Improved Co-occurrence Matrix as a Feature Space for Relative Entropy-based Image Thresholding

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CGIV
2007
Computer Graphics, Imaging and Visualisation (CGIV 2007)
Abstract - Improved Co-occurrence Matrix as a Feature Space for Relative Entropy-based Image Thresholding

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Computer Graphics, Imaging and Visualisation (CGIV 2007)

Bangkok, Thailand

August 14-August 17

ISBN: 0-7695-2928-3

	ASCII Text	x
	I. El-Feghi, N. Adem, M.A. Sid-Ahmed, M. Ahmadi, "Improved Co-occurrence Matrix as a Feature Space for Relative Entropy-based Image Thresholding," Computer Graphics, Imaging and Visualization, International Conference on, pp. 314-320, Computer Graphics, Imaging and Visualisation (CGIV 2007), 2007.

	BibTex	x
	@article{ 10.1109/CGIV.2007.49, author = {I. El-Feghi and N. Adem and M.A. Sid-Ahmed and M. Ahmadi}, title = {Improved Co-occurrence Matrix as a Feature Space for Relative Entropy-based Image Thresholding}, journal ={Computer Graphics, Imaging and Visualization, International Conference on}, volume = {0}, year = {2007}, isbn = {0-7695-2928-3}, pages = {314-320}, doi = {http://doi.ieeecomputersociety.org/10.1109/CGIV.2007.49}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Computer Graphics, Imaging and Visualization, International Conference on TI - Improved Co-occurrence Matrix as a Feature Space for Relative Entropy-based Image Thresholding SN - 0-7695-2928-3 SP314 EP320 A1 - I. El-Feghi, A1 - N. Adem, A1 - M.A. Sid-Ahmed, A1 - M. Ahmadi, PY - 2007 KW - image thresholding KW - cooccurrence matrix KW - relative entropy VL - 0 JA - Computer Graphics, Imaging and Visualization, International Conference on ER -

I. El-Feghi, Higher Industrial Insitutte, Misurata-Libya

N. Adem, El-Fath University, Tripoli-Libya

M.A. Sid-Ahmed, University of Windsor, Canada

M. Ahmadi, University of Windsor, Canada

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CGIV.2007.49

In this paper, a thresholding technique suitable for noisy background images is proposed. The proposed algorithm uses an improved co-occurrence matrix as feature spaces. The threshold value is obtained by maximizing the relative entropy. Experimental results show that the proposed method outperforms other thresholding techniques especially on the presences of noise in the background of the input image.

Index Terms:

image thresholding, cooccurrence matrix, relative entropy

Citation:

I. El-Feghi, N. Adem, M.A. Sid-Ahmed, M. Ahmadi, "Improved Co-occurrence Matrix as a Feature Space for Relative Entropy-based Image Thresholding," cgiv, pp.314-320, Computer Graphics, Imaging and Visualisation (CGIV 2007), 2007

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