Does anyone have an implementation of EnFCM algorithm?

Dear Ahmed,

Please see the following:

1-ISSN 2231-4903, Volume 03, No 02

July 2013, P.P. 55-61

A New Efficient Algorithm for Segmenting Noisy image Using Fuzzy

Abstract:

This paper presents a variation of fuzzy c-means (FCM) algorithm that provides image segmentation. The proposed algorithm includes the tradeoff weighted fuzzy factor which depends on the distance all neighboring pixels and their gray-level difference simultaneously. The new algorithm is called kernel weighted fuzzy local information C-Means (KWFLICM). KWFLICM can overcome the disadvantages of the known fuzzy c-means algorithms and at the same time enhances the clustering performance. The major characteristic of KWFLICM is that the use of a kernel distance measure to its objective function to increase the performance. The new algorithm adaptively determines the kernel parameter by using a fast bandwidth selection rule based on the distance variance of all data points in the collection. Especially, the tradeoff weighted fuzzy factor and the kernel distance measure are both parameter free. Experiments performed on synthetic and real-world images show that KWFLICM algorithm is effective and efficient in improving the performance of image segmentation, providing robustness to noisy images.

Key words:

Clustering, Fuzzy C-means, KWFLICM, weighted fuzzy factor, gray level, image segmentation, and

https://www.academia.edu/5035100/A_New_Efficient_Algorithm_for_Segmenting_Noisy_image_Using_Fuzzy

2-A Robust Fuzzy Local Information C-means

Clustering Algorithm

Stelios Krinidis and Vassilios Chatzis

Abstract—This paper presents a variation of fuzzy c-means

(FCM) algorithm that provides image clustering. The proposed

algorithm incorporates the local spatial information and gray

level information in a novel fuzzy way. The new algorithm

is called Fuzzy Local Information C-Means (FLICM). FLICM

can overcome the disadvantages of the known fuzzy c-means

algorithms and at the same time enhances the clustering performance.

The major characteristic of FLICM is the use of a fuzzy

local (both spatial and gray level) similarity measure, aiming

to guarantee noise insensitiveness and image detail preservation.

Furthermore, the proposed algorithm is fully free of the empirically

adjusted parameters (a, λg, λs, etc) incorporated in to

all other fuzzy c-means algorithms proposed in the literature.

Experiments performed on synthetic and real-world images

show that FLICM algorithm is effective and efficient, providing

robustness to noisy images.

Index Terms—Fuzzy c-means, clustering, image segmentation,

spatial constraints, gray level constraints, fuzzy constraints.

http://infoman.teikav.edu.gr/~stkrini/pdfFiles/journals/2010_TIP.pdf

3-https://books.google.ps/books?id=fKIMBwAAQBAJ&pg=PA287&lpg=PA287&dq=implementation+of+EnFCM+algorithm&source=bl&ots=AahpX5BEGW&sig=QjLhS7cBaY4ykFzZawT8KrIA968&hl=en&sa=X&ved=0ahUKEwiwgvbS09fJAhWGuRoKHSVODpQ4ChDoAQgmMAI#v=onepage&q=implementation%20of%20EnFCM%20algorithm&f=false

4-A Fuzzy Clustering Algorithm of Automatic Classification Based on EnFCM

AUTHOR(S)Hong-wei Han; Lin Tian; Jia-qing Miao

PUB. DATEJuly 2014

SOURCEAdvanced Materials Research;7/24/2014, Vol. 989-994, p1489

SOURCE TYPEAcademic Journal

DOC. TYPEArticle

ABSTRACTFuzzy c- means (FCM) algorithm is an unsupervised clustering algorithm for image segmentation, and has been widely applied because the segmentation results are consistent with human visual characteristics. Enhanced fuzzy c-means clustering (EnFCM) algorithm is the improved FCM algorithm, which reduces the computational complexity. But, both FCM algorithm and EnFCM algorithm, clustering number still need to be manually determined. This paper, in order to realize the automation degree of algorithm, presents an improved algorithm. It first analyzes the histogram, then automatically determines the clustering number and peak value of each class through use of the peak point detection technology, finally segments image by using EnFCM algorithm. Experiments show that this method is a kind of faster fuzzy clustering algorithm with automatic classification ability for image segmentation.

http://connection.ebscohost.com/c/articles/100086391/fuzzy-clustering-algorithm-automatic-classification-based-enfcm

5-J. Biomedical Science and Engineering, 2009, 2, 656-660

doi: 10.4236/jbise.2009.28096 Published Online December 2009 (http://www.SciRP.org/journal/jbise/

JBiSE

Published Online December 2009 in SciRes. http://www.scirp.org/journal/jbise

Fingerprint image segmentation using modified fuzzy c-means

algorithm

Jia-Yin Kang1

, Cheng-Long Gong1

, Wen-Juan Zhang2

School of Electronics Engineering, Huaihai Institute of Technology, Lianyungang, China; 2

School of Computer Engineering, Huaihai Institute of Technology, Lianyungang, China.

Email: [email protected]

Received 16 July 2009; revised 31 August 2009; accepted 1 September 2009.

ABSTRACT

Fingerprint segmentation is a crucial step in fingerprint

recognition system, and determines the results

of fingerprint analysis and recognition. This paper

proposes an efficient approach for fingerprint segmentation

based on modified fuzzy c-means (FCM).

The proposed method is realized by modifying the

objective function in the Szilagyi’s algorithm via introducing

histogram-based weight. Experimental results

show that the proposed approach has an efficient

performance while segmenting both original

fingerprint image and fingerprint images corrupted

by different type of noises.

Keywords: Fingerprint; Segmentation; Fuzzy C-means;

Histogram; Robustness

file:///C:/Users/Rafik%20Karaman/Downloads/JBiSE20080800014_45746603.pdf

6-A fast fuzzy c-means algorithm for color image

segmentation

Hoel Le Capitaine and Carl Frélicot

EUSFLAT-LFA 2011

Abstract

Color image segmentation is a fundamental task in

many computer vision problems. A common approach

is to use fuzzy iterative clustering algorithms

that provide a partition of the pixels into a given

number of clusters. However, most of these algorithms

present several drawbacks: they are time

consuming, and sensitive to initialization and noise.

In this paper, we propose a new fuzzy c-means algorithm

aiming at correcting such drawbacks. It relies

on a new efficient cluster centers initialization and

color quantization allowing faster and more accurate

convergence such that it is suitable to segment

very large color images. Thanks to color quantization

and a new spatial regularization, the proposed

algorithm is also more robust. Experiments on real

images show the efficiency in terms of both accuracy

and computation time of the proposed algorithm as

compared to recent methods of the literature

file:///C:/Users/Rafik%20Karaman/Downloads/eusflatlfa2011_RS_36.pdf

7-EDAF15 Algorithm Implementation

http://cs.lth.se/edaf15

8-https://books.google.ps/books?id=VhRIcHjvFY8C&pg=PA218&lpg=PA218&dq=implementation+of+EnFCM+algorithm&source=bl&ots=WTGZC-opdq&sig=PTkajfjBj5UyiU0Pa1FN0a1mYP8&hl=en&sa=X&ved=0ahUKEwiLlYLF0tfJAhUG2RoKHRhCBuMQ6AEIOzAI#v=onepage&q=implementation%20of%20EnFCM%20algorithm&f=false

9-https://books.google.ps/books?id=2hk5eBA4C_QC&pg=PA342&lpg=PA342&dq=implementation+of+EnFCM+algorithm&source=bl&ots=NTE_jAGb7e&sig=wEIIFd1MPl9wbU6oMuvOZTgXQ2A&hl=en&sa=X&ved=0ahUKEwiLlYLF0tfJAhUG2RoKHRhCBuMQ6AEIPTAJ#v=onepage&q=implementation%20of%20EnFCM%20algorithm&f=false

Hoping this will be helpful,

Rafik

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