bsuirДоклады БГУИРDoklady BGUIR1729-76482708-0382БГУИРbsuir-868Research ArticleСтатьиТЕКСТУРНАЯ СЕГМЕНТАЦИЯ ИЗОБРАЖЕНИЙ НА ОСНОВЕ ГЕОМЕТРИЧЕСКОЙ КЛАССИФИКАЦИИ И ОЦЕНКИ ПЛОТНОСТИ КОНТУРНЫХ ЭЛЕМЕНТОВTexture image segmentation based on geometric classification and assessment density of contour elementsАльзакиХ. М.AlzakkiH. M.noemail@neicon.ruЦветковВ. Ю.TsviatkouV. Yu.vtsvet@bsuir.byБелорусский государственный университет информатики и радиоэлектроникиBelarusian state university of informatics and radioelectronics201703062019039399Copyright © Альзаки Х.М., Цветков В.Ю., 20192019Альзаки Х.М., Цветков В.Ю.Alzakki H.M., Tsviatkou V.Y.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://doklady.bsuir.by/jour/article/view/868

Предложен метод текстурной сегментации изображений на основе геометрической классификации и оценки плотности контурных элементов, обеспечивающий в сравнении с методом на основе энергетических карт уменьшение ошибки локализации текстурных областей за счет учета геометрических характеристик образующих их элементов.

A method of texture image segmentation based on geometric classification and assessment density of contour elements. The proposed method in comparison with the method based on energy maps, providing a reduction in the error of localization of textural regions by taking into account the geometric characteristics of the elements.

текстурная сегментация изображенийклассификация контурных элементовоценка плотности контурных элементовtextural image segmentationclassification contour elementsassessment density of contour elementsReferencesLee D-Ch., Shchenk T. Image segmentation of texture measurement // A Collection of Papers Presented At the XVII Congress of ISPRS. 1992. № 48. P. 75-80.Lee D-Ch., Shchenk T. Image segmentation of texture measurement // A Collection of Papers Presented At the XVII Congress of ISPRS. 1992. № 48. P. 75-80.Ertuğrul Ö. Adaptive texture energy measure method // International Journal of Intelligent Information Systems. 2014. Vol. 3. № 2. P. 13-18.Ertuğrul Ö. Adaptive texture energy measure method // International Journal of Intelligent Information Systems. 2014. Vol. 3. № 2. P. 13-18.Shrivakshan G.T., Chandrasekar C.A. Comparison of various edge detection techniques used in image processing // IJCSI International Journal of Computer Science Issues. 2012. Vol. 9. № 1. P. 269-276.Shrivakshan G.T., Chandrasekar C.A. Comparison of various edge detection techniques used in image processing // IJCSI International Journal of Computer Science Issues. 2012. Vol. 9. № 1. P. 269-276.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.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.Materka A., Strzelecki M. Texture Analysis Methods - A Review // Technical university of lodz, institute of electronics. 1998. № 11. P. 9-11.Materka A., Strzelecki M. Texture Analysis Methods - A Review // Technical university of lodz, institute of electronics. 1998. № 11. P. 9-11.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.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.

The authors declare that there are no conflicts of interest present.