Research the possibilities of different filters and their application to image recognition problems

• Autorzy: Brytik V. , Grebinnik O. , Kobziev V.
• Języki publikacji: EN

Abstrakty

EN

This article is devoted to the analysis from the viewpoint of accuracy allocation of contour points, and maintaining information about the distribution of non-derivative elements. In this article is researched the edge enhancement algorithms such as Prewitt, Sobel, Laplacian, Kirsch and evaluated their work, using some well-known performance measures. Also compared the efficacy of the researched algorithms, which was determined on the selected images with superimposed noise.

Słowa kluczowe

EN

image analysis; contour points; filter; convolution mask

• Wydawca: Polish Academy of Sciences, Branch in Lublin
• Czasopismo: ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
• Rocznik: 2016
• Tom: Vol. 5, No 4
• Strony: 21--27
• Opis fizyczny: Bibliogr. 15 poz., rys., tab., wz.

Twórcy

autor

Brytik V.

• Kharkiv National University of Radio Electronics

autor

Grebinnik O.

• Kharkiv National University of Radio Electronics

autor

Kobziev V.

• Kharkiv National University of Radio Electronics

Bibliografia

• 1. Duda R., Hart P., 1973. Pattern Classification and Scene Analysis. New York: Wiley.
• 2. Tou T., Gonzalez R., 1974. Pattern Recognition Principles. Addison-Wesley, Reading, Mass. 97-104.
• 3. Gonzales R., Woods R. 2007. Digital Image Processing. Prentice Hall, 976.
• 4. Shapiro L., Stockman G., 2006. Computer Vision. Мoscow, BYNOM. 752.
• 5. Putyatin E., Matat E., 2003. Information systems technology. Image processing and pat tern recognition. Kharkiv National University of Radio Electronics. Kharkiv. 105.
• 6. 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.
• 7. Liu X., Wang D., 2006. Image and Texture Segmentation Using Local Spectral Histograms // IEEE Transaction on image processing, Vol. 15, No. 10, 3066-3077.
• 8. Semenets V., Natalukha Yu., Taranukha O., Tokarev V. 2014. About One Method of Mathematical Modelling of Human Vision Functions. ECONTECHMOD. An international quarterly journal Vol. 3, №3, 51-59.
• 9. Brytik V.I., Zhilina O.Yu., Kobziev V.G. 2014. Structural Method of Describing The Texture Images. ECONTECHMOD. An international quarterly journal Vol. 3, №3, 89-98.
• 10. Anokhin M., Koryttsev I., 2015. Decisionmaking rule efficiency estimation with applying similarity metrics. ECONTECHMOD. An international quarterly journal. Vol. 4, №3, 73-78.
• 11. Vitcus R., Yaroslavskiy L., 1988. The adaptive linear filters for image processing. Мoscow, Nauka.
• 12. Forsyth D., 2004. Computer vision: a modern approach – Мoscow, Publishing House Williams. 928.
• 13. Kinoshenko D., Mashtalir V., Shlyakhov V., Yegorova E., 2010. Nested Partitions Properties for Spatial Content Image Retrieval // International Journal of Digital Library Systems.– Vol. 1, No 3. - 58-89.
• 14. Venmathi A., Ganesh E., Kumaratharan N., 2016. Kirsch Compass Kernel Edge Detection Algorithm for Micro Calcification Clusters in Mammograms. Middle-East Journal of Scientific Research 24 (4). - 1530-1535.
• 15. Brytik V., Zhilina E., 2014. Investigation possibilities of various filters which used in pattern recognition problems // Bionica Intellecta. 2(83), 88-95.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).