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Fast segmentation of N-dimensional grayscale images

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Fast segmentation of N-dimensional grayscale images

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28 May 2013 (Updated )

Partition N-D grayscale image into c classes using efficient C-means and fuzzy C-means clustering

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Description

c-means and fuzzy c-means clustering are two very popular image segmentation algorithms. While their implementation is straightforward, if realized naively it will lead to substantial overhead in execution time and memory consumption. Although these deficiencies could be ignored for small 2D images they become more noticeable for large 3D datasets. This submission is intended to provide an efficient implementation of these algorithms for segmenting N-dimensional grayscale images. The computational efficiency is achieved by using the histogram of the image intensities during the clustering process instead of the raw image data. Finally, since the algorithms are implemented from scratch there are no dependencies on any auxiliary toolboxes.

For a quick demonstration of how to use the functions, run the attached 'DemoFCM' file.

Acknowledgements

This file inspired Fast Stereo Matching And Disparity Estimation By S Mukherjee And Prof. G.R.M Reddy.

Required Products MATLAB
MATLAB release MATLAB 7.12 (R2011a)
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Comments and Ratings (2)
01 Jan 2014 H612

if i'd like to do it based on color values and my input image is COLOR image... then? Plz guide

28 Aug 2013 Emmanuel Farhi

This is a very good replacement for the kmeans function, and it does not require any toolbox, and works N-D.

Updates
30 May 2013

Included a function that transforms 1D fuzzy memberships to fuzzy membership maps.

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