K-MEANS CLUSTERING IN TEXTURED IMAGE: EXAMPLE OF LAMELLAR MICROSTRUCTURE IN TITANIUM ALLOYS
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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Lodz University of Technology, Institute of Applied Computer Science Poland
Lodz University of Technology, Institute of Applied Computer Science Poland
Lodz University of Technology, Institute of Applied Computer Science Poland
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