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Segmentation of Nanocolumnar Crystals from Microscopic Images

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Image Analysis and Recognition (ICIAR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3656))

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Abstract

This paper addresses the segmentation of crystalline Zinc oxide nanocolumns from microscopic images. ZnO is a direct band semiconductor suitable for many applications whose interest has been growing recently. One of these applications are light-collecting devices such as solar cells, using nanostructured substrates. Electrodeposition is a low cost technique very suitable for the preparation of nanostructured ZnO, producing nanocolumnar ZnO crystals with a morphology that depends on the deposition parameters and the substrate characteristics. The parameters of the sample can be determined processing images of the nanostructures, which is the objective of this study.

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References

  1. Cembrero, J., Elmanouni, A., Hartiti, B., Mollar, M., Marí, B.: Nanocolumnar ZnO Films for Photovoltaic Applications. Thin Solid Films, pp. 198–202. Elsevier, Amsterdam (2004)

    Google Scholar 

  2. Damonte, L.C., Mendoza, L.A., Marí, B., Hernández, M.A.: Nanoparticles of ZnO Obtained by Mechanical Milling. In: Powder Technology, vol. 148, pp. 15–19. Elsevier, Amsterdam (2004)

    Google Scholar 

  3. Yu, Z., Bajaj, C.: Detecting Circular and Rectangular Particles Based on Geometric Feature Detection in Electron Micrographs. Journal of Structural Biology 145, 168–180 (2004)

    Article  Google Scholar 

  4. Zhu, Y., et al.: Automatic Particle Selection: Results of a Comparative Study. Journal of Structural Biology 145, 3–14 (2004)

    Article  Google Scholar 

  5. Prasad, D., Ray, N., Acton, S.T.: Level Set Analysis for Leukocyte Detection and Tracking. IEEE Transactions On Image Processing 13(4), 562–572 (2004)

    Article  Google Scholar 

  6. Yue, Z.Q., Chen, S., Tham, L.G.: Finite Element Modeling of Geomaterials Using Digital Image Processing. Computers and Geotechnics 30, 375–397 (2003)

    Article  Google Scholar 

  7. Chen, T., Ma, K.K., Chen, L.H.: Tri-State Median Filter for Image Denoising. IEEE Transactions on Image Processing 8(12), 1834–1838 (1999)

    Article  Google Scholar 

  8. Ablameyko, S., Pridmore, T.: Machine Interpretation of Line Drawing Images, Technical Drawings, Maps and Diagrams. Springer, Heidelberg (2000)

    Google Scholar 

  9. Otsu, N.: A Threshold Selection Method from Gray Level Histograms. IEEE Transactions on Systems, Man and Cybernetics 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  10. Canny, J.: A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 1(11), 679–698 (1986)

    Article  Google Scholar 

  11. Maher, A., Rabab, W.: A Rotation Invariant Rule-Based Thinning Algorithm for Character Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(12), 1672–1678 (2002)

    Article  Google Scholar 

  12. Wenyin, L., Dori, D.: Incremental Arc Segmentation Algorithm and Its Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(4), 424–430 (1998)

    Article  Google Scholar 

  13. Qiao, Y., Ong, S.H.: Connectivity-based Multiple-circle Fitting. In: Pattern Recognition, vol. 37, pp. 755–765. Elsevier, Amsterdam (2004)

    Google Scholar 

  14. Zhang, S.C., Liu, Z.Q.: A Robust, Real-time Ellipse Detector. In: Pattern Recognition, vol. 38, pp. 273–287. Elsevier, Amsterdam (2005)

    Google Scholar 

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Author information

Authors and Affiliations

  1. DISCA Department, Polytechnic University of Valencia, Alcoi Campus, Plaza Ferràndiz i Carbonell, 2, 03801, Alcoi, Spain

    David Cuesta Frau & Pau Micó Tormos

  2. Department of Applied Physics, Polytechnic University of Valencia, Camíde Vera s/n, 46071, Valencia, Spain

    María Ángeles Hernández-Fenollosa

  3. DSIC Department, Polytechnic University of Valencia, Alcoi Campus, Plaza Ferràndiz i Carbonell, 2, 03801, Alcoi, Spain

    Jordi Linares-Pellicer

Authors
  1. David Cuesta Frau
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  2. María Ángeles Hernández-Fenollosa
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  3. Pau Micó Tormos
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  4. Jordi Linares-Pellicer
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Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering Department, University of Waterloo,  

    Mohamed Kamel

  2. FEUP - Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, P.O. Box, 4200-465, Porto, Portugal

    Aurélio Campilho

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© 2005 Springer-Verlag Berlin Heidelberg

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Frau, D.C., Hernández-Fenollosa, M.Á., Tormos, P.M., Linares-Pellicer, J. (2005). Segmentation of Nanocolumnar Crystals from Microscopic Images. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2005. Lecture Notes in Computer Science, vol 3656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559573_8

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  • DOI: https://doi.org/10.1007/11559573_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29069-8

  • Online ISBN: 978-3-540-31938-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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