Contents and abstracts
-
K. Madani and R. Kh. Sadykhov.
Editorial. - p. 5-7.
-
M. Kamrul Islam.
An Improved Architecture for Competitive and Cooperative Neurons (CCNS) in
Neural Networks. - p. 8-15.
-
E. Volna.
Forming Evolutionary Design of Neural Networks with Different Nodes. - p. 16-23.
-
P. C. Hung, S. F. McLoone, R. Farrell.
Direct and Indirect Classification of High Frequency LNA Gain Performance –
A Comparison between SVMs and MLPs. - p. 24-31.
-
M. Voiry, K. Madani, V. Amarger, J. Bernier.
Data Dimensionality Reduction for Neural Based Classification of Optical Surfaces Defects. - p. 32-42.
-
I. Budnyk, A. Chebira, K. Madani.
Estimating Complexity of Classification Tasks Using Neurocomputers Technology. - p. 43-52.
-
R. A. Vazquez, H. Sossa.
Associative Memories Network for Face Recognition and Object Recognition. - p. 53-60.
-
Y. Kurylyak, I. Paliy, A. Sachenko, A. Chohra, K. Madani.
Face Detection on Grayscale and Color Images Using Combined Cascade of Classifiers. - p. 61-71.
-
H. Kim, J. K. Tan, S. Ishikawa, T. Shinomiya.
Automatic Detection of Spinal Deformity Based on Statistical Features from the Moire
Topographic Images. - p. 72-78.
-
W. Bellil, C. Ben Amar, A. M. Alimi.
A Survey on Wavelet Network, Multi Library Wavelet Network Training, 1D-2D Function
Approximation and a New Image Compression Method. - p. 79-86.
-
R. Kh. Sadykhov, V. V. Ganchenko, L. P. Podenok.
Fuzzy Clustering Methods in Multispectral Satellite Image Segmentation. - p. 87-94.
EDITORIAL
“Artificial Neural Networks and Intelligent Information Processing”
Guest Editors: Kurosh Madani 1) and Rauf Kh. Sadykhov 2)
1) Prof. Dr. Kurosh Madani
PARIS-EST / PARIS 12 – Val de Marne University
Senart-FB Institute of Technology / LISSI Lab.
IUT Senart – Bat. A, Av. Pierre Point
F-77127 Lieusaint, France
Email: madani@univ-paris12.fr
2) Prof. Dr. Rauf Kh. Sadykhov
Computer Systems Department
University of Informatics and Radioelecrtronics
6 P. Brovka st, Minsk, Belarus.
Email: rsadykhov@bsuir.by
New applicative and technological challenges
emanating from industrial, socioeconomic or
environmental needs, appeared in recent decades,
have opened new dilemmas which have decisively
highlighted limitations of conventional
computational science and issued techniques. Recent
borders’ contraction between biological and
computational sciences, especially the latest
developments in bio-inspired artificial systems over
the last decade, may play a central role in designing
adequate solutions to these new challenging
dilemmas. The fantastic ever-increasing intellectual
dynamics created around bio-inspired Artificial
Intelligence and related topics (as Artificial Neural
Networks, Humanoid Robotics, Ambient
Intelligence, etc…), uphold by escalating interest of
both confirmed and young researchers on this
relatively juvenile science, generates a reach
multidisciplinary synergy between a large number of
scientific communities making conceivable a
forthcoming emergence of viable solutions to
aforementioned real-world applicative and
technological challenges.
This special issue of the International Scientific
Journal of Computing includes a selection of papers
presented at the Third International Workshop on
Artificial Neural Networks and Intelligent
Information Processing (ANNIIP), which was held
in Angers, France, May 9-12th, 2007. Since 2005, the
ANNIIP international workshop takes part in the
aforementioned appealing dynamics within the
frame of the prestigious IFAC/IEEE ICINCO
International Conference. The objective of this
workshop is to convene a set of relevant recent
works focusing Bio-inspired Artificial Intelligence
related fields and applications by offering a
privileged space to overhaul and exchange the
knowledge about further theoretical advances, new
experimental discoveries and novel technological
improvements in this promising area.
ANNIIP points toward the choice of a relatively
restricted number of papers. Such philosophy is
motivated on the one hand by the premeditated
desire to give a large space to exchanges and
discussions during the workshop, and on the other
hand by the strong principle of the presentation of
each accepted article by its authors. Conformably to
our philosophy and reaching those objectives, the
idea of the present Special Issue has been motivated
by our desire to devote an additional “scientific
space” to a number of selected papers of ANNIIP
2007 in order to complete and to extend the
presented works.
Articles composing this Special Issue are
extended versions of already accepted papers and
presented during ANNIIP 2007. That’s why we have
attached a special attention on reviewing process of
this Special Issue. In fact, our first attention has been
our premeditated desire to involve totally new
experts not belonging to ANNIIP 2007 Program
Committee. We would like to reedit our gratitude to
all those experts for the valuable work that they have
accomplished in total freedom and independency.
The papers selected for this special issue reflect
the above-proclaimed deliberate philosophy and the
variety of research presented during the workshop.
They epitomize miscellany of involved topics and
the diversity of techniques connecting Artificial
Neural Networks, genetic algorithms, evolutionary
computation, machine learning and experts’
hybridization.
The following two papers are devoted to Machine
learning mechanisms, design and architecture of
Artificial Neural Networks.
The paper “An Improved Architecture for
Competitive and Cooperative Neurons (CCNS) in
Neural Networks” by K. Islam describes competition
and cooperation in neural processing dealing
especially with the storing ability of the memory
model. The paper proposes a new architecture for
competitive and cooperative neurons improving and
increasing the storing ability.
The paper “Forming Evolutionary Design of
Neural Networks With Different Nodes” by E. Volna
presents the design of a neuron-evolution based
system. The presented system combines populations
of neurons in order to form the fully connected
multilayer feed-forward neural networks with fixed
architecture. The article shows the transfer
function’s important impact on obtained artificial
neural network’s architecture and its significant
impact on artificial neural network’s performance.
The efficiency of described method has been
evaluated on pattern recognition as well as on
alphabet coding problem.
Complex data and information classifications as
well as models’ mixing strategies (e.g. intelligent
systems) have traditionally been one of the main
pillars of ANNIIP. The three following papers
reflect three significant aspects of the
aforementioned areas. The first one is the paper
“Direct and Indirect Classification of High
Frequency LNA Gain Performance – A Comparison
between SVMs and MLPs” by P. C. Hung,
S. F. McLoone and R. Farrell, which deals with
direct and indirect ANN based classification. An
indirect Multilayer Perceptron (MLP) and direct
support vector machine (SVM) classification
strategy are considered, evaluated and compared.
The evaluation is performed considering the
challenging problem of low noise amplifiers (LNA)
design and their high-frequency performances in
functional testing. A novel testing strategy using
machine learning classifiers to predict highfrequency
LNA gain performance by combining
information from several lower frequency
measurements has been elaborated. The reported
case study shows the proposed technique’s potential
in extending the operating frequency range by 20%
at 2 GHz and 42% at 1.4 GHz.
The second key aspect relating classification
skills is data dimensionality reduction. The paper
“Data Dimensionality Reduction for Neural Based
Classification of Optical Surfaces Defects” by
M. Voiry, K. Madani, V. Amarger and J. Bernier is
concerned with this purpose. Within the frame of a
real industrial application, dealing with high-tech
optical devices production, the paper compares
different techniques which permit dimensionality
reduction and evaluate their impact on ANN based
classification tasks performances. Principal
Component Analysis (PCA), Self Organizing Maps
(SOM), Curvilinear Component Analysis (CCA) and
Curvilinear Distance Analysis (CDA) issued
techniques have been used in order to reduce the
dimensionality of the initial feature space. The paper
shows that using CDA the global defect detection
and correct classification performances reach 95%
defecting performances obtained when using raw
data (not reduced feature space).
Finally, the third key aspect coping with complex
data classification is “classification task’s
complexity estimation which is the purpose of the
paper “Estimating Complexity of Classification
Tasks Using Neuro-Computers Technology” by
I. Budnyk, A. Chebira and K. Madani. This paper
presents an alternative approach for estimating
classification tasks’ complexity. The frame of the
presented work deals with the construction of a selforganizing
neural tree-like structure, following the
“divide and rule” paradigm. A new approach using
IBM Zero Instruction Set Computer (ZISC-036)
neuro-processor is described, evaluated and applied
to a range of the different classification tasks dealing
with real-world classifications dilemmas.
If applications of Artificial Neural Networks
have been, over the last decade, an ever increasing
subject in numerous conferences relating these bioinspired
models (including ANNIIP), their
innovative applications continue to be a noteworthy
part of the workshop. During ANNIIP 2007, this
component has taken a thrilled space. This can be
appreciated in the five selected papers of this Special
Issue. Two among them focus face recognition, a
rising concern in nowadays public security domain.
The three others deal with further skills of pattern
and image recognition.
The paper “Associative Memories Network for
Face Recognition and Object Recognition” by
R. A. Vazquez and H. Sossa is devoted to a network
of associative memories (AMs) to recall a collection
of patterns. The accuracy of the proposed AMs is
evaluated on the one hand on an objects’ recognition
application and the other hand on a faces’
recognition one. First the all, the benchmarks are
split into several collections and then this collections
are used to train the network of AMs. During
training an image of a collection is associated with
the rest of the images belonging to the same
collection. Once trained the network we expected to
recover a collection of images by using as an input
pattern any image belonging to the collection.
The paper “Face Detection on Grayscale and
Color Images Using Combined Cascade of
Classifiers” by Y. Kurylyak, I. Paliy, A. Sachenko,
A. Chohra and K. Madani is another emissary
representative of how to deal with the face
recognition applications combining several
classifiers. The paper describes improved face
detection methods for grayscale and color images
using a cascade of classifiers and skin color
segmentation. The proposed multi-classifiers face
authentication method allows achieving one of the
best detection rates on available benchmarks with a
high processing speed making the proposed
approach suitable for a video flow processing. The
paper also shows that the use of a mixture of color
spaces is more efficient for the skin color
segmentation than the consideration of only one
color space.
In the paper “Automatic Detection of Spinal
Deformity Based on Statistical Features from the
Moire Topographic Images” by H. Kim, J. K. Tan,
S. Ishikawa and T. Shinomiya, a new automatic
classification method for the spinal deformity
detection. The focused application concerns the
biomedical area relating a disease mainly suffered
by teenagers during their growth stage particularly
from elementary school to middle school. The
proposed technique combines an automatic moire
image based Regions of interest’s (ROI) extractor
and Artificial Neural Network based classifier.
Several Artificial Neural Network based classifiers
have been evaluated and compared. The obtained
results reach the classification rate of 85% making
the proposed concept a suitable candidate for
designing computer aided spinal deformity detection
tool.
The paper “A Survey on Wavelet Network, Multi-
Library Wavelet Network Training, 1D-2D Function
Approximation and a New Image Compression
Method” by W. Bellil, C. Ben Amar and
A. M. Alimi focuses Wavelet Network and its Multi
library training within the frame of image
compression application. The proposed approach
takes advantage from an optimization procedure
using a wavelet functions’ library. The efficiency of
color images compression is increased comparing to
a number of conventional techniques. The loss in
processing speed can be largely corrected by
decreasing the number of wavelets in hidden layer
without decreasing considerably the compression
ratio.
Finally, the paper “Fuzzy Clustering Methods in
Multi-spectral Satellite Images Segmentation” by
R. Kh. Sadykhov, V. V. Ganchenko and
L. P. Podenok, presents the core concept Fuzzy
Clustering (FC) based satellite images segmentation.
The three FC methods (C-means, Gustafson-Kessel,
and Gath-Geva algorithms), with and without
preliminary nonlinear filtering, testify that
segmentation using fuzzy clustering methods
provides good-looking discrimination of land cover
types that occurs in the complex cases. Applied on
Landsat multi-spectral images, the obtain results
show the effectiveness of proposed concept in
segmentation of wetland, water-meadow, and bush
areas which remain awkward points in satellite
images’ analysis. The quality of segmented images
was approved by experts on the basis of land-based
expedition data.
Before ending the editorial, it is important to
remind that scientific relevance and technical quality
of a collective issue emerge from quality of its
contributors: those who contribute by the high
quality of their manuscripts. We would like to
express again our acknowledgements to contributors
of all selected papers: You are the central reason of the
nobles of this Special Issue.
It is also essential to be reminiscent that
frequently, creative dynamics is the result of fruitful
human contacts within a same scientific field or the
consequence of humans’ interactions from different
scientific communities and since 2004, the date of
the its first edition, the ICINCO multi-conference
has been an outstanding bench of such creative
synergies. For that, we would like to express our
particular gratitude to Prof. Joaquim Filipe, ICINCO
2007 Conference’s Chair, for his faith in young
science of “Bio-inspired Artificial Intelligence” and
for his reliance on including once more the ANNIIP
workshop within his valuable conference. We would
like also be thankful to Prof. Anatoly Sachenko for
devoting this privileged space of his journal to
ANNIIP 2007.
Finally, we would like to apology for the
somehow tardy reviewing processing, which have
been, nevertheless, the condition to guarantee the
freedom and total independency of Reviewing Board
in accomplishment of their valuable expertise.
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AN IMPROVED ARCHITECTURE FOR COMPETITIVE AND
COOPERATIVE NEURONS (CCNS) IN NEURAL NETWORKS
M. Kamrul Islam
School of Computing,
Queen’s University,
Kingston, K7L 3N6, ON, Canada
islam@cs.queensu.ca
In neural networks, the associative memory is one in which applying some input pattern leads to the response
of a corresponding stored pattern. During the learning phase the memory is fed with a number of input vectors and in
the recall phase when some known input is presented to it, the network recalls and reproduces the output vector. Here,
we improve and increase the storing ability of the memory model proposed in [1]. We show that there are certain
instances where their algorithm can not produce the desired performance by retrieving exactly the correct vector. That
is, in their algorithm, a number of output vectors can become activated from the stimulus of an input vector while the
desired output is just a single vector. Our proposed solution overcomes this and uniquely determines the output vector
as some input vector is applied. Thus we provide a more general scenario of this neural network memory model
consisting of Competitive Cooperative Neurons (CCNs).
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FORMING EVOLUTIONARY DESIGN OF NEURAL NETWORKS
WITH DIFFERENT NODES
Eva Volna
University of Ostrava,
30th Dubna st. 22,
701 03 Ostrava, Czech Republic
e-mail: eva.volna@osu.cz, http://www.osu.cz
Evolution in artificial neural networks (e.g. neuroevolution) searches through the space of behaviours for a
network that performs well at a given task. Here is presented a neuroevolution system evolving populations of neurons
that are combined to form the fully connected multilayer feedforward neural network with fixed architecture. In this
article, the transfer function has been shown to be an important part of architecture of the artificial neural network and
have significant impact on an artificial neural network’s performance. In order to test the efficiency of described
method, we applied it to the pattern recognition problem and to the alphabet coding problem.
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DIRECT AND INDIRECT CLASSIFICATION OF HIGH FREQUENCY LNA
GAIN PERFORMANCE – A COMPARISON BETWEEN SVMS AND MLPS
Peter C. Hung, Seán F. McLoone, Ronan Farrell
Institute of Microelectronics and Wireless Systems,
Department of Electronic Engineering,
National University of Ireland Maynooth,
Maynooth, Co. Kildare, Ireland,
{phung, sean.mcloone, rfarrell}@eeng.nuim.ie,
http://imws.eeng.nuim.ie/
The task of determining low noise amplifier (LNA) high-frequency performance in functional testing is as
challenging as designing the circuit itself due to the difficulties associated with bringing high frequency signals offchip.
One possible strategy for circumventing these difficulties is to inferentially estimate the high frequency
performance measures from measurements taken at lower, more accessible, frequencies. This paper investigates the
effectiveness of this strategy for classifying the high frequency gain of the amplifier, a key LNA performance parameter.
An indirect Multilayer Perceptron (MLP) and direct support vector machine (SVM) classification strategy are
considered. Extensive Monte-Carlo simulations show promising results with both methods, with the indirect MLP
classifiers marginally outperforming SVMs.
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DATA DIMENSIONALITY REDUCTION FOR NEURAL BASED
CLASSIFICATION OF OPTICAL SURFACES DEFECTS
Matthieu Voiry 1&2) , Kurosh Madani 1), Véronique Amarger 1), Joël Bernier 2)
1) Image, Signal and Intelligent Systems Laboratory (LISSI / EA 3956),
Senart Institute of Technology,
University PARIS XII, Av. Pierre Point,
F-77127 Lieusaint, France,
{voiry ; madani ; amarger}@univ-paris12.fr,
http://www.univ-paris12.fr/
2) SAGEM REOSC
Avenue de la Tour Maury, Saint Pierre du Perray, 91280, France
{mathieu.voiry or joel.bernier}@sagem.com
A major step for high-quality optical surfaces faults diagnosis concerns scratches and digs defects
characterization in products. This challenging operation is very important since it is directly linked with the produced
optical component’s quality. A classification phase is mandatory to complete optical devices diagnosis since a number
of correctable defects are usually present beside the potential “abiding” ones. Unfortunately relevant data extracted
from raw image during defects detection phase are high dimensional. This can have harmful effect on the behaviors of
artificial neural networks which are suitable to perform such a challenging classification. Reducing data dimension to a
smaller value can decrease the problems related to high dimensionality. In this paper we compare different techniques
which permit dimensionality reduction and evaluate their impact on classification tasks performances.
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ESTIMATING COMPLEXITY OF CLASSIFICATION TASKS USING
NEUROCOMPUTERS TECHNOLOGY
Ivan Budnyk, Abdennasser Chebira, Kurosh Madani
Images, Signals and Intelligent Systems Laboratory (LISSI / EA 3956)
PARIS 12 – Val de Marne University, Senart-Fontainebleau Institute of Technology,
Bat. A, Av. Pierre Point, F-77127 Lieusaint, France,
{ivan.budnyk, chebira, madani}@univ-paris12.fr,
http://lissi.univ-paris12.fr
This paper presents an alternative approach for estimating task complexity. Construction of a self-organizing
neural tree structure, following the paradigm “divide and rule”, requires knowledge about task complexity. Our aim is
to determine complexity indicator function and to hallmark its’ main properties. A new approach uses IBM © Zero
Instruction Set Computer (ZISC-036 ®) and applies for a range of the different classification tasks.
Top
ASSOCIATIVE MEMORIES NETWORK FOR FACE RECOGNITION AND
OBJECT RECOGNITION
Roberto A. Vazquez 1), Humberto Sossa 2)
1) Center for Computing Research (CIC-IPN), Av. Juan de Dios Batiz s/n Col. Nueva Industrial Vallejo, CP. 07738
Mexico City, Mexico, ravem@ipn.mx
2) Center for Computing Research (CIC-IPN), Av. Juan de Dios Batiz s/n Colonia Nueva Industrial Vallejo CP. 07738
Mexico City, Mexico, hsossa@cic.ipn.mx
An associative memory (AM) is a special kind of neural network that allows associating an output pattern
with an input pattern. Some problems require associating several output patterns with a unique pattern. Classical
associative and neural models cannot solve this simple task and less if these patterns are complex images, for example
faces. In this paper a network of AMs to recall a collection of patterns is proposed. The accuracy of the proposal is
tested with two benchmarks. One is composed by 20 objects and the other is composed by 20 images of 15 different
people faces. First the all, the benchmarks are split into several collections and then this collections are used to train
the network of AMs. During training an image of a collection is associated with the rest of the images belonging to the
same collection. Once trained the network we expected to recover a collection of images by using as an input pattern
any image belonging to the collection.
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FACE DETECTION ON GRAYSCALE AND COLOR IMAGES USING
COMBINED CASCADE OF CLASSIFIERS
Yuriy Kurylyak 1), Ihor Paliy 1), Anatoly Sachenko 1), Amine Chohra 2), Kurosh Madani 2)
1) Research Institute of Intelligent Computer Systems,
Ternopil National Economic University,
3 Peremoga Square, 46004, Ternopil, Ukraine
{yuk, ipl, as}@tneu.edu.ua
2) Images, Signals and Intelligent Systems Laboratory (LISSI / EA 3956),
PARIS XII University, Senart-FB Institute of Technology
Av. Pierre Point, Bat. A, F-77127, Lieusaint, France
{chohra, madani}@univ-paris 12.fr
The paper describes improved face detection methods for grayscale and color images using the combined
cascade of classifiers and skin color segmentation. The combined cascade with proposed face candidates’ verification
method allows achieving one of the best detection rates on CMU test set and a high processing speed suitable for a
video flow processing. It’s also shown that the mixture of color spaces is more efficient during the skin color
segmentation than the application of one color space. A lot of experiments are made to choose rational parameters for
the developed face detection system in order to improve the detection rate, false positives’ number and system’s speed.
Top
AUTOMATIC DETECTION OF SPINAL DEFORMITY BASED ON
STATISTICAL FEATURES FROM THE MOIRE TOPOGRAPHIC IMAGES
Hyoungseop Kim 1), Joo Kooi Tan 1), Seiji Ishikawa 1), Takashi Shinomiya 2)
1) Department of Control Engineering, Kyushu Institute of Technology,
1-1, Sensui, Tobata, Kitakyushu 804-8550,
Japan, kim@cntl.kyutech.ac.jp
2) Nikon Co. LTD., Japan
Spinal deformity is one of a disease mainly suffered by teenagers during their growth stage particularly from
element school to middle school. There are many different causes of abnormal spinal curves, but all of them are
unknown. To find the spinal deformity in early stage, orthopedists have traditionally performed on children a painless
examination called a forward bending test in mass screening of school. But this test is neither objective nor
reproductive, and the inspection takes much time when applied to medical examination in schools. To solve this
problem, a moire method has been proposed which takes moire topographic images of human backs and checks
symmetry/asymmetry of their moire patterns. In this paper, we propose a method for automatic judgment of spinal
deformity which is obtained moire topographic images based on statistical features on the moire image. Statistical
feature of asymmetry degrees are applied to train employing the classifier such as Artificial Neural Network, Support
Vector Machine, Self-Organization Map and AdaBoost.
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A SURVEY ON WAVELET NETWORK, MULTI LIBRARY WAVELET
NETWORK TRAINING, 1D-2D FUNCTION APPROXIMATION AND A NEW
IMAGE COMPRESSION METHOD
Wajdi Bellil 1), Chokri Ben Amar 2), Adel M.Alimi 3)
1) Faculty of sciences, University of Gafsa, City Zarroug, Gafsa, Tunisia wajdi.bellil@ieee.org
2) Department of Electrical Engineering, University of Sfax, Tunisia Chokri.benamar@ieee.org
3) Department of Electrical Engineering, University of Sfax, Tunisia Adel.Alimi@ieee.org
This paper presents an original architecture of Wavelet Neural Network (WNN) based on multi Wavelets
activation function and uses a selection method to determine a set of best wavelets whose centers and dilation
parameters are used as initial values for subsequent training library WNN for color image compression and coding
which consists to transform an RGB image into Luminance-Chrominance space and then segment the luminance in a
set of m blocks n by n pixels. These blocks should be transferred row by row (1D input vector) to the input of our
wavelet network. Every input vector will be considered as unknown functional mapping and then it will be
approximated by the network.
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FUZZY CLUSTERING METHODS IN MULTISPECTRAL SATELLITE
IMAGE SEGMENTATION
Rauf Kh. Sadykhov 1), Valentin V. Ganchenko 1), Leonid P. Podenok 2)
1) Computer Systems Department,
Belarusian State University of Informatics and Radioelecrtronics,
6 P. Brovka st, Minsk, Belarus
rsadykhov@bsuir.by, ganchenko@lsi-bas-net.by, http://www.bsuir.by
2) Laboratory of System Identification,
United Institute of Informatics Problems,
National Academy of Sciences of Belarus,
6 Surganov st, Minsk, Belarus
podenok@lsi-bas-net.by, http://uiip.bas-net.by
Segmentation method for subject processing the multi-spectral satellite images based on fuzzy clustering and
preliminary non-linear filtering is represented. Three fuzzy clustering algorithms, namely Fuzzy C-means, Gustafson-
Kessel, and Gath-Geva have been utilized. The experimental results obtained using these algorithms with and without
preliminary nonlinear filtering to segment multi-spectral Landsat images have approved that segmentation based on
fuzzy clustering provides good-looking discrimination of different land cover types. Implementations of Fuzzy Cmeans,
Gustafson-Kessel, and Gath-Geva algorithms have got linear computational complexity depending on initial
cluster amount and image size for single iteration step. They assume internal parallel implementation. The preliminary
processing of source channels with nonlinear filter provides more clear cluster discrimination and has as a consequence
more clear segment outlining…
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