Texture image segmentation based on estimation of density of contour elements and absorption of small regions
Abstract
Texture image segmentation method based on estimation of density of contour elements and absorption of small regions, providing an increase in the accuracy in the selection of texture regions in the images to the specification of their boundaries is proposed.
About the Authors
H. M. Alzakki
Belarusian state university of informatics and radioelectronics
Belarus
Belarus
V. Yu. Tsviatkou
Belarusian state university of informatics and radioelectronics
Belarus
Belarus
References
1. Huang X. Li S.Z., Wang Y. Shape Localization Based on Statistical Method Using Extended Local Binary Pattern // Proceedings of the Third International Conference on Image and Graphics (ICIG’04). Stockholm, 2014. P. 1-4.
2. Hammouda K. Texture Segmentation Using Gabor Filters // In Visual Communications and Image Processing. Boston, 1993. P. 1- 8.
3. Florindo J.B., Bruno O.M. Fractal descriptors based on Fourier spectrum applied to texture analysis // Journal elsevier. 2012. Vol. 391, № 10. P. 4909-4922.
4. Dharampal F., Mutneja V. Methods of Image Edge Detection: A Review // IEEE transactions on pattern analysis and machine intelligence. 2015. Vol. 4, № 2. P. 183-191.
5. Kang Y. Texture Structure Classification and Depth Estimation using Multi-Scale Local Autocorrelation Features // Proceedings of the 2003 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’03). Chengdu, China, 2003. P. 1-4.
6. Costa A.F., Mamani G.H., Traina A.M. An Efficient Algorithm for Fractal Analysis of Textures // Multimedia and Expo, 2002. Proceedings. 2002. Vol. 2. P. 157-160.
7. D Computation of Gray Level Co-occurrence in Hyperspectral Image Cubes/ F. Tasi [et al.] // IEEE transactions on image processing. 2007. Vol. 24, № 44. P. 429-440.
8. Zhang Y., Brady M., Smith S. Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm // IEEE transactions on medical imaging. 2001. Vol. 20, № 1. P. 45-57.
9. Javed Y., Khan M.M. Image Texture Classification Using Textons // IEEE transactions on circuits and systems for video technology. 2011. Vol. 13, № 4. P. 358-363.
10. Tuceryan M., Jain A. Texture Segmentation Using Voronoi Polygons // 2011 IEEE International Symposium on Multimedia. USA, 2011. P. 257-262.
11. Arivazhagan S., Ganesan L. Texture classification using wavelet transform // Patter Recognition Letters. 2003. Vol. 24. P. 1513-1521.
12. Lee D-Ch., Shchenk T. // A Collection of Papers Presented At the XVII Congress of ISPRS. 1992. № 48. P. 75-80.
13. Альзаки Х.М., Цветков В.Ю. Текстурная сегментация изображений на основе геометрической классификации и оценки плотности контурных элементов // Телекоммуникации: сети и технологии, алгебраическое кодирование и безопасность данных: матер. междунар. науч.-техн. семинара. Минск, апрель-декабрь 2016 г. Ч. 2. С. 17-23.
14. Ertuğrul, Ö. Adaptive texture energy measure method // International Journal of Intelligent Information Systems. 2014. Vol. 3, № 2. P. 13-18.
15. Alzakki H.M., Tsviatkou V.Texture image segmentation based on classification of contour elements and logical addition of classes // Al-Sadeq International Conference on Multidisciplinary in IT and Communication Science and Applications (IEEE). 2016. P. 1-6.
16. Xu B., Wang J., Zhao G. Adaptive algorithm of edge detection based on mathematical morphology // Journal of Computer Applications. 2009. Vol. 29, № 4. P. 997-999.
17. Materka A., Strzelecki M. Texture Analysis Methods - A Review // Technical university of lodz, institute of electronics. 1998. № 11. P. 9-11.
Review
For citations:
Alzakki H.M., Tsviatkou V.Yu. Texture image segmentation based on estimation of density of contour elements and absorption of small regions. Doklady BGUIR. 2017;(5):46-53. (In Russ.)
Views: 627
Email this article 