Fuzzy Neighbor Research Articles

Unsupervised Segmentation Method of Multicomponent Images based on Fuzzy Connectivity Analysis in the Multidimensional Histograms

Image segmentation denotes a process for partitioning an image into distinct regions, it plays an important role in interpretation and decision making. A large variety of segmentation methods has been developed; among them, multidimensional histogram methods have been investigated but their implementation stays difficult due to the big size of histograms. We present an original method for segmenting n-D (where n is the number of components in image) images or multidimensional images in an unsupervised way using a fuzzy neighbourhood model. It is based on the hierarchical analysis of full n-D compact histograms integrating a fuzzy connected components labelling algorithm that we have realized in this work. Each peak of the histo- gram constitutes a class kernel, as soon as it encloses a number of pixels greater than or equal to a secondary arbitrary threshold knowing that a first threshold was set to define the degree of binary fuzzy similarity be- tween pixels. The use of a lossless compact n-D histogram allows a drastic reduction of the memory space necessary for coding it. As a consequence, the segmentation can be achieved without reducing the colors population of images in the classification step. It is shown that using n-D compact histograms, instead of 1-D and 2-D ones, leads to better segmentation results. Various images were segmented; the evaluation of the quality of segmentation in supervised and unsupervised of segmentation method proposed compare to the classification method k-means gives better results. It thus highlights the relevance of our approach, which can be used for solving many problems of segmentation.

Orthogonal Fuzzy Neighborhood Discriminant Analysis for Multifunction Myoelectric Hand Control

Developing accurate and powerful electromyogram (EMG) driven prostheses controllers that can provide the amputees with effective control on their artificial limbs, has been the focus of a great deal of research in the past few years. One of the major challenges in such research is extracting an informative subset of features that can best discriminate between the different forearm movements. In this paper, a new dimensionality reduction method, referred to as orthogonal fuzzy neighborhood discriminant analysis (OFNDA), is proposed as a response to such a challenge. Unlike existing attempts in fuzzy linear discriminant analysis, the objective of the proposed OFNDA is to minimize the distance between samples that belong to the same class and maximize the distance between the centers of different classes, while taking into account the contribution of the samples to the different classes. The proposed OFNDA is validated on EMG datasets collected from seven subjects performing a range of 5 to 10 classes of forearm movements. Practical results indicate the significance of OFNDA in comparison to many other feature projection methods (including locality preserving and uncorrelated variants of discriminant analysis) with accuracies ranging from 97.66% to 87.84% for 5 to 10 classes of movements, respectively, using only two EMG electrodes.

Comparative clustering analysis of bispectral index series of brain activity

Bispectral index scale (BIS) is a continuous processed electroencephalogram (EEG) parameter that correlates to the patient’s level of brain activity, where 100 is awake and 0 (flat line) is dead. BIS was designed to correlate with “hypnotic” clinical endpoints (sedation, lack of awareness, and memory) and to track changes in the effects of anesthetics on the brain. In this study, an approach to utilize clustering methods is investigated in the analysis of BIS series data. Fuzzy c-Means (The FCM) and Fuzzy Neighborhood DBSCAN (FN-DBSCAN) algorithms are handled in the paper. The FN-DBSCAN algorithm is advantageous in such a way that it aggregates the speed of the well-known Density Based Spatial Clustering of Applications with Noise (DBSCAN) and the robustness of the Noise-Robust Fuzzy Joint Points (NRFJP) algorithms. As a result of the computational experiments, we can conclude that FN-DBSCAN method gives more realistic results to recognize the stable duration intervals and the BIS stages in the measurement series.

Robustness of density-based clustering methods with various neighborhood relations

Cluster analysis is one of the most crucial techniques in statistical data analysis. Among the clustering methods, density-based methods have great importance due to their ability to recognize clusters with arbitrary shape. In this paper, robustness of the clustering methods is handled. These methods use distance-based neighborhood relations between points. In particular, DBSCAN (density-based spatial clustering of applications with noise) algorithm and FN-DBSCAN (fuzzy neighborhood DBSCAN) algorithm are analyzed. FN-DBSCAN algorithm uses fuzzy neighborhood relation whereas DBSCAN uses crisp neighborhood relation. The main characteristic of the FN-DBSCAN algorithm is that it combines the speed of the DBSCAN and robustness of the NRFJP (noise robust fuzzy joint points) algorithms. It is observed that the FN-DBSCAN algorithm is more robust than the DBSCAN algorithm to datasets with various shapes and densities.

A Novel Approach to Modeling and Flooding in Ad-hoc Wireless Networks

This study proposes a new modeling approach for wireless ad-hoc networks. The new approach is based on the construction of fuzzy neighborhoods and essentially consists of assigning a membership or importance degree to each network radio link which reflects the relative quality of this link. This approach is first used to model the flooding problem and then an algorithm is proposed to solve this problem which is of a great importance in ad-hoc wireless networks intrinsically subject to a certain level of node mobility. Simulations carried out in a dynamic environment show promising results and stability compared to the enhanced dominant pruning algorithm. Such an approach is suitable to take into account the volatile aspect of radio links and the physical layer uncertainty when modeling these networks, particularly when the physical layer offers no or insufficient guaranties to high-level protocols as for the flooding.

Fuzzy Neighborhood and Meta-heuristics based Approach to Truck Delivery Problem

Fuzzy Neighborhood and Meta-heuristics based Approach to Truck Delivery Problem

An MRF spatial fuzzy clustering method for fMRI SPMs

The paper presents a method for spatial fuzzy clustering (SFC) via Markov Random Fields (MRF) for the detection of brain activation regions in Functional Magnetic Resonance Imaging (fMRI) statistical parametric maps (SPMs) to improve the accuracy of the detection of such regions. The fMRI SPM is assumed to be an MRF and we define a fuzzy neighborhood energy function to describe the interaction between neighboring voxels. The final labeling is determined by a joint fuzzy membership. We compare the proposed spatial fuzzy clustering technique with the usual voxel-wise thresholding, traditional fuzzy clustering and Contextual Clustering (CC) [E. Salli, H.J. Aronen, S. Savolainen, A. Korvenoja, A. Visa, Contextual clustering for analysis of functional MRI data, IEEE Transactions on Medical Imaging 20 (2001) 403–414]. Experiments based on synthetic and real fMRI data demonstrate that the clustering performance of our method is significantly better than both simple thresholding and conventional non-spatial fuzzy clustering techniques. Our experiments also show that in relatively high quality SPMs (contrast to noise ratio ( CNR ) > 2.5 ), the performance of SFC and CC is very similar. In the case of the simulated datasets, when the SPMs have poor quality ( CNR < 2.5 ), our method outperforms CC in reducing false positives and improving classification accuracy.

Support vector machine classifier based on fuzzy partition and neighborhood pairs

Support vector machine classifier based on fuzzy partition and neighborhood pairs

Double Fuzzy Preproximity Spaces

In this paper, we introduce the concept of double fuzzy preproximity spaces as a generalization of a fuzzy preproximity spaces and investigate some of their properties. Also we study the relationships between double fuzzy preproximity spaces, double fuzzy topological spaces and double fuzzy closure spaces. In addition to this was the introduction of the concept of double fuzzy neighborhood system and has been studying the connection with double fuzzy preproximity, which resulted in the definition of the concept double fuzzy preproximal neighborhood.

A fuzzy neighborhood-based training algorithm for feedforward neural networks

In this work we present a new hybrid algorithm for feedforward neural networks, which combines unsupervised and supervised learning. In this approach, we use a Kohonen algorithm with a fuzzy neighborhood for training the weights of the hidden layers and gradient descent method for training the weights of the output layer. The goal of this method is to assist the existing variable learning rate algorithms. Simulation results show the effectiveness of the proposed algorithm compared with other well-known learning methods.

Binary object extraction using bi-directional self-organizing neural network (BDSONN) architecture with fuzzy context sensitive thresholding

A novel neural network architecture suitable for image processing applications and comprising three interconnected fuzzy layers of neurons and devoid of any back-propagation algorithm for weight adjustment is proposed in this article. The fuzzy layers of neurons represent the fuzzy membership information of the image scene to be processed. One of the fuzzy layers of neurons acts as an input layer of the network. The two remaining layers viz. the intermediate layer and the output layer are counter-propagating fuzzy layers of neurons. These layers are meant for processing the input image information available from the input layer. The constituent neurons within each layer of the network architecture are fully connected to each other. The intermediate layer neurons are also connected to the corresponding neurons and to a set of neighbors in the input layer. The neurons at the intermediate layer and the output layer are also connected to each other and to the respective neighbors of the corresponding other layer following a neighborhood based connectivity. The proposed architecture uses fuzzy membership based weight assignment and subsequent updating procedure. Some fuzzy cardinality based image context sensitive information are used for deciding the thresholding capabilities of the network. The network self organizes the input image information by counter-propagation of the fuzzy network states between the intermediate and the output layers of the network. The attainment of stability of the fuzzy neighborhood hostility measures at the output layer of the network or the corresponding fuzzy entropy measures determine the convergence of the network operation. An application of the proposed architecture for the extraction of binary objects from various degrees of noisy backgrounds is demonstrated using a synthetic and a real life image.

A note on fuzzy neighbourhood base spaces

Fuzzy neighbourhood base spaces ( FNBS), introduced by Morsi, are a special type of stratified I-topological spaces ( SI- TOP), previously denoted as fuzzy topological spaces ( FTS). We introduce further characterizations of FNBS in terms of level sets. Also, we construct stable subconstructs of SI- TOP, in the sense of Lowen and Wuyts [Stable subconstructs of FTS: I, J. Fuzzy Math. 1(3) (1993) 475–489], whose corresponding total stratified I-topologies are generated by elements of I I which are greater than id I . The maximal objects of such stable subconstructs have weaker properties than that of the ascending chain property, and then these objects give rise to fuzzy neighbourhood base spaces. Therefore, special types of fuzzy neighbourhood base spaces are constructed.

On initial and final fuzzy uniform structures, Part II

This paper is the second part and continuation of a paper for the author published in 2003 and investigating initial fuzzy uniform structures. The final fuzzy uniform structures and the final global fuzzy neighborhood structures, for the notions of fuzzy uniform structure and of global fuzzy neighborhood structure introduced by the author and others in 1998 in two separate papers, are characterized. This paper also shows that the expected relations between the final fuzzy uniform structures and the final fuzzy topologies and the final global fuzzy neighborhood structures are indeed true.

N‐topo nilpotency in fuzzy neighborhood rings

We introduce the notion of N‐topo nilpotent fuzzy set in a fuzzy neighborhood ring and develop some fundamental results. Here we show that a fuzzy neighborhood ring is locally inversely bounded if and only if for all 0 &lt; α &lt; 1, the α‐level topological rings are locally inversely bounded. This leads us to prove a characterization theorem which says that if a fuzzy neighborhood ring on a division ring is Wuyts‐Lowen WNT2 and locally inversely bounded, then the fuzzy neighborhood ring is a fuzzy neighborhood division ring. We also present another characterization theorem which says that a fuzzy neighborhood ring on a division ring is a fuzzy neighborhood division ring if the fuzzy neighborhood ring contains an N‐topo nilpotent fuzzy neighborhood of zero.

Proximity measures for terms based on fuzzy neighborhoods in document sets

Two types of proximity measures between two terms are defined using a fuzzy neighborhood in a sequence of terms of a document set, where the concept of neighborhood is borrowed from generalized rough sets. These proximity measures are a refinement of similarity measures based on the term-document cooccurrence matrix for associating and clustering term and/or documents. Symmetric and nonsymmetric proximity measures are considered and theoretical properties of them are investigated. Normalization and symmetrization of the measures are also considered. Illustrative examples including bibliographic citations are given.

On concretely both reflective and coreflective subconstructs of Chang fuzzy topological spaces

In this paper, the concretely both reflective and coreflective subconstructs of the construct CFTS of Chang fuzzy topological spaces are characterized and their relationship to such subconstructs of stratified fuzzy topological spaces is investigated. Properties of a particular such subconstruct of CFTS, the construct of weak fuzzy neighborhood spaces, are discussed in detail. And it is proved that there exists exactly c such subconstructs between the subconstruct of weakly induced spaces and CFTS.

On initial and final fuzzy uniform structures

This paper is devoted to study the initial and final fuzzy uniform structures of the fuzzy uniform structure defined by the author and others in 1998. In this paper, we show that all initial and final lifts in the category FUN of these fuzzy uniform spaces and hence the initial and final fuzzy uniform structures exist. We introduce a characterization for the initial fuzzy uniform structures and as a special initial fuzzy uniform spaces the subspaces and product spaces of these fuzzy uniform spaces can be also characterized. We also show that the fuzzy topology (global homogeneous fuzzy neighborhood structure) associated to the initial fuzzy uniform structure of a family of fuzzy uniform structures coincides with the initial fuzzy topology (initial global homogeneous fuzzy neighborhood structure) of the family of fuzzy topologies (global homogeneous fuzzy neighborhood structures) associated to these fuzzy uniform structures.

On the Relationship Between Several Basic Categories in Fuzzy Topology

In this paper, a process, which associates with every fibre-small topo- logical category A and every completely distributive lattice L another topological category called the co-tower extension of A, is introduced to investigate the relationship between several basic categories in fuzzy topology. It is proved that the category of stratified Šostak L-fuzzy topological spaces is the co-tower extension of the category of stratified Chang-Goguen spaces; and the category of L-fuzzifying topological spaces (which is isomorphic the category of fuzzy neighborhood spaces when L = [0, 1]) is the co-tower extension of the category of topological spaces. Thus, in some sense, we can say that properties of the category of stratified Šostak L-fuzzy topological spaces and L-fuzzifying topological spaces are determined by the categories of stratified Chang-Goguen spaces, topological spaces and the lattice L. Therefore it can be said that in fuzzy topology the category of (stratified) Chang-Goguen topological spaces is more basic and more important than the other categories.

Reduction operations in fuzzy or valued constraint satisfaction

In the constraint satisfaction problem (CSP) knowledge about a relation on n variables is given in the form of constraints on subsets of the variables. A solution to the CSP is simply an instantiation of the n variables which satisfies all the constraints. The fuzzy constraint satisfaction problem (FCSP) is a generalisation of the CSP to the case in which the constraints are not categorical but represent preferences in the form of fuzzy sets. The FCSP is then an optimisation problem in the space of all possible instantiations of the n variables. It has potentially many more applications in real-world problems than the CSP. When the aim is to maximise the value of the most violated constraint, then the FCSP can be solved by solving a logarithmic number of CSPs. Fuzzy k-consistency can even be established by an algorithm whose worst-case complexity is identical to that of an optimal k-consistency algorithm for CSPs. When the operator used to aggregate constraint values is strictly monotonic (as is the case in MAX-CSP, for example) the classic operation of arc consistency has to be completely redefined. We show that an ordered version of arc consistency exists for all FCSPs with a strictly monotonic aggregation operator, and that, in the case of MAX-CSP, it can be established by an algorithm whose worst-case complexity is identical to that of the optimal arc consistency algorithm for CSPs. Neighbourhood substitution in CSPs can be generalised to fuzzy neighbourhood substitution in FCSPs, whether the aggregation operator is strictly monotonic or idempotent. We give an algorithm for applying fuzzy neighbourhood substitutions until convergence based on the corresponding algorithm for CSPs.

A generic fuzzy rule based image segmentation algorithm

Fuzzy rule based image segmentation techniques tend in general, to be application dependent with the structure of the membership functions being predefined and in certain cases, the corresponding parameters being manually determined. The net result is that the overall performance of the segmentation technique is very sensitive to parameter value selections. This paper addresses these issues by introducing a generic fuzzy rule based image segmentation (GFRIS) algorithm, which is both application independent and exploits inter-pixel spatial relationships. The GFRIS algorithm automatically approximates both the key weighting factor and threshold value in the definitions of the fuzzy rule and neighbourhood system, respectively. A quantitative evaluation is presented between the segmentation results obtained using GFRIS and the popular fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms. The results demonstrate that GFRIS exhibits a considerable improvement in performance compared to both FCM and PCM, for many different image types.