CFP last date
20 December 2024
Reseach Article

A New Method for Measuring Texture Regularity based on the Intensity of the Pixels in Grayscale Images

by Khoerul Anwar, Agus Harjoko, Suharto Suharto
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 137 - Number 7
Year of Publication: 2016
Authors: Khoerul Anwar, Agus Harjoko, Suharto Suharto
10.5120/ijca2016908805

Khoerul Anwar, Agus Harjoko, Suharto Suharto . A New Method for Measuring Texture Regularity based on the Intensity of the Pixels in Grayscale Images. International Journal of Computer Applications. 137, 7 ( March 2016), 1-5. DOI=10.5120/ijca2016908805

@article{ 10.5120/ijca2016908805,
author = { Khoerul Anwar, Agus Harjoko, Suharto Suharto },
title = { A New Method for Measuring Texture Regularity based on the Intensity of the Pixels in Grayscale Images },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 137 },
number = { 7 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume137/number7/24284-2016908805/ },
doi = { 10.5120/ijca2016908805 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:37:42.492422+05:30
%A Khoerul Anwar
%A Agus Harjoko
%A Suharto Suharto
%T A New Method for Measuring Texture Regularity based on the Intensity of the Pixels in Grayscale Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 137
%N 7
%P 1-5
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Texture regularity is one of the important visual characteristics. It can be used to determine differences in the surface between two objects. Several methods of measurement have been produced by previous researchers.However, in this paper the authors offer a new formula for calculating the regularity of texture features. Regularity is measured by the intensity of the pixels of grayscale images in a cross-diagonal position and the intensity of the pixels in an axis-ordinate position. The testing results of the new formula obtained good measurement accuracy. The linear test results using the human visual system worked. The observation of the human visual system suggested that a chessboard-image texture has a higher level of regularity than a bark-image texture. The results of measurement using the new formula showed that the value of the chessboard-texture regularity (0.2490) was greater (a higher level of regularity) than that of the bark-texture regularity (0.0078). General term cross-diagonal, image, textures

References
  1. M. Lukashevich, and R. Sadykhov, 2012, Texture Analysis: Algorithm for Texture Teatures Computation. IV International Conference “Problems of Cybernetics and Informatics” (PCI'2012), IEEE. Baku, Azerbaijan , 161-163.
  2. S. Varadarajan, and L. J. Karam, 2014, Adaptive Texture Synthesis Based On Perceived Texture Regularity. Sixth International Workshop on Quality of Multimedia Experience (QoMEX), IEEE,2014, 76-80.
  3. C. Zheng, D.W. Sun, and L. Zheng, 2007, A new region-primitive method for classification of colour meat image texture based on size, orientation, and contrast, Meat Science, 620–627.
  4. O. Gyuhwan, S. Lee, and S.Y. Shin, 1999, Fast determination of textural periodicity using distance matching function, Pattern Recognition Letters, 191-197.
  5. R.F. Wang, W.Z. Chen, S.Y. Zhang, Y. Zhang, and X.Z. Ye, 2008 ,Similarity-based denoising of point-sampled surfaces, Journal of Zhejiang University Science A , 807-815.
  6. M. Deswali, and N. Sharma, 2014, A Simplified Review on Fast HSV Image Color and Texture Detection and Image Conversion Algorithm, International Journal of Computer Science and Mobile Computing, 1216-1222.
  7. A.H. Rasal, and J. Rangole, 2015, A Review On Approaches To Texture Analysis Of Seabed Images. International Journal of Informative & Futuristic Research, 2347-1697.
  8. R. M Haralick, 1979, Statistical and structural approaches to texture. Proceedings of the IEEE 67, IEEE, 786–804.
  9. G.N. Srinivasan, and G. Shobha, 2008, Statistical Texture Analysis. Proceedings Of World Academy Of Science, Engineering And Technology Volume 36 WASET, 1264-1269.
  10. D. Chetverikov, and A. Hanbury, 2002, Finding defects in texture using regularity and local orientation, Pattern Recognition, pp 2165 – 2180.
  11. J.G. Leu, 2001, On Indexing The Periodicity of Image Texture. Image and Vision Computing, 987-1000.
  12. Q. Ye, W. Gao, and W. Wang, 2003, A New Texture- Insensitive Edge Detection Method, CICS-PCM, ingapure, IEEE, 768-772.
  13. D. Liu, Y. Xu, U. Quan, and P.L Callet, 2014, Reduced reference image quality assessment using regularity of phase congruency. Signal Processing, Image Communication, 844–855.
  14. W. Li, K. Mao, H. Zhang, and T. Chai, 2010, Selection of Gabor Filters for Improved Texture Feature Ekstraction. Proceedings of 2010 IEEE 17th International Conference on Image Processing, Hongkong: IEEE, 361-364.
Index Terms

Computer Science
Information Sciences

Keywords

regularity intensity of pixels diagonal ordinate-axis (DOA)