K-MEANS CLUSTERING IN TEXTURED IMAGE: EXAMPLE OF LAMELLAR MICROSTRUCTURE IN TITANIUM ALLOYS

Ranya Al Darwich

raldarwich@kis.p.lodz.pl
Lodz University of Technology, Institute of Applied Computer Science (Poland)

Laurent Babout


Lodz University of Technology, Institute of Applied Computer Science (Poland)

Krzysztof Strzecha


Lodz University of Technology, Institute of Applied Computer Science (Poland)

Abstract

This paper presents an implementation of the k-means clustering method, to segment cross sections of X-ray micro tomographic images of lamellar Titanium alloys. It proposes an approach for estimating the optimal number of clusters by analyzing the histogram of the local orientation map of the image and the choice of the cluster centroids used to initialize k-means. This is compared with the classical method considering random coordinates of the clusters.


Keywords:

k-means clustering, oriented textured, number of clusters, X-ray tomography

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Published
2019-09-30

Cited by

Al Darwich, R., Babout, L. ., & Strzecha, K. . (2019). K-MEANS CLUSTERING IN TEXTURED IMAGE: EXAMPLE OF LAMELLAR MICROSTRUCTURE IN TITANIUM ALLOYS. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 7(3), 43–46. https://doi.org/10.5604/01.3001.0010.5213

Authors

Ranya Al Darwich 
raldarwich@kis.p.lodz.pl
Lodz University of Technology, Institute of Applied Computer Science Poland

Authors

Laurent Babout 

Lodz University of Technology, Institute of Applied Computer Science Poland

Authors

Krzysztof Strzecha 

Lodz University of Technology, Institute of Applied Computer Science Poland

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