PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Structural method of describing the texture images

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This article describes the histograms and polarograms obtained from the different types of textures using BRVAL filters. Comparative analysis of the polarograms and histograms showed that BRVAL filters description for textures in a wide range of distances and the light does not depend by the researched factors, and the level of detail segments on polarograms are inversely proportional to the increasing the number of absolute zero tending picture elements.
Twórcy
autor
  • Kharkov National University of Radio Electronics Lenin Ave, 14, Kharkov, 61166, Ukraine
  • Kharkov National University of Radio Electronics Lenin Ave, 14, Kharkov, 61166, Ukraine
  • Kharkov National University of Radio Electronics Lenin Ave, 14, Kharkov, 61166, Ukraine
Bibliografia
  • 1. Aujol J.F., Gilboa G., Chan T., Osher S. 2006. Structure-Texture Image Decomposition - Modeling, Algorithms, and Parameter Selection // International Journal of Computer Vision, 67(1), 111–136.
  • 2. Boychuk P., Boychuk Kh., Nahorski Z., Horabik J. 2012. Spatial inventory of greenhouse gas emissions from the road transport in Poland // Econtechmod. An international quarterly journal. Vol. 01, No. 4, 09–15.
  • 3. Brytik V.I., Gvozdenko A.N., Kobziev V.G., Semenets V.V. 2014. The use of texture analysis of fotoimages // XII Conference on High Energy Physics, Nuclear Physics and Accelerators. – Kharkov, NNC KhFTI, 55.
  • 4. Davis L.S. 1980. Image texture analysis tecniquse - A Survey // Digital Image Processing. Proceedings of the NATO Advanced Study Institude. 189-201.
  • 5. Duda R.O. and Hart P.E. 1973. Pattern Classification and Scene Analysis. New York: Wiley.
  • 6. Julesz B. 1975. Experiments in the visual perception of texture // Sci. Amer. - V. 232, К 1. - 34 - 43.
  • 7. Julesz B. 1981. Textons, the elements of texture perception, and their interactions // Nature. - V. 290, К 5. 91 - 97.
  • 8. Kirvida L. 1976. Texture Measure-ment for the Automatic Classification of Image // IEEE Trans. On Electromagnety Compability. February. Vol. 18. 38-42.
  • 9. Kolodnikova N. 2004. Overview of the textural features for pattern recognition problems // Reports TUSUR. The information automated processing systems, control and design. 113.
  • 10. Lee D.-C., Schenk T. Image segmentation from texture measurement [electronic resource]: access mode: http://www.isprs.org/proceedings/XXIX/congress/part3/195_XXIX-part3.pdf
  • 11. Lesiv M., Bun R., Shpak N., Danylo O. and Topylko P. 2012. Spatial analysis of GHG emissions in eastern polish regions: energy production and residential sector // Econtechmod. An international quarterly journal. Vol. 01, No. 2, 17–23.
  • 12. Liu X., Wang D.L. 2006. Image and Texture Segmentation Using Local Spectral Histograms // IEEE Transaction on image processing, Vol. 15, No. 10, 3066-3077.
  • 13. Malik J., Belongie S., Leung T., Shi J. 2001. Contour and Texture Analysis for Image Segmentation // International Journal of Computer Vision, 43(1), 7–27.
  • 14. Protasov K., Ryumkin A. 2002. Nonparametric algorithm for recognizing objects of the underlying surface of the Earth according to the Aerospace Survey // Herald of the Tomsk State University. - № 275. - 41-46.
  • 15. Putyatin E., Matat E. 2003. Information systems technology. Image processing and pattern recognition. Kharkiv National University of Radio Electronics. Kharkiv. 105.
  • 16. Reyuord-Smith W. Formal language theory. - M.: Radio and Communica-tions, 1988. - 128.
  • 17. Sayadi M., Tlig L., Fnaiech F. 2007. A New Texture Segmentation Method Based on the Fuzzy C-Mean Algorithm and Statistical Features // Applied Mathematical Sciences. 1(60), 2999-3007.
  • 18. Shapiro L.G. and Stockman G.C. 2001. Computer Vision, Upper Saddle River: Prentice–Hall.
  • 19. Tou T. and Gonzalez R. 1974. Pattern Recognition Principles. Addison-Wesley, Reading, Mass. 97-104.
  • 20. Zhu S.C., Wu Y.N., Mumford D. 1997. Minimax entropy principle and its application to texture modeling // Neural Comput., vol. 9, 1627–1660.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-24f41381-1ea8-4ed5-a696-702fdfd3636b
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.