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Reseach Article

Morphological Shape features for Classification of Textures based on Fuzzy Texture Element

by M. Rama Bai, V.Venkata Krishna, J.Sasi Kiran
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 19 - Number 7
Year of Publication: 2011
Authors: M. Rama Bai, V.Venkata Krishna, J.Sasi Kiran
10.5120/2373-3127

M. Rama Bai, V.Venkata Krishna, J.Sasi Kiran . Morphological Shape features for Classification of Textures based on Fuzzy Texture Element. International Journal of Computer Applications. 19, 7 ( April 2011), 22-30. DOI=10.5120/2373-3127

@article{ 10.5120/2373-3127,
author = { M. Rama Bai, V.Venkata Krishna, J.Sasi Kiran },
title = { Morphological Shape features for Classification of Textures based on Fuzzy Texture Element },
journal = { International Journal of Computer Applications },
issue_date = { April 2011 },
volume = { 19 },
number = { 7 },
month = { April },
year = { 2011 },
issn = { 0975-8887 },
pages = { 22-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume19/number7/2373-3127/ },
doi = { 10.5120/2373-3127 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:06:22.441424+05:30
%A M. Rama Bai
%A V.Venkata Krishna
%A J.Sasi Kiran
%T Morphological Shape features for Classification of Textures based on Fuzzy Texture Element
%J International Journal of Computer Applications
%@ 0975-8887
%V 19
%N 7
%P 22-30
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Texture is an important spatial feature useful for identifying objects or regions of interest in an image. The present paper derives a new set of texture features, which are morphological shape components derived from the fuzzy texture elements of a 3x3 mask. The proposed fuzzy texture element patterns (FTP’s) extract textural information of an image with a more complete respect of texture characteristics in all the eight directions instead of only one displacement vector. The proposed FTP’s retains discriminating power of texture elements. In the present paper, five simple morphological shape components are evaluated on each of the derived FTP. The experimental results on the five groups of texture images clearly show the efficacy and simplicity of the present method.

References
  1. Caselles, V., Kimmel, R. and Sapiro, G., ” Geodesic active contours, Int. J. Comput.Vis. ”,Vol.22, pp. 61-79, 1997.
  2. Eswara Reddy,B. and Vijaya Kumar,V.,”Texture Discrimination by Morphological Patterns after Edge Operator”, International Conference (ICORG), R&D Cell, JNT University, Hyderabad, 2006.
  3. Liu, X., Skidmore, A.K. and Oosten, H.V,”Integration of classification methods for improvement of land-cover map accuracy”, ISPRS Journal of Photogrammetry and Remote Sensing, Vol.56, pp. 257-268, 2002.
  4. Meisel, W.S.,”Computer Oriented Approaches to Pattern Recognition, Academic Press”, New York, 1972.
  5. Tsai, D.M. and Wang, H.J.,” Segmenting focused objects in complex visual images, Pattern Recognition”, Letters. vol. 19, pp. 929 - 940, 1998.
  6. Richards, W. and Polit, A.,” Texture Matching, Cybernetic”, Vol 16, pp.155-162, 1974.
  7. Chen, G. and Yang, Y.H.H.,” Edge Detection by Regularized Cubic B- spline Fitting”, IEEE Transactions on Systems, Man, and Cybernetics, Vol.25, pp.635-642, 1995.
  8. Lira, J. and Maletti, G,” A supervised contextual classifier based on a region-growth algorithm”, Computers and Geosciences, Vol.28, pp. 951-959, 2002.
  9. Jensen, J.R,” Introduction to Digital Image Processing: A Remote Sensing Perspective”, 2nd Edition. Princeton Hall, 1996.
  10. Tamura, H., Mori, S. and Yamawaki, Y.,” Textural Features Corresponding to Visual Perception”, IEEE Trans. on Systems, Man and Cybernetics, Vol.8, pp.460-473, 1978.
  11. Bhanu, B. and Peng, J.,” Adaptive integrated image recognition and segmentation”, IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., Vol.30, pp. 427–441, 2000.
  12. Barandela, R. and Juarez, M.,” Supervised classification of remotely sensed data with ongoing learning capability”, International Journal of Remote Sensing, Vol.23, pp. 4965–4970, 2002.
  13. Morrone, M.C. and Owens, R.A.,” Feature detection from local energy”, Patterns Recognition Letters, Vol.6, pp. 303-313, 1987.
  14. Sayre, K.M.,” Recognition, A Study in the Philosophy of Artificial Intelligence”, University of Notre Dame Press, Ohio, 1965.
  15. Heitger, F.,” Feature Detection using Suppression and Enhancement. Technical Report TR-163”, Communication Technology Laboratory, Swiss Federal Institute of Technology, 1995.
  16. Kashyap, R.L. and Khotanzad, A.,” A Model based Method for Rotation Invariant Texture Classification”, IEEE Trans. Pattern Anal. Machine Intell, Vol. 8, pp.472-481, 1986.
  17. Peder Klith Bocher and Keith R. McCloy,” The Fundamentals of Average Local Variance: Detecting Regular Patterns”, IEEE Trans. on Image Processing, Vol. 15, pp.300-310, 2006.
  18. Penaloza, M.A. and Welch, R.M,” Feature selection for classification of polar regions using a fuzzy expert system”, Remote Sensing of Environment, Vol.58, pp. 81-100, 1996.
  19. Biehl, L. and Landgrebe, D.,” MultiSpec—a tool for multispectral-hyper spectral image data analysis. Computers and Geosciences”, Vol.28, pp. 1153–1159, 2002.
  20. Binaghi, E., Madella, P., Montesano, M.G. and Rampini, A,” Fuzzy contextual classification of multisource remote sensing images”, IEEE Transactions on Geoscience and Remote Sensing, Vol.35, pp. 326–339, 1997.
  21. Dong-Chen He , Li Wang “Texture Feature Extraction from Texture Spectrum”, IEEE Trans, Vol.8,p.p 1987-19901990
  22. D.C.He and L.Wang, “Texture unit, texture spectrum andtexture analysis”, in Proc of IGARSS’89,Vancouver,Canada, 1989,Vol.5,pp.2769-2771.
  23. V.Venkata Krishna, M.Rama Bai and V. Vijaya Kumar, “Extraction of shape components for classification of textures based on texture elements,” IJCSNS, Vol.11,No.2, p.p 114-120, 2011.
  24. V. Vijaya Kumar, B. Eswara Reddy and U.S.N.Raju ,“A measure of patterns trends on various types of preprocessed images,” IJCSNS, Vol.7 No.8, p.p. 253-257, 2007.
  25. V. Vijaya Kumar, B. Eswara Reddy , U.S.N.Raju and K. Chandra Sekharan “An Innovative Technique of Texture Classification and Comparison Based on Long Linear Patterns”, Journal of Computer Science 3 (8):633-638,2007.
  26. B. Eswara Reddy, A. Nagaraja Rao, A. Suresh and V.Vijaya Kumar “Texture Classification by simple patterns on edge direction movements”, IJCSNS, Vol.7 No.11, p.p. 220-225, 2007.
  27. Dougherty E. and J. Astola,” An Introduction to Nonlinear Image Processing”, vol. TT 16, SPIE Optical Engineering Press, Washington, 1994.
  28. Gasteratos,” Mathematical morphology operations and structuring elements”, In CVonline: On-Line Compendium of Computer Vision [Online]. R. Fisher(ed). Available: http://www.dai.ed.ac.uk/ CVonline/transf.htm, Dec. 2001, Section: Image Transformations and Filters.
  29. Maragos P., and Schafer R.W.,” Morphological filters-part II, Their relations to median, order statistics, and stack filters”, IEEE Trans., 1987, ASSP-35,(8), pp. 1170-1184.
  30. Maragos P., and Schafer R.W.,” Morphological skeleton representation and coding of binary images”, IEEE Trans. 1986, ASSP-34, pp. 1228-1244.
  31. Schonfeld D., and Goutsias J.,”Optimal morphological pattern restoration from noisy binary images”, IEEE Trans. Pattern Anal. Mack Intell., 1991, 13, pp. 14-29.
Index Terms

Computer Science
Information Sciences

Keywords

Morphological Shape components Textural information Classification Fuzzy texture element