Texture Classification using Local and Global Histogram Equalization and the Lempel-Ziv-Welch Algorithm

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Fifth International Conference on Hybrid Intelligent Systems (HIS'05)

Rio de Janeiro, Brazil

December 06-December 09

ISBN: 0-7695-2457-5

	ASCII Text	x
	Leonardo Vidal Batista, Moab Mariz Meira, Nicomedes L. Cavalcanti, "Texture Classification using Local and Global Histogram Equalization and the Lempel-Ziv-Welch Algorithm," Hybrid Intelligent Systems, International Conference on, pp. 297-302, Fifth International Conference on Hybrid Intelligent Systems (HIS'05), 2005.

	BibTex	x
	@article{ 10.1109/ICHIS.2005.102, author = {Leonardo Vidal Batista and Moab Mariz Meira and Nicomedes L. Cavalcanti}, title = {Texture Classification using Local and Global Histogram Equalization and the Lempel-Ziv-Welch Algorithm}, journal ={Hybrid Intelligent Systems, International Conference on}, volume = {0}, year = {2005}, isbn = {0-7695-2457-5}, pages = {297-302}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICHIS.2005.102}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Hybrid Intelligent Systems, International Conference on TI - Texture Classification using Local and Global Histogram Equalization and the Lempel-Ziv-Welch Algorithm SN - 0-7695-2457-5 SP297 EP302 A1 - Leonardo Vidal Batista, A1 - Moab Mariz Meira, A1 - Nicomedes L. Cavalcanti, PY - 2005 KW - null VL - 0 JA - Hybrid Intelligent Systems, International Conference on ER -

Leonardo Vidal Batista, DI - UFPB Cidade Universitaria, Brazil

Moab Mariz Meira, DI - UFPB Cidade Universitaria, Brazil

Nicomedes L. Cavalcanti, CIn / UFPE Cidade Universitaria, Brazil

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICHIS.2005.102

This paper presents a new texture classification method using histogram equalization and the Lempel-Ziv-Welch algorithm. Two versions of the classifier are presented. The first version uses global histogram equalization and the second one uses local histogram equalization. After local or global histogram equalization, each texture sample is encoded by LZW using pre-constructed dictionaries and assigned to the class whose dictionaries minimize the coding rate. Both versions are tested under different conditions of gray-level variations.

Citation:

Leonardo Vidal Batista, Moab Mariz Meira, Nicomedes L. Cavalcanti, "Texture Classification using Local and Global Histogram Equalization and the Lempel-Ziv-Welch Algorithm," his, pp.297-302, Fifth International Conference on Hybrid Intelligent Systems (HIS'05), 2005

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