Skip to main content

Pencil Drawing of Microscopic Images Through Edge Preserving Filtering

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11868)

Abstract

Automatic diatom identification approaches have revealed remarkable abilities to tackle the challenges of water quality assessment and other environmental issues. Scientists often analyze the taxonomic characters of the target taxa for automatic identification. In this process the digital photographs, sketches or drawings are recorded to analyze the shape and size of the frustule, the arrangement of striae, the raphe endings, and the striae density. In this paper, we describe two new methods for producing drawings of different diatom species at any stage of their life cycle development that can also be useful for future reference and comparisons. We attempt to produce drawings of diatom species using Edge-preserving Multi-scale Decomposition (EMD). The edge preserving smoothing property of Weighted Least Squares (WLS) optimization framework is used to extract high-frequency details. The details extracted from two-scale decomposition are transformed to drawings which help in identifying possible striae patterns from diatom images. To analyze the salient local features preserved in the drawings, the Scale Invariant Feature Transform (SIFT) model is adopted for feature extraction. The generated drawings help to identify certain unique taxonomic and morphological features that are necessary for the identification of the diatoms. The new methods have been compared with two alternative pencil drawing techniques showing better performance for details preservation.

Keywords

  • Edge-preserving filters
  • Diatom identification
  • Taxonomic
  • Drawings
  • Feature analysis

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-31321-0_17
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   79.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-31321-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   99.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

References

  1. British Diatomists of the 19th Century Database. http://rbg-web2.rbge.org.uk/DIADIST/dia_intro.htm

  2. How to turn any image into a pencil sketch with 10 lines of code. https://bit.ly/2FgkzCV. Accessed 28 Apr 2019

  3. Photoshop Blend Modes Explained. https://bit.ly/2RtJU1z. Accessed 28 Apr 2019

  4. Simple filters and pencil line drawing effect. https://bit.ly/2Vzn1Pg. Accessed 28 Apr 2019

  5. du Buf, H., Bayer, M.M.: Automatic Diatom Identification. Series in Machine Perception and Artificial Intelligence, vol. 51, pp. 1–316. World Scientific, Singapore (2002)

    CrossRef  Google Scholar 

  6. Hilaluddin, F., Leaw, C.P., Lim, P.: Fine structure of the diatoms Thalassiosira and Coscinodiscus (Bacillariophyceae): light and electron microscopy observation. Ann. Microsc. 10, 28–35 (2010)

    Google Scholar 

  7. Hilaluddin, F., Leaw, C.P., Lim, P.: Morphological observation of common pennate diatoms (Bacillariophyceae) from Sarawak estuarine waters. Ann. Microsc. 11, 12–23 (2011)

    Google Scholar 

  8. Farbman, Z., Fattal, R., Lischinski, D., Szeliski, R.: Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Trans. Graph. 27(3), 67:1–67:10 (2008)

    CrossRef  Google Scholar 

  9. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice Hall, Upper Saddle River (2008)

    Google Scholar 

  10. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3(6), 610–621 (1973). https://doi.org/10.1109/TSMC.1973.4309314

    CrossRef  Google Scholar 

  11. He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013)

    CrossRef  Google Scholar 

  12. Hicks, Y., Marshall, D., Rosin, P.L., Martin, R.R., Mann, D.G., Droop, S.: A model of diatom shape and texture for analysis, synthesis and identification. Mach. Vis. Appl. 17(5), 297–307 (2006)

    CrossRef  Google Scholar 

  13. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    CrossRef  Google Scholar 

  14. McLaughlin, R.B.: An Introduction to the Microscopical Study of Diatoms. Hooke College of Applied Sciences, Westmont (2012)

    Google Scholar 

  15. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979). https://doi.org/10.1109/TSMC.1979.4310076

    CrossRef  Google Scholar 

  16. Paris, S., Hasinoff, S.W., Kautz, J.: Local Laplacian filters: edge-aware image processing with a Laplacian pyramid. ACM Trans. Graph. 30(4), 68:1–68:12 (2011)

    CrossRef  Google Scholar 

  17. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)

    CrossRef  Google Scholar 

  18. Singh, H., Kumar, V., Bhooshan, S.: Weighted least squares based detail enhanced exposure fusion. ISRN Sig. Process. 2014, 18 (2014)

    Google Scholar 

  19. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of the Sixth International Conference on Computer Vision, ICCV 1998. IEEE Computer Society (1998)

    Google Scholar 

  20. Zahn, C.T., Roskies, R.Z.: Fourier descriptors for plane closed curves. IEEE Trans. Comput. C-21(3), 269–281 (1972)

    MathSciNet  CrossRef  Google Scholar 

  21. Zhu, F., Liang, Z., Jia, X., Zhang, L., Yu, Y.: A benchmark for edge-preserving image smoothing. IEEE Trans. Image Process. 28, 3556–3570 (2019). A publication of the IEEE Signal Processing Society

    MathSciNet  CrossRef  Google Scholar 

Download references

Acknowledgements

The authors acknowledge financial support of the Spanish Government under the Aqualitas-retos project (Ref. CTM2014-51907-C2-R-MINECO). The authors thank Saúl Blanco for providing subjective evaluation of the results.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Carlos Sánchez .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Singh, H., Sánchez, C., Cristóbal, G., Bueno, G. (2019). Pencil Drawing of Microscopic Images Through Edge Preserving Filtering. In: Morales, A., Fierrez, J., Sánchez, J., Ribeiro, B. (eds) Pattern Recognition and Image Analysis. IbPRIA 2019. Lecture Notes in Computer Science(), vol 11868. Springer, Cham. https://doi.org/10.1007/978-3-030-31321-0_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-31321-0_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31320-3

  • Online ISBN: 978-3-030-31321-0

  • eBook Packages: Computer ScienceComputer Science (R0)