CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Image Segmentation using Multiresolution Texture Gradient and Watershed Algorithm

by Roshni V.S, Raju G
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 22 - Number 6
Year of Publication: 2011
Authors: Roshni V.S, Raju G
10.5120/2588-3579

Roshni V.S, Raju G . Image Segmentation using Multiresolution Texture Gradient and Watershed Algorithm. International Journal of Computer Applications. 22, 6 ( May 2011), 21-28. DOI=10.5120/2588-3579

@article{ 10.5120/2588-3579,
author = { Roshni V.S, Raju G },
title = { Image Segmentation using Multiresolution Texture Gradient and Watershed Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { May 2011 },
volume = { 22 },
number = { 6 },
month = { May },
year = { 2011 },
issn = { 0975-8887 },
pages = { 21-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume22/number6/2588-3579/ },
doi = { 10.5120/2588-3579 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:08:41.498531+05:30
%A Roshni V.S
%A Raju G
%T Image Segmentation using Multiresolution Texture Gradient and Watershed Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 22
%N 6
%P 21-28
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The wavelet transform as an important multi resolution analysis tool has already been commonly applied to texture analysis and classification. Mathematical morphology is very attractive for automatic image segmentation because it efficiently deals with geometrical descriptions such as size, area, shape, or connectivity that can be considered as segmentation-oriented features. This paper presents an image-segmentation system based on some well-known strategies implemented in a different methodology. The segmentation process is divided into three basic steps, namely: texture gradient extraction, marker extraction, and boundary decision. Texture information and its gradient are extracted using the decimated form of a complex wavelet packet transform. A novel marker location algorithm is subsequently used to locate significant homogeneous textured or non textured regions. The goal of boundary decision is to precisely locate the boundary of regions detected by the marker extraction. This decision is based on a region-growing algorithm which is a modified flooding based watershed algorithm.

References
  1. R.W. Conners and C.A. Harlow. 1980, “A theoretical comparison of texture algorithms,” IEEE transactions on Pattern Analysis and Machine Intelligence, vol. 2 ,pp. 204-222
  2. T.R. Reed, J.M.H. Du Buf,1993, “A review of recent texture segmentation, feature extraction techniques,” CVGIP Image Understanding ,vol. 7 ,pp. 359-372.
  3. N.R. Pal, S.K. Pal 1993, “A review on image segmentation techniques, Pattern Recognition ,”vol. 26, pp. 1277-1294,.
  4. J. Zhang and T. Tan 2002, “Brief review of invariant texture analysis methods,” Pattern Recognit., vol. 35, pp. 735– 747,.
  5. C.-C. Chen and C.-C. Chen 1999, “Filtering methods for texture discrimination,” Pattern Recognit. Lett., vol. 20, pp. 783–790,.
  6. P.C. Chen, T. Pavlidis ,1979, “Segmentation by texture using a co-occurrence matrix and a split-and-merge algorithm,” Comput. Graphics Image Processing, vol. 10, pp. 172-182.
  7. P. C. Chen and T. Pavlidis 1983., “Segmentation by texture using correlation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 5, no. 1, pp. 64–69, Jan.
  8. R. L. Kashyap and R. Chellappa 1983., “Estimation and choice of neighbors in spatial-interaction models of images,” IEEE Trans. Inf. Theory, vol. 29, no. 1, pp. 60–72, Jan.
  9. M. Unser 1986, “Local linear transforms for texture measurements, ” Signal Process., vol. 11, pp. 61–79,.
  10. M.Unser 1995., “Texture classification and segmentation using wavelet frames,” IEEE Trans. Image Process., vol. 4, no. 11, pp. 1549–1560,
  11. T. Chang and C.-C. J. Kuo 1992,, “A wavelet transform approach to texture analysis,” in Proc. IEEE ICASSP, vol. 4, no. 23–26, pp.661–664.
  12. T. Chang and C.-C. J. Kuo 1992., “Tree-structured wavelet transform for textured image segmentation,” Proc. SPIE, vol. 1770, pp. 394–405,
  13. T. Chang and C.-C. J. Kuo 1993., “Texture analysis and classification with tree-structured wavelet transform,” IEEE Trans. Image Process., vol. 2, no. 4, pp. 429–441,
  14. W. Y. Ma and B. S. Manjunath 1995, “A comparison of wavelet transform features for texture image annotation,” Proc. IEEE Int. Conf. Image Processing, vol. 2, no. 23–26, pp. 256–259,.
  15. W. Y. Ma and B. S. Manjunath 1996., “Texture features and learning similarity,” Proc. IEEE CVPR, no. 18–20, 425–430,
  16. B. S. Manjunath and W. Y. Ma ,1996., “Texture feature for browsing and retrieval of image data,” IEEE Trans.Pattern Anal. Mach. Intell., vol. 18, no. 8, pp. 837–842
  17. A. K. Jain and F. Farrokhnia 1991, “Unsupervised texture segmentation using Gabor filters,” Pattern Recognition vol. 24, no. 12, pp. 1167-1186,.
  18. W. E. Higgins, T. P. Weldon, and D. F. Dunn 1996., “Gabor filter design for multiple texture segmentation,” Opt. Eng., vol. 35, no. 10, pp. 2852–2863,
  19. N.W. Campbell and B. T. Thomas 1997,, “Automatic selection of Gabor filters for pixel classification,” in Proc. 6th Int. Conf. Image Processing and its Applications, pp. 761–765.
  20. N. Kingsbury 1998.,”The dual-tree complex wavelet transform: a new technique for shift invariance and directional filters,” in IEEE Digital Signal ProcessingWorkshop, vol. 86,
  21. N. Kingsbury 2000,”The dual-tree complex wavelet transform with improved orthogonality and symmetry properties,” in Proceedings IEEE Conference on Image Processing, Vancouver,
  22. “Segmentation tools in mathematical morphology,” Proc. SPIE, vol. 1350, pp. 70–84, 1990.
  23. P. Jackway 1996., “Gradient watersheds in morphological scale- space,” IEEE Trans. Image Processing, vol. 5, pp. 913–921,
  24. A.Bieniek, A.Moga ,2000., “An efficient watershed algorithm based on connected components”, Pattern Recognition, vol. 33 ,pp. 907–916
  25. H. Sun, J. Yang, M. Ren 2005, “A fast watershed algorithm based on chain code and its application in image segmentation”, Pattern Recognition Letters 26 ,pp. 1266–1274,.
  26. Paul R. Hill, C. Nishan Canagarajah, David R. Bull 2003 ,” Image segmentation using a texture gradient based watershed transform”, IEEE Trans on Image Processing, vol 12, No. 12,pp. 1618 – 1633,
  27. Robert J. O’Callaghan and David R. Bull 2005., “Combined Morphological - Spectral Unsupervised Image segmentation,” IEEE Transactions on Image Processing, vol. 14, no. 1, pp. 49-62,
  28. Roshni VS, Dr Raju G 2010, “Segmentation of image using texture gradient, marker and scan based watershed algorithm” , The 2nd International Conference on Digital Image Processing, Singapore,
  29. R.R. Coifman and M.V. Wickerhauser 1992, “Entropybasedalgorithms for best basis selection,” IEEE Trans. Inf. Theory, vol. 38, no. 2, pp. 713-718.
Index Terms

Computer Science
Information Sciences

Keywords

Image Segmentation Texture Gradient Watershed Algorithm

Powered by PhDFocusTM