Skip to main content
SpringerLink
Log in

A vectorization framework for constant and linear gradient filled regions

  • Original Article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Linear gradients are commonly applied in non-photographic artwork for shading and other artistic effects. It is sometimes necessary to generate a vector graphics form of raster images comprising such artwork with the expectation to obtain a simple output and plug it into a traditional workflow, to be further edited and arranged. Many such workflows support only linear gradients and our goal is to generate a standard vector form of the image that can fit such workflow. This vectorization process should be automatic with minimal user intervention. We present a simple image vectorization algorithm that detects regions of linear gradient in potentially noisy images and reconstructs the vector definition on the basis of that information. It uses a novel interval gradient optimization scheme to derive large regions of uniform gradient. We also demonstrate the technique on noisy and hand-drawn portraits.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Adobe Systems Inc., Adobe illustrator CS5 (2010)

  2. Barla, P., Bousseau, A.: Gradient art: creation and vectorization. In: Rosin, P., Colomosse, J. (eds.) Image and Video Based Artistic Stylization. Springer, New York, Nov 2012

  3. Barrett, W.A., Cheney, A.S.: Object-based image editing. In: Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, ACM, SIGGRAPH ’02, pp. 777–784, New York, NY, USA (2002)

  4. Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000)

  5. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)

    Article  Google Scholar 

  6. Dori, D., Liu, W.: Sparse pixel vectorization: an algorithm and its performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 21, 202–215 (1999)

    Article  Google Scholar 

  7. Fan, K.-C., Chen, D.-F., Wen, M.-G.: A new vectorization-based approach to the skeletonization of binary images. In: Proceedings of ICDAR, pp. 627–630. IEEE Computer Society Washington, DC, USA (1995)

  8. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  9. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Addison-Wesley Longman Publishing Co. Inc, Boston (1992)

    Google Scholar 

  10. Hori, O., Tanigawa, S.: Raster-to-vector conversion by line fitting based on contours and skeletons. In: Proceedings of the Second International Conference on Document Analysis and Recognition 1993, pp. 353–358, Oct 1993

  11. Inkscape. An open source linux/windows scalable vector graphics editor (2010)

  12. Jeschke, S., Cline, D., Wonka, P.: Estimating color and texture parameters for vector graphics. Comput. Gr. Forum 30(2), 523–532 (2011). This paper won the 2nd best paper award at Eurographics 2011

    Article  Google Scholar 

  13. Kansal, R., Kumar, S.: A framework for detection of linear gradient filled regions and their reconstruction for vector graphics. In: Proceedings of WSCG’2013, communication papers, pp. 220–229, June 2013

  14. Lai, Y.-K., Hu, S.-M., Martin, R.R.: Automatic and topology-preserving gradient mesh generation for image vectorization. ACM Trans. Gr. 28(3), 85:1–85:8 (2009)

    Article  Google Scholar 

  15. Lecot, G., Levy, B.: Ardeco: automatic region detection and conversion. In: Proceedings of Eurographics Symposium on Rendering (2006)

  16. Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theor. 28(2), 129–137 (2006)

    Article  MathSciNet  Google Scholar 

  17. Mammadov, M.A.: A new global optimization algorithm based on dynamical systems approach. In: Proceedings of the 6th International Conference on Optimization: Techniques and Applications (ICOTA’ 04), Ballarat, Australia (2004)

  18. Mumford, D., Shah, J.: Boundary detection by minimizing functionals. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (1985)

  19. Orzan, A., Bousseau, A., Winnemöller, H., Barla, P., Thollot, J., Salesin, D.: Diffusion curves: a vector representation for smooth-shaded images. In: Proceedings of ACM SIGGRAPH 2008 papers, SIGGRAPH ’08, ACM, pp. 92:1–92:8. New York, NY, USA (2008)

  20. SVG working group. SVG format for vector graphics

  21. Selinger, P.: Potrace: a polygon-based tracing algorithm (2003)

  22. Sertl, S., Dellnitz, M.: Global optimization using a dynamical systems approach. J. Glob. Optim. 34(4), 569–587 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  23. Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–168 (2004)

    Article  Google Scholar 

  24. Stockman, G., Shapiro, L.G.: Computer Vision, 1st edn. Prentice Hall PTR, Upper Saddle River (2001)

    Google Scholar 

  25. Sun, J., Liang, L., Wen, F., Shum, H.-Y.: Image vectorization using optimized gradient meshes. In: Proceedings of ACM SIGGRAPH 2007 papers, SIGGRAPH ’07, ACM, New York, NY, USA (2007)

  26. Tamura, H.: A comparison of line thinning algorithms from digital geometry viewpoint. In: Proceedings of the Fourth International Joint Conference Pattern Recognition, Kyoto, Japan (1978)

  27. University of Ballarat. GANSO library for optimization functions

  28. Xia, T., Liao, B., Yu, Y.: Patch-based image vectorization with automatic curvilinear feature alignment. In: Proceedings of ACM SIGGRAPH Asia 2009 papers, SIGGRAPH Asia ’09, ACM, pp. 115:1–115:10. New York, NY, USA (2009)

  29. Zhang, S.-H., Chen, T., Zhang, Y.-F., Martin, R.R.: Vectorizing cartoon animations. IEEE Trans. Vis. Comput. Gr. 15(4), 618–629 (July 2009)

Download references

Acknowledgments

The images used in Sect. 4.6 have been taken from ARDECO [15] website. Other images have been taken from various open content provider websites. This research was supported in part by the department of Science and Technology, government of India.

Author information

Authors and Affiliations

  1. IIT, Delhi, Delhi, India

    Ruchin Kansal & Subodh Kumar

Authors
  1. Ruchin Kansal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Subodh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruchin Kansal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kansal, R., Kumar, S. A vectorization framework for constant and linear gradient filled regions. Vis Comput 31, 717–732 (2015). https://doi.org/10.1007/s00371-014-0997-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-014-0997-3

Keywords

Search

Navigation