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Fast segmentation of N-dimensional grayscale images

5.0 | 2 ratings Rate this file 53 Downloads (last 30 days) File Size: 308 KB File ID: #41967 Version: 1.1
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Fast segmentation of N-dimensional grayscale images


Anton Semechko (view profile)


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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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.


This file inspired A Hybrid Algorithm For Disparity Calculation From Sparse Disparity Estimates Based On Stereo Vision.

Required Products MATLAB
MATLAB release MATLAB 7.12 (R2011a)
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Comments and Ratings (4)
19 Apr 2016 Pasha Mahmoudzadeh

10 Mar 2016 tan yuki

May I know what is the algorithm used? Do you have any article? Thanks

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01 Jan 2014 H612

H612 (view profile)

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

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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.

30 May 2013 1.1

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

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