Sampling and clustering algorithm for determining the number of clusters based on the rosette pattern

Sadr, Ali; Momtaz, Amirkeyvan

doi:doi:10.1117/1.OE.51.1.017204

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Optical Engineering / Volume 51 / Issue 1 / Machine Vision, Pattern Recognition

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Sampling and clustering algorithm for determining the number of clusters based on the rosette pattern

Opt. Eng. 51, 017204 (Feb 06, 2012); http://dx.doi.org/10.1117/1.OE.51.1.017204

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Abstract

References (49)

Ali Sadr and Amirkeyvan Momtaz

Iran University of Science and Technology, Daneshgah Street, Hengam Avenue , Tehran, Iran

Clustering is one of the image-processing methods used in non-destructive testing (NDT). As one of the initializing parameters, most clustering algorithms, like fuzzy C means (FCM), Iterative self-organization data analysis (ISODATA), K-means, and their derivatives, require the number of clusters. This paper proposes an algorithm for clustering the pixels in C-scan images without any initializing parameters. In this state-of-the-art method, an image is sampled based on the rosette pattern and according to the pattern characteristics, and extracted samples are clustered and then the number of clusters is determined. The centroids of the classes are computed by means of a method used to calculate the distribution function. Based on different data sets, the results show that the algorithm improves the clustering capability by 92.93% and 91.93% in comparison with FCM and K-means algorithms, respectively. Moreover, when dealing with high-resolution data sets, the efficiency of the algorithm in terms of cluster detection and run time improves considerably.

History

Received Jul 18, 2011
Accepted Nov 14, 2011
Revised Oct 26, 2011
Published online Feb 06, 2012

Digital Object Identifier

http://dx.doi.org/10.1117/1.OE.51.1.017204

Citation

Ali Sadr and Amirkeyvan Momtaz, "Sampling and clustering algorithm for determining the number of clusters based on the rosette pattern", Opt. Eng. 51, 017204 (Feb 06, 2012); http://dx.doi.org/10.1117/1.OE.51.1.017204