CFP last date
15 April 2024
Reseach Article

Color image segmentation using wavelet

by Samer kais Jameel, Ramesh R. Manza
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 6
Year of Publication: 2012
Authors: Samer kais Jameel, Ramesh R. Manza
10.5120/10.5120/ijais12-450134

Samer kais Jameel, Ramesh R. Manza . Color image segmentation using wavelet. International Journal of Applied Information Systems. 1, 6 ( February 2012), 1-4. DOI=10.5120/10.5120/ijais12-450134

@article{ 10.5120/10.5120/ijais12-450134,
author = { Samer kais Jameel, Ramesh R. Manza },
title = { Color image segmentation using wavelet },
journal = { International Journal of Applied Information Systems },
issue_date = { February 2012 },
volume = { 1 },
number = { 6 },
month = { February },
year = { 2012 },
issn = { 2249-0868 },
pages = { 1-4 },
numpages = {9},
url = { https://www.ijais.org/archives/volume1/number6/95-0134/ },
doi = { 10.5120/10.5120/ijais12-450134 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T10:41:33.170815+05:30
%A Samer kais Jameel
%A Ramesh R. Manza
%T Color image segmentation using wavelet
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 1
%N 6
%P 1-4
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we discussed color image segmentation by extract the optimal features with which to discriminate between regions. Many real or texture images are made up of smooth regions and are best segmented using features in different areas. Schemas that select the optimal features for each pixel using wavelet analysis are proposed, leading to robust segmentation algorithm. Using two dimensions wavelet transforms to decompose the image into subbands channels and made up the of smooth image and convert the image into NTSC color space enables us to quantify the visual differences in the image, and then applies a clustering technique to partition the image into a set of “homogeneous” regions is also proposed.

References
  1. L. P. Clarke et al., “MRI segmentation: Methods and applications,” Magn. Resonance Imaging, Vol. 13, no. 3, pp. 343-368, 1995.
  2. M. Kunt, A. Ikonomopoulos, and M. Kocher, “Second-generation image-coding techniques,” proc. IEEE, Vol.73, no. 4, pp. 549-574, Apr. 1985.
  3. Rafael C. Gonzalez & Richard E. Woods, Addison- Wesley, “Digital Image Processing”, 2002.
  4. A. K. Jain and F. Farrokhnia, “Unsupervised texture segmentation using Gabor filters,” patt. Reogn., Vol. 24, no. 12, pp. 1167-1186, Dec. 1991.
  5. Y. Hu and T. J. Dennis, “texture image segmentations by context enhanced clustering,” Proc. Inst. Elec. Eng.-vision, Image, Signal processing, Vol. 114, no. 6, pp. 413-421, Dec. 1995.
  6. I. Daubechies. Ten Lectures on Wavelets. Soc. Ind. Applied Math, Philadelphia, 1992.
  7. S. G. Mallat, “Multifrequency channel decomposition of images and wavelet models,” IEEE Trans. Acoustics, speech, signal processing, Vol. 37, no. 12, pp. 2091-2110, Dec. 1989.
  8. T. Chang and C.-C. J. Kao, “Texture analysis and classification with tree-structured wavelet transform,” IEEE Trans. Image Processing, Vol. 2, no. 4, pp. 429-441, Oct. 1993.
  9. subhasis Saha and Rao Vemuri “ Analysis based adaptive wavelet filter selection in Lossy image coding schemes”, ISCAS-2000-IEEE international Symposium on Circuits and system May, Geneva, Switzerland, 2000.
  10. Bryan E. Usevitch “ A Tutorial on Modern Lossy wavelet image compression: Foundations of JPEG 2000” ,IEEE signal processing Magazine, September,2001.
Index Terms

Computer Science
Information Sciences

Keywords

Segmentation color image wavelet transform k-means clustering