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

Ranya Al Darwich | Zendy; Laurent Babout | Zendy; Krzysztof Strzecha | Zendy

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

Author(s) -

Ranya Al Darwich,

Laurent Babout,

Krzysztof Strzecha

Publication year - 2017

Publication title -

informatyka automatyka pomiary w gospodarce i ochronie środowiska

Language(s) - English

Resource type - Journals

eISSN - 2391-6761

pISSN - 2083-0157

DOI - 10.5604/01.3001.0010.5213

Subject(s) - centroid , lamellar structure , histogram , cluster analysis , cluster (spacecraft) , image (mathematics) , titanium , microstructure , materials science , titanium alloy , orientation (vector space) , artificial intelligence , computer science , pattern recognition (psychology) , mathematics , metallurgy , geometry , alloy , programming language

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.

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