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A Modified Watershed Algorithm for Stellar Image

by Dibyendu Ghoshal, Pinaki Pratim Acharjya
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 47 - Number 13
Year of Publication: 2012
Authors: Dibyendu Ghoshal, Pinaki Pratim Acharjya
10.5120/7251-0365

Dibyendu Ghoshal, Pinaki Pratim Acharjya . A Modified Watershed Algorithm for Stellar Image. International Journal of Computer Applications. 47, 13 ( June 2012), 38-43. DOI=10.5120/7251-0365

@article{ 10.5120/7251-0365,
author = { Dibyendu Ghoshal, Pinaki Pratim Acharjya },
title = { A Modified Watershed Algorithm for Stellar Image },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 47 },
number = { 13 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 38-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume47/number13/7251-0365/ },
doi = { 10.5120/7251-0365 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:41:47.894722+05:30
%A Dibyendu Ghoshal
%A Pinaki Pratim Acharjya
%T A Modified Watershed Algorithm for Stellar Image
%J International Journal of Computer Applications
%@ 0975-8887
%V 47
%N 13
%P 38-43
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A modified gray scale watershed image segmentation algorithm suitable for low contrast image has been proposed. Digital images acquired from far away stellar objects (like stars, planets, galaxies, comets etc. ) are prone to be severally affected by various types of noises and the contrast of these categories of images are generally found to be low. In present study, a preserving de noising method is presented by a contrast adjustment based on adaptive histogram equalization technique. The proposed method has been found to yield satisfactory segmentation of the stellar images. The entropy of the original and the segmented image is compared and the result confirms to the reality.

References
  1. P. Suetnes, P. Fua and A. J. Hanson, "Computational strategies for object recognition," ACM Computing Surveys, Vol. 24, pp. 05-61, 1992.
  2. R. Besl, R. Jain, "Three dimensional object recognition," ACM Computing Surveys, Vol. 17, pp. 75-145, 1985.
  3. K. Hohne, H. Fuchs, S. Pizer, "3D imaging in medicine: Algorithms, systems, Applications", Berlin, Germany, Springer –Verlag, 1990.
  4. M. Bomans, K. Hohne, U. Tiede and M. Riemer, "3D segmentation of MR images of the head for 3D display," IEEE Transactions on Medical imaging, Vol. 9, pp. 253-277, 1990.
  5. M. Kunt, M. Bernard and R. Leonardi, "Recent results in high compression image coding," IEEE Trans. on Circuits and Systems, Vol. 34, pp. 1306-1336, 1987.
  6. P. Willemin, T. Reed and M. Kunt, "Image sequence coding by split and merge," IEEE Trans. on Circuits and Systems, Vol. 34, pp. 1306-1306, 1987.
  7. F. D. Natale, G. Desoli, D. Glusto and G. Vernazza, "Polynomial approximation and vector quantization: A region based integration," IEEE transections on Communications, Vol. 43, 1995.
  8. K. Haris, "A hybrid algorithm for the segmentation of 2D and 3D images," Master's thesis, University of Crete, 1994.
  9. R. Harlick and L. Shapiro, "Image segmentation technique," CVGIP, Vol. 29, pp. 100-137, 1985.
  10. Vicent L. Solille P, Watershed in digital spaces, "An efficient algorithm based immersion simulations", IEEE Transections PAMI, pp. 538-598, 1991.
  11. Gonzalez & Woods, Digital Image Processing, 3rd edition, Prentice Hall India, 2008.
  12. K. Haris,"Hybrid image segmentation using watersheds and fast region merging," IEEE Trans Image Processing, 7(12), pp. 1684-1699, 1998.
  13. Jos B. T. M. Roerdink and Arnold Meijster. : "The watershed transform: Definitions, algorithms and parallelization strategies," Fundamental Informatics, Vol. 41, pp. 187-228, 2001.
  14. Hua LI et al. , "An improved image segmentation approach based on level set and mathematical morphology," GREYC-ISMRA, CNRS 6072, 6 Bd Maréchal Juin, 14050 Caen, France.
  15. Mahua Bhattacharya, Arpita Das, "A Study on Seeded Region Based Improved Watershed Transformation for Brain Tumor segmentation," Indian Institute of Information Technology & Management, Gwalior Morena Link Road, Gwalior-474010.
  16. D. Marr and E. Hildreth, "A theory of edge detection," Proc. R. Soc. London, no 207, pp. 187-217.
  17. Chen Pan, Congxun Zheng, Hao-Jun Wang "Robust Color Image Segmentation Based On Mean Shift And Marker-controlled Watershed Algorithm", Second International Conference on Machine Learning and Cybernetics, Wan, ,pp 2752-2756, 2003.
  18. F. Meyer, S. Beucher, "Morphological Segmentation", Journal of Visual Communication and Image Representation, 1, pp. 21-46, 1990.
  19. N. Pal and S. pal, "A review on image segmentation techniques," Pattern Recognition, Vol. 26, pp. 1277-1294, 1994.
  20. K. Haris, G. Tziritas and S. Orphanoudakis, "Smoothing 2D or 3D images using local classification," in Proc. EUSIPCO, Edinburg, U. K. , 19994.
Index Terms

Computer Science
Information Sciences

Keywords

Stellar Image Segmentation Watersheds

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