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A Novel q-parameter Automation in Tsallis Entropy for Image Segmentation

by M Seetharama Prasad, P Radha Krishna
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 70 - Number 15
Year of Publication: 2013
Authors: M Seetharama Prasad, P Radha Krishna
10.5120/12042-8120

M Seetharama Prasad, P Radha Krishna . A Novel q-parameter Automation in Tsallis Entropy for Image Segmentation. International Journal of Computer Applications. 70, 15 ( May 2013), 48-53. DOI=10.5120/12042-8120

@article{ 10.5120/12042-8120,
author = { M Seetharama Prasad, P Radha Krishna },
title = { A Novel q-parameter Automation in Tsallis Entropy for Image Segmentation },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 70 },
number = { 15 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 48-53 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume70/number15/12042-8120/ },
doi = { 10.5120/12042-8120 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:32:59.051933+05:30
%A M Seetharama Prasad
%A P Radha Krishna
%T A Novel q-parameter Automation in Tsallis Entropy for Image Segmentation
%J International Journal of Computer Applications
%@ 0975-8887
%V 70
%N 15
%P 48-53
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image Thresholding is the necessary task in some image processing applications. The goal of any segmentation technique in image processing is to find out the object from the background in the given image. In this paper, q-parameter of the Tsallis entropy is analyzed in the existing and the proposed methods. Different techniques have been introduced to automate the q-parameter to obtain corresponding threshold value. After the comparison of all results by using misclassification error, the proposed one yields better results as demonstrated by statistical analysis.

References
  1. Pun, T. , 1981. Entropic thresholding: A new approach. Comput. Graphics Image Process. 16, 210–239.
  2. Kapur, J. N. , Sahoo, P. K. , Wong, A. K. C. , 1985. A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vision Graphics Image Process. 29, 273–285.
  3. Tsallis, 2004. Image thresholding using Tsallis entropy. Pattern Recognition Letters 25 (2004) 1059–1065
  4. Abutaleb, A. S. , 1989. Automatic thresholding of gray level pictures using two dimensional entropy. Comput. Vision Graphics Image Process. 47, 22–32.
  5. Y. Xiao, Z. G. Cao, and S. Zhong, "New entropic thresholding approach using gray-level spatial correlation histogram", Optics Engineering, 49, 127007, 2010
  6. M Seetharama Prasad, T Divakar, L S S Reddy, "Improved Entropic Thresholding based on GLSC histogram with varying similarity measure", International Journal of Computer Applications, vol. 24, June 2011.
  7. M. Sezgin and B. Sankur, "Survey over image thresholding techniques and quantitative performance evaluation," J. Electron. Imag. , vol. 13, no. 1, pp. 146–165, Jan. 2004.
Index Terms

Computer Science
Information Sciences

Keywords

Tsallis entropy Image Thresholding q-parameter

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