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Reseach Article

Application of Stochastic Gradient Kernel in Watershed Segmentation to be used in Noisy Environment

by Dibyendu Ghoshal, Pinaki Pratim Acharjya
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 74 - Number 13
Year of Publication: 2013
Authors: Dibyendu Ghoshal, Pinaki Pratim Acharjya
10.5120/12944-9997

Dibyendu Ghoshal, Pinaki Pratim Acharjya . Application of Stochastic Gradient Kernel in Watershed Segmentation to be used in Noisy Environment. International Journal of Computer Applications. 74, 13 ( July 2013), 9-15. DOI=10.5120/12944-9997

@article{ 10.5120/12944-9997,
author = { Dibyendu Ghoshal, Pinaki Pratim Acharjya },
title = { Application of Stochastic Gradient Kernel in Watershed Segmentation to be used in Noisy Environment },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 74 },
number = { 13 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 9-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume74/number13/12944-9997/ },
doi = { 10.5120/12944-9997 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:42:10.147071+05:30
%A Dibyendu Ghoshal
%A Pinaki Pratim Acharjya
%T Application of Stochastic Gradient Kernel in Watershed Segmentation to be used in Noisy Environment
%J International Journal of Computer Applications
%@ 0975-8887
%V 74
%N 13
%P 9-15
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Morphological image processing has been widely used for segmentation of binary, grayscale and color images. To extend the concept of segmentation, an ordering of the data is required. In this research paper, an effective methodology for digital color image segmentation has been publicized with stochastic gradients and watershed algorithm. The results demonstrate that combining of these two strategies has been very helpful for image segmentation and for computer vision, even in noisy images. The efficiency of the proposed methodology has been explained by experimental results and statistical measurements.

References
  1. L. Vincent, "Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms," IEEE Transactions on Image Processing, vol. 2, pp. 176-201, 1993.
  2. L. Vincent and P. Soille, "Watersheds in digital spaces: an efficient algorithm based on immersion simulations," IEEE transactions on pattern analysis and machine intelligence, vol. 13, pp. 583-598, 1991.
  3. S. Beucher and C. Lantuejoul, "Use of watersheds in contour detection," 1979.
  4. F. Meyer and S. Beucher, "Morphological segmentation," Journal of visual communication and image representation, vol. 1, pp. 21-46, 1990.
  5. F. Meyer, "Topographic distance and watershed lines," Signal Processing, vol. 38, pp. 113-125, 1994.
  6. L. Vincent, Algorithmes morphologiques a base de files d'attente et de lacets: extension aux graphes: Paris, 1990.
  7. A. N. Moga and M. Gabbouj, "Parallel image component labeling with watershed transformation," IEEE transactions on pattern analysis and machine intelligence, vol. 19, pp. 441-450, 1997.
  8. J. Roerdink and A. Meijster, "The watershed transform: Definitions, algorithms and parallelization strategies," Mathematical Morphology, vol. 41, pp. 187-S28, 2000.
  9. P. Soille, Morphological image analysis: principles and applications: Springer-Verlag New York, Inc. Secaucus, NJ, USA, 1999.
  10. J. Serra, Image analysis and mathematical morphology: Academic Press, Inc. Orlando, FL, USA, 1983.
  11. J. Serra and L. Vincent, "An overview of morphological filtering," Circuits, Systems, and Signal Processing, vol. 11, pp. 47-108, 1992.
  12. W. J. Niessen, K. L. Vincken, J. A. Weickert, and M. A. Viergever, "Nonlinear multiscale representations for image segmentation," Computer Vision and Image Understanding, vol. 66, pp. 233-245, 1997.
  13. M. Berouti, R. Schwartz, and J. Makhoul, "Enhancement of Speech Corrupted by Acoustic Noise," in IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 208–211, 1979. H. Digabel and C. Lantuejoul, "Iterative algorithms," Quantitative Analysis of Microstructures in Materials Sciences, Biology and Medicine, pp. 85-99, 1977.
  14. S. F. Boll, "Suppression of Acoustic Noise in Speech Using Spectral Subtraction," IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 27, no. 2, pp. 113–120, 1979.
  15. A. K. Jain, "Fundamentals of digital image processing", Second Edition, Prentice Hall, 2002.
  16. Canny, J. , A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 8(6):679–698, 1986.
  17. R. Deriche, Using Canny's criteria to derive a recursively implemented optimal edge detector, Int. J. Computer Vision, Vol. 1, pp. 167–187, April 1987.
  18. C. Gonzalez, Richard E. Woods, "Digital Image Processing", 2nd Edition, Addison Wesley Pub. Co, 2002.
  19. Greene, Thomas P. ; Wilking, Bruce A. ; Andre, Philippe; Young, Erick T. ; Lada, Charles J , "Further mid-infrared study of the rho Ophiuchi cloud young stellar population: Luminosities and masses of pre-main-sequence stars", The Astrophysical Journal, vol. 434, pp. 614–626, 1994.
  20. Andre, Philippe; Ward-Thompson, Derek; Barsony, Mary, "Submillimeter continuum observations of Rho Ophiuchi A – The candidate protostar VLA 1623 and prestellar clumps", The Astrophysical Journal, vol. 406, pp. 122–141, 1993.
Index Terms

Computer Science
Information Sciences

Keywords

Image segmentation image smoothing stochastic gradient watershed algorithm