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Reseach Article

Texture Analysis using Rough Texton

by Phani Kumar Talluri, Sudhakar Putheti
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 145 - Number 14
Year of Publication: 2016
Authors: Phani Kumar Talluri, Sudhakar Putheti
10.5120/ijca2016910856

Phani Kumar Talluri, Sudhakar Putheti . Texture Analysis using Rough Texton. International Journal of Computer Applications. 145, 14 ( Jul 2016), 29-33. DOI=10.5120/ijca2016910856

@article{ 10.5120/ijca2016910856,
author = { Phani Kumar Talluri, Sudhakar Putheti },
title = { Texture Analysis using Rough Texton },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 145 },
number = { 14 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 29-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume145/number14/25348-2016910856/ },
doi = { 10.5120/ijca2016910856 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:48:51.688616+05:30
%A Phani Kumar Talluri
%A Sudhakar Putheti
%T Texture Analysis using Rough Texton
%J International Journal of Computer Applications
%@ 0975-8887
%V 145
%N 14
%P 29-33
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The present paper derived a new texture classification tactic using textons and rough sets. Texton is a statistical approach used to analyze the texture of an image. Textures will be developed only if the side elements lie within the contiguity. Texton Image has the discrimination power of color, texture and shape features. In the proposed tactic texton using rough texture spectrum and color features are calculated using HSV color space. Rough texture spectrum covers the entire range that is difficult unless otherwise. Rough set theory is better handles vagueness. The designed mechanism is estimating interesting as it enumerates contrasting visage with confined number of preferred components. The experimental results suggest the adequacy of the present mechanism over the various other approaches.

References
  1. Texture scrutinity. In: Chen, C.H; Pau, L.F. & Wang, P.S.P., (eds). The handbook of pattern recognition and computer vision. 2nd ed. World Scientific Publishing Co., ISBN 9-810-23071-0, Singapore.
  2. Texture scrutinity in machine vision, World Scientific Publishing, 981-02-4373-1, Singapore.
  3. Handbook of texture scrutinity, Imperial College Press, 1-84816-115-8, UK.
  4. Doi K: Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Comput Med Imaging Graph 31(4–5):198–211, 2007
  5. Zaidi H, Vees H, Wissmeyer M: Molecular PET/CT imaging guided radiation therapy treatment planning. Acad Radiol 16(9):1108–33, 2009
  6. Marcus C, Ladam-Marcus V, Cucu C, Bouché O, Lucas L, Hoeffel C: Imaging techniques to evaluate the response to treatment in oncology: Current standards and perspectives. Crit Rev Oncol/ Hematol 72(3):217–38, 2009
  7. M. Tuceryan, A.K. Jain, Texture scrutinity, in: C.H. Chen, L.F. Pau, P.S.P. Wang (Eds.), Handbook of Pattern Recognition and Computer Vision, 1993.
  8. A.R. Rao, A Taxonomy for Texture Description and Identi0cation, Springer, Berlin, 1990.
  9. F. Tomita, S. Tsuji, Computer Scrutinity of Visual Textures, Kluwer Academic, Hingham, MA, 1990.
  10. T.N. Tan, Geometric transform invariant texture scrutinity.
  11. R.M. Haralick, Statistical and structural approaches to Texture, Proc.
  12. T.N. Tan, Texture segmentation approaches: a brief review, Proceedings of CIE and IEEE International Conference on Neural Networks and Signal Processing, Guangzhou, China, November 1993.
  13. T.R. Reed, J.M.H. Du Buf, A review of recent texture segmentation and feature extraction techniques.
  14. L. Van Gool, P. Dewaele, A. Oosterlinck, Survey: texture scrutinity anno 1983.
  15. R. Chellappa, R.L. Kashyap, B.S. Manjunath, Portrait based texture segmentation and classi0cation, in: C.H. Chen, L.F. Pau, P.S.P. Wang (Eds.), Handbook of Pattern Recognition and Computer Vision, 1993.
  16. B. Julesz, T. Caelli, On the limits of fourier decompositions in visual texture perception.
  17. B. Julesz, Experiments in the visual perception of texture, Sci. Am.
  18. Ojala, T. and M Pietikäinen: Texture Classification. Machine Vision and Media Processing Unit, University of Oulu, Finland. Available at http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/OJALA1/ texclas.htm. January, 2004.
  19. F. Cohen et al., Classi0cation of rotated and scaled texture images using Gaussian Markov random 0eld portraits, IEEE Trans.
  20. K. Sivakumar, Morphologically constrained GRFs: applications to texture synthesis and scrutinity, IEEE Trans.
  21. Fang Liu, R.W. Picard, Periodicity, directionality, and randomness: wold features of image and portraiting and retrieval, IEEE Trans. Pattern Anal. Mach. Intell.
  22. Fang Liu, R.W. Picard, Periodicity, directionality, and randomness: wold features for perceptional pattern recognition, Proceedings of the International Conference on Pattern Recognition, Vol. 11, Jerusalem, October 1994.
  23. Aboul Ella Hassanien, Ajith Abraham, James F. Peters, Gerald Schaefer, Christopher Henry, “Rough Sets and Near Sets in Medical Imaging: A Review”, IEEE trans. on information technology in biomedicine, 2009.
  24. S Putheti, SR Edara, SA Edara, “CBIR using Texels of colour Fuzzy Textons” , Hybrid Intelligent Systems (HIS), 2012 12th International Conference on, 461-467, 2012.
  25. E. Mirkes, KNN and Potential Energy (Applet). University of Leicester. Available: http: //www.math.le.ac.uk/people/ag153/homepage/KNN/ KNN3.html, 2011.
  26. Simoncelli E.P., Freeman W.T, “The steerable pyramid: A flexible architecture for multi-scale derivative computation,” Proceedings of IEEE ICIP 13, pp:891-906,1995.
  27. Arivazhagan S., Ganesan L., Priyal S.P, “Texture classification using gabor wavelets based rotation invariant features,” Pattern Recognition Letters, vol.27, pp:1976-1982, 2006.
Index Terms

Computer Science
Information Sciences

Keywords

Texton Rough Sets Rough Texture Unit Texture Classification Texture Unit