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2006 | Vol. 15, No. 3/4 | 321-328
Tytuł artykułu

Particle measurement in scanning electron microscopy images

Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Konferencja
International Conference on Computer Vision and Graphics ICCVG 2006 (25-27.09.2006 ; Warsaw, Poland)
Języki publikacji
EN
Abstrakty
EN
This paper describes the segmentation of nanoparticles of ZnO obtained by mechanical milling. Segmentation of objects in images is a common application of computer vision methods. In contrast to manual segmentation, these techniques are fast, objective, and accurate. We describe in this paper a method based on such techniques aimed at segmenting the particles in a microscopic image of ZnO in order to obtain an approximation of the grain size, and a measure of the homogeneity, in a non-supervised way. The images are obtained using scanning electron microscopy and then preprocessed to enhance the contrast and to reduce the noise. Next, an edge detection algorithm is applied to obtain the boundaries of the particles. Finally, the particles that satisfy a specific criterion are extracted and measured, and their measure is taken as an approximation of the particle size.
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Rocznik
Strony
321-328
Opis fizyczny
Bibliogr. 15 poz., wykr., il.
Twórcy
  • Polytechnic University of Valencia, Alcoi Campus (Spain)
Bibliografia
  • [1] Canny J.: A Computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 1, No. 11. 679-698, 1986.
  • [2] Chen T., Ma K. K., Chen L. H.: Tri-State Median Filter for Image Denoising. IEEE Transactions on Image Processing, Vol. 8, No. 12. 1834-1838, 1999.
  • [3] Huttunen H., Yli-Harja O.: Fast algorithm for updating the local histogram of multidimensional signals. International Symposium on Nonlinear Theory and its Applications, NOLTA 65-68, 1999.
  • [4] Stark J. A.: Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Transactions on Image Processing, Vol. 9, No. 5. 889-895, 2000.
  • [5] Chen Y., Huang T. S.: Optimal radial contour tracking by dynamic programming. International Conference on Image Processing, Vol. 1. ICIP 626-629, 2001.
  • [6] Ding L., Goshtasby A.: On the canny edge detector. Pattern Recognition, Vol. 34. Elsevier 721-725, 2001.
  • [7] Ramponi G., Tenze L., Carrato S., Marsi S.: Nonlinear contrast enhancement based on the retinex approach. Image Processing: Algorithms and Systems II, Proceedings of SPIE/IS&T, Vol. 5014. 169-177, 2003.
  • [8] Starek J. L., Murtagh F., Candés E. J., Donoho D. L.: Gray and color image contrast enhancement by the Curvelet Transform. IEEE Transactions on Image Processing, Vol. 12, No.6. 706-716, 2003.
  • [9] Veenman C. J., Reinders M. J. T., Backer E.: A cellular coevolutionary algorithm for image segmentation IEEE Transactions on Image Processing, Vol. 12, No. 3. 304—316, 2003.
  • [10] Yue Z. Q., Chen S., Tham L. G.: Finite element modeling of geomaterials using digital image processing. Computers and Geotechnics, Vol. 30. Elsevier 375-397, 2003.
  • [11] Damonte L. C., Mendoza Zélisa L. A., Mari Soucaseb B., Hernandez Fenollosa M. A.: Nanoparticles of ZnO obtained by mechanical milling. Powder Technology, Vol. 148. Elsevier 15-19, 2004.
  • [12] Prasad D., Ray N., Acton S. T.: Level set analysis for leukocyte detection and tracking. IEEE Transactions On Image Processing, Vol. 13, No. 4. 562-572, 2004.
  • [13] Yu Z., Bajaj C.: Detecting circular and rectangular particles based on geometric feature detection in electron micrographs. Journal of Structural Biology, Vol. 145. Elsevier 168-180, 2004.
  • [14] Yu Z., Bajaj C.: A fast and adaptive method for image contrast enhancement. IEEE International Conference on Image Processing, ICIP04, 2004.
  • [15] Zhu Y. et al: Automatic particle selection: Results of a Comparative Study. Journal of Structural Biology, Vol. 145. Elsevier 3-14, 2004.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-article-BWA1-0025-0009
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