present an interactive system to generate concise line abstractions of arbitrary images via a few user specified strokes. Specifically, the user simply has to provide a few intuitive strokes on the input images, including tracing roughly along edges and scribbling on the region of interest, through a sketching interface. The system then automatically extracts lines that are long, coherent and share similar textural structures to form a corresponding highly detailed line drawing. We have tested our system with a wide variety of images. Our experimental results show that our system outperforms state-of-the-art techniques in terms of quality and efficiency.

Keywords

line abstraction interactive drawing coherence strokes structure strokes stroke matching 

Notes

Acknowledgements

We are grateful to the anonymous reviewers for their comments and suggestions. The work was supported in part by the “Ministry of Science and Technology of Taiwan” (Nos. 103-2221-E-007-065-MY3 and 105-2221-E-007-104-MY2).

Supplementary material

41095_2016_76_MOESM1_ESM.pdf (1.1 mb)
User-guided line abstraction using coherence and structure analysis

References

  1. [1]
    Canny, J. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. PAMI-8, No. 6, 679–698, 1986.CrossRefGoogle Scholar
  2. [2]
    Kyprianidis, J. E.; Döllner, J. Image abstraction by structure adaptive filtering. In: Proceedings of the EG UK Theory and Practice of Computer Graphics, 51–58, 2008.Google Scholar
  3. [3]
    Winnemöeller, H.; Olsen, S. C.; Gooch, B. Real-time video abstraction. ACM Transactions on Graphics Vol. 25, No. 3, 1221–1226, 2006.CrossRefGoogle Scholar
  4. [4]
    Winnemöller, H.; Kyprianidis, J. E.; Olsen, S. C. XDoG: An extended difference-of-Gaussians compendium including advanced image stylization. Computers & Graphics Vol. 36, No. 6, 740–753, 2012.CrossRefGoogle Scholar
  5. [5]
    Arbelaez, P.; Maire, M.; Fowlkes, C.; Malik, J. Contour detection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 33, No. 5, 898–916, 2011.CrossRefGoogle Scholar
  6. [6]
    Maire, M.; Arbelaez, P.; Fowlkes, C.; Malik, J. Using contours to detect and localize junctions in natural images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1–8, 2008.Google Scholar
  7. [7]
    Limpaecher, A.; Feltman, N.; Treuille, A.; Cohen, M. Real-time drawing assistance through crowdsourcing. ACM Transactions on Graphics Vol. 32, No. 4, Article No. 54, 2013.Google Scholar
  8. [8]
    Su, Q.; Li, W. H. A.; Wang, J.; Fu, H. EZsketching: Three-level optimization for error-tolerant image tracing. ACM Transactions on Graphics Vol. 33, No. 4, Article No. 54, 2014.Google Scholar
  9. [9]
    Gleicher, M. Image snapping. In: Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, 183–190, 1995.Google Scholar
  10. [10]
    Li, Y.; Sun, J.; Tang, C.-K.; Shum, H.-Y. Lazy snapping. ACM Transactions on Graphics Vol. 23, No. 3, 303–308, 2004.CrossRefGoogle Scholar
  11. [11]
    Lee, Y. J.; Zitnick, C. L.; Cohen, M. F. ShadowDraw: Real-time user guidance for freehand drawing. ACM Transactions on Graphics Vol. 30, No. 4, Article No. 27, 2011.Google Scholar
  12. [12]
    Iarussi, E.; Bousseau, A.; Tsandilas, T. The drawing assistant: Automated drawing guidance and feedback from photographs. In: Proceedings of the ACM Symposium on User Interface Software and Technology, 2013.Google Scholar
  13. [13]
    Dixon, D.; Prasad, M.; Hammond, T. iCanDraw: Using sketch recognition and corrective feedback to assist a user in drawing human faces. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 897–906, 2010.Google Scholar
  14. [14]
    Lu, J.; Barnes, C.; DiVerdi, S.; Finkelstein, A. RealBrush: Painting with examples of physical media. ACM Transactions on Graphics Vol. 32, No. 4, Article No. 117, 2013.Google Scholar
  15. [15]
    Berger, I.; Shamir, A.; Mahler, M.; Carter, E.; Hodgins, J. Style and abstraction in portrait sketching. ACM Transactions on Graphics Vol. 32, No. 4, Article No. 55, 2013.Google Scholar
  16. [16]
    Yang, S.; Wang, J.; Shapiro, L. Supervised semantic gradient extraction using linear-time optimization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2826–2833, 2013.Google Scholar
  17. [17]
    Wobbrock, J. O.; Wilson, A. D.; Li, Y. Gestures without libraries, toolkits or training: A $1 recognizer for user interface prototypes. In: Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology, 159–168, 2007.Google Scholar
  18. [18]
    Orbay, G.; Kara, L. B. Beautification of design sketches using trainable stroke clustering and curve fitting. IEEE Transactions on Visualization and Computer Graphics Vol. 17, No. 5, 694–708, 2011.CrossRefGoogle Scholar
  19. [19]
    Floyd, R. W. Algorithm 97: Shortest path. Communications of the ACM Vol. 5, No. 6, 345, 1962.CrossRefGoogle Scholar
  20. [20]
    McDonald, R.; Smith, K. J. CIE94—A new colourdifference formula. Journal of the Society of Dyers and Colourists Vol. 111, No. 12, 376–379, 1995.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2016

Open Access The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Department of Computer Science“National Tsing Hua University”Hsinchu, TaiwanChina
  2. 2.Information and Communications Research LaboratoriesIndustrial Technology Research InstituteChutung, Hsinchu, TaiwanChina

Personalised recommendations