Image Fusion Base on K-Means Clustering and Contourlet Transform

Abstract:

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The robustness of K-means clustering is poor in non-spherical distribution data, in order to improve the universal ability of clustering algorithms, the cross-entropy distance measure was used to replace the Euclidean distance measure . Contour let transform, not only has characteristics of multi-resolution, locality and critical sampling which wavelet has, but also has the characteristics of multiple decomposition directions and anisotropy which wavelets lack. So we combine the modified K-means clustering and Contour let transform to apply for image fusion. Experimental results show that this method is feasible.

Info:

Periodical:

Edited by:

David Wang

Pages:

540-544

DOI:

10.4028/www.scientific.net/KEM.500.540

Citation:

L. K. Wang et al., "Image Fusion Base on K-Means Clustering and Contourlet Transform", Key Engineering Materials, Vol. 500, pp. 540-544, 2012

Online since:

January 2012

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Price:

$35.00

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