Статья 'О выборе уровней серого в задаче текстурной сегментации изображений на основе матриц яркостной зависимости ' - журнал 'Кибернетика и программирование' - NotaBene.ru
Journal Menu
> Issues > Rubrics > About journal > Authors > About the Journal > Requirements for publication > Council of Editors > Peer-review process > Policy of publication. Aims & Scope. > Article retraction > Ethics > Online First Pre-Publication > Copyright & Licensing Policy > Digital archiving policy > Open Access Policy > Open access publishing costs > Article Identification Policy > Plagiarism check policy
Journals in science databases
About the Journal

Публикация за 72 часа - теперь это реальность!
При необходимости издательство предоставляет авторам услугу сверхсрочной полноценной публикации. Уже через 72 часа статья появляется в числе опубликованных на сайте издательства с DOI и номерами страниц.
По первому требованию предоставляем все подтверждающие публикацию документы!
MAIN PAGE > Back to contents
Cybernetics and programming

On the choice of gray levels in the problem of texture segmentation of images based on the luminance dependence matrices

Tymchuk Andrey Igorevich

graduate student, Kuban State Technological University

350080 181, kv.103, Russia, Krasnodarskii Krai oblast', g. Krasnodar, ul. Sormovskaya, 181, kv. 103

Другие публикации этого автора




Review date:


Publish date:


Abstract: The object of research is the method of textural image segmentation based on the construction and use of luminance dependence matrices. The subject of the study is the influence of the number of reference gray levels used for computations on the speed and quality of image segmentation. Particular attention is paid to the process of constructing brightness dependency matrices, as well as texture attributes, which are calculated on their basis. In the article the author conclude that the choice of the size of the brightness dependence matrix (the number of gray levels) is certainly an important aspect in solving the image segmentation problem, since it directly affects the segmentation quality and program speed. The research methodology is based on the analysis of the method of textural image segmentation based on luminance dependence matrices, testing the segmentation algorithm based on this approach for various input parameters and analysis of results. The main conclusion of the study is the conclusion about the selection of the best number of gray levels in solving the segmentation problem in terms of productivity and segmentation quality. This conclusion is made on the basis of analysis of the results of the program, which implements the algorithm of image segmentation. The analysis was carried out with respect to the time spent on constructing the matrices and calculating the texture attributes, and also with respect to the value of each texture feature separately. The novelty of the study is to determine the number of reference levels relative to the speed and quality of segmentation.

Keywords: texture characteristic, textural feature, texture analysis, texture segmentation, segment, texture, image processing, Gray Level Co-occurrence Matrix, gray level, pixel
This article written in Russian. You can find full text of article in Russian here .

Haralick R. M., Shanmugan K., Dinstein I. Textural Features for Image Classification // IEEE Trans. Systems, Man and Cybernetics. 1973, vol. 3, no. 6, pp. 610-621.
Haralick R. M. Statistical and Structural Approaches to Texture //Proceedings of the IEEE. 1979, vol. 67, no. 5, pp. 786-804.
Sebastian V. B., Unnikrishnan A., Balakrishnan K. Grey Level Co-occurrence Matrices: Generalisation and Some New Features // International Journal of Computer Science, Engineering and Information Technology. 2012, vol. 2, no. 2, pp. 151-157.
Tymchuk, A. I. Analiz aktual'nykh metodov segmentatsii tekstur na aerofotosnimkakh // Avtomatizirovannye informatsionnye i elektroenergeticheskie sistemy: Materialy V Mezhdunarodnoi nauchno-prakticheskoi konferentsii (7–8 dekabrya 2017 goda).
Ulaby F. T., Kouyate F., Brisco B. Textural Information in SAR Images // IEEE Trans. Geoscience and Remote Sensing. 1986, vol. GE-24, no. 2, pp. 235-245.
Zhao Q., Shi CZ., Luo LP., Role of the texture features of images in the diagnosis of solitary pulmonary nodules in different sizes // Chinese Journal of Cancer Research. 2014, vol. 26, no. 4, pp. 451-458.
Kovaleva, I. L. Teksturnye priznaki izobrazhenii // Minsk: Izd-vo BNTU, 2010. – 24 c.
Albregtsen F. Statistical Texture Measures Computed from Gray Level Coocurrence Matrices // Image Processing Laboratory, Department of Informatics, University of Oslo. 2008, 14 p.
Gray-Level Co-occurrence Matrices (GLCMs): [Elektronnyi resurs]. – Rezhim dostupa: http://www.code.ucsd.edu/pcosman/glcm.pdf. – (Data obrashcheniya: 18.03.2018).
Properties of gray-level co-occurrence matrix: [Elektronnyi resurs]. – Rezhim dostupa: https://www.mathworks.com/help/images/ref/graycoprops.html. – (Data obrashcheniya: 21.03.2018).
Calculating texture measures from the GLCM: [Elektronnyi resurs]. – Rezhim dostupa: http://www.ucalgary.ca/mhallbey/measures. – (Data obrashcheniya: 19.03.2018).
Link to this article

You can simply select and copy link from below text field.

Other our sites:
Official Website of NOTA BENE / Aurora Group s.r.o.
"History Illustrated" Website