Paper Title:
Image Fusion Base on K-Means Clustering and Contourlet Transform
  Abstract

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
Chapter
Chapter 2: Microwaves Optics and Image
Edited by
David Wang
Pages
540-544
DOI
10.4028/www.scientific.net/KEM.500.540
Citation
L. K. Wang, C. J. Li, Q. Wang, Z. H. Yang, Z. J. Wang, "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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