performance modeling and algorithm characterization for robust image segmentation this paper presents a probabilistic framework based on bayesian theory for the performance prediction and selection of an optimal segmentation algorithm the framework models the optimal algorithm selection process as one that accounts for the information content of an input image as well as the behavioral properties of a particular candidate segmentation algorithm the input image information content is measured in terms of image features while the candidate segmentation algorithm s behavioral characteristics are captured through the use of segmentation quality features gaussian probability distribution models are used to learn the required relationships between the extracted image and algorithm features and the framework tested on the berkeley segmentation dataset using four candidate segmentation algorithms

fuzzy homogeneity and scale space approach to color image segmentation image segmentation is the procedure in which the original image is partitioned into homogeneous regions and has many applications in this paper a fuzzy homogeneity and scale space approach to color image segmentation is proposed a color image is transformed into fuzzy domain with maximum fuzzy entropy principle the fuzzy homogeneity histogram is employed and both global and local informations are considered when we process fuzzy homogeneity histogram the scale space filter is utilized for analyzing the fuzzy homogeneity histogram to find the appropriate segments of the homogeneity histogram bounded by the local extrema of the derivatives a fuzzy region merging process is then implemented based on color difference and cluster sizes to avoid over segmentation the proposed method is compared with the space domain approach and experimental results demonstrate the effectiveness of the proposed approach

two group sir epidemic model with stochastic perturbation a stochastic two group sir model is presented in this paper the existence and uniqueness of its nonnegative solution is obtained and the solution belongs to a positively invariant set furthermore the globally asymptotical stability of the disease free equilibrium is deduced by the stochastic lyapunov functional method if r 0 1 which means the disease will die out while if r 0 1 we show that the solution is fluctuating around a point which is the endemic equilibrium of the deterministic model in time average in addition the intensity of the fluctuation is proportional to the intensity of the white noise when the white noise is small we consider the disease will prevail at last we illustrate the dynamic behavior of the model and their approximations via a range of numerical experiments

segmentation of color textures this paper describes an approach to perceptual segmentation of color image textures a multiscale representation of the texture image generated by a multiband smoothing algorithm based on human psychophysical measurements of color appearance is used as the input initial segmentation is achieved by applying a clustering algorithm to the image at the coarsest level of smoothing the segmented clusters are then restructured in order to isolate core clusters i e patches in which the pixels are definitely associated with the same region the image pixels representing the core clusters are used to form 3d color histograms which are then used for probabilistic assignment of all other pixels to the core clusters to form larger clusters and categorise the rest of the image the process of setting up color histograms and probabilistic reassignment of the pixels to the clusters is then propagated through finer levels of smoothing until a full segmentation is achieved at the highest level of resolution

global dynamics of a reaction diffusion waterborne pathogen model with general incidence rate abstract in this paper we first propose a reaction diffusion waterborne pathogen model with general incidence rate incorporating both direct and indirect transmission pathways then using the basic reproduction number we investigate the global dynamical behaviors of the continuous model by nonstandard finite difference scheme we derive a discrete counterpart of the continuous model then the global properties of the discretized model are investigated finally we conclude the paper by an example and numerical simulations

extraction of tumor region in color images using wavelets abstract in this paper a wavelet based method is proposed to extract the tumor region from a color medical image in the past the diagnosis of tumors usually relied upon the tissue section stained by methylene blue however traditional approaches have relied primarily on manual processes and erroneous assessment may arise from subjective judgement and eye measurement instead of the previous method we propose an improved radial search technique to increase the accuracy of the extraction of the tumor border experimental results show that the proposed method is fast and can extract the tumor region successfully moreover the results of this research will be helpful in the development of a computer aided image analysis system that will automate visual feature extraction for tumor evaluation

further results on state estimation for neural networks of neutral type with time varying delay in this paper further result on design problem of state estimator for a class of neural networks of neutral type is presented a delay dependent linear matrix inequality lmi criterion for existence of the estimator is derived a numerical simulation is given to show the effectiveness of proposed estimator

h control of discrete time linear systems with norm bounded uncertainties and time delay in state this paper deals with h control problem for discrete time linear systems with norm bounded uncertainties and time delay in state in terms of a modified riccati inequality for discrete time linear systems a sufficient condition is presented and a state feedback control law is also given the proposed control law is a memoryless state feedback control law

passivity analysis for discrete time stochastic markovian jump neural networks with mixed time delays in this paper passivity analysis is conducted for discrete time stochastic neural networks with both markovian jumping parameters and mixed time delays the mixed time delays consist of both discrete and distributed delays the markov chain in the underlying neural networks is finite piecewise homogeneous by introducing a lyapunov functional that accounts for the mixed time delays a delay dependent passivity condition is derived in terms of the linear matrix inequality approach the case of markov chain with partially unknown transition probabilities is also considered all the results presented depend upon not only discrete delay but also distributed delay a numerical example is included to demonstrate the effectiveness of the proposed methods

output feedback stabilization for a discrete time system with a time varying delay this study employs the free weighting matrix approach to investigate the output feedback control of a linear discrete time system with an interval time varying delay first the delay dependent stability is analyzed using a new method of estimating the upper bound on the difference of a lyapunov function without ignoring any terms and based on the results a design criterion for a static output feedback sof controller is derived since the conditions thus obtained for the existence of admissible controllers are not expressed strictly in terms of linear matrix inequalities a modified cone complementarity linearization algorithm is employed to solve the nonconvex feasibility sof control problem furthermore the problem of designing a dynamic output feedback controller is formulated as one of designing an sof controller numerical examples demonstrate the effectiveness of the method and its advantage over existing methods

technical communique robust h observer design of linear state delayed systems with parametric uncertainty the discrete time case in this note we consider the problem of h observer design for a class of uncertain linear discrete time systems with delayed state and parameter uncertainties the goal of this problem is to design a linear state observer such that for the positive integer state time delay and all admissible norm bounded parameter uncertainties the observation process remains robustly stable and the transfer function from exogenous disturbances to error state outputs meets the prespecified h norm upper bound constraint the observer structure does not depend on the parameter uncertainties a simple effective algebraic methodology is developed to derive the conditions for the existence of the desired robust h observers and the analytical expression of these observers is then characterized in terms of the matrix riccati like equations inequalities we provide a numerical example to demonstrate the validity and the applicability of the proposed approach

multiresolution color image segmentation image segmentation is the process by which an original image is partitioned into some homogeneous regions in this paper a novel multiresolution color image segmentation mcis algorithm which uses markov random fields mrf s is proposed the proposed approach is a relaxation process that converges to the map maximum a posteriori estimate of the segmentation the quadtree structure is used to implement the multiresolution framework and the simulated annealing technique is employed to control the splitting and merging of nodes so as to minimize an energy function and therefore maximize the map estimate the multiresolution scheme enables the use of different dissimilarity measures at different resolution levels consequently the proposed algorithm is noise resistant since the global clustering information of the image is required in the proposed approach the scale space filter ssf is employed as the first step the multiresolution approach is used to refine the segmentation experimental results of both the synthesized and real images are very encouraging in order to evaluate experimental results of both synthesized images and real images quantitatively a new evaluation criterion is proposed and developed

novel delay dependent master slave synchronization criteria of chaotic lur e systems with time varying delay feedback control this paper is concerned with the issue of master slave synchronization for chaotic lur e systems clss with time varying delay feedback control a novel integral inequality is developed by employing two adjustable parameters which encompasses the celebrated wirtinger s integral inequality and jensen s inequality as two special cases by constructing an augmented lyapunov krasovskii functional lkf taking fully the information time varying delay range into account less conservative delay dependent synchronization criteria are established in the form of linear matrix inequalities lims besides the desired controller gain can be achieved by introducing new nonlinear function conditions which have not been proposed so far finally a numerical example of typical chua s circuit is presented to show the improvements over the existing criteria and the effectiveness of the design approach

further improvements in feature weighted fuzzy c means in cluster analysis certain features of a given data set may exhibit higher relevance than others to address this issue feature weighted fuzzy c means fwfcm approaches have emerged in recent years however there are certain deficiencies in the existing fwfcms e g the elements in a feature weight vector cannot be adaptively adjusted during the training phase and the update formulas of a feature weight vector cannot be derived analytically in this study an improved fwfcm ifwfcm is proposed to overcome these shortcomings the ifwfcm kd based on the kernelized distance is also proposed experimental results reported for five numerical data sets and the color images show that ifwfcm is superior to the existing fwfcms an interesting conclusion that ifwfcm kd might not improve the performance of ifwfcm is also obtained by applying ifwfcm kd to tackle the above mentioned numerical data sets and color images

stability analysis for a class of neutral type singular systems with time varying delay abstract this paper is concerned with the stability problem for a class of neutral type singular systems with time varying delay the considered systems contain delays both in their state and in their derivatives of state based on the singular system approach and the lyapunov krasovskii functional approach some sufficient conditions which guarantee the considered systems to be regular impulse free and stable are derived finally some numerical examples are provided to show the effectiveness of the presented methods

color image segmentation based on 3 d clustering morphological approach in this paper a new segmentation algorithm for color images based on mathematical morphology is presented color image segmentation is essentially a clustering process in 3 d color space but the characteristics of clusters vary severely according to the type of images and color coordinates hence the methodology employs the scheme of thresholding the difference of gaussian smoothed 3 d histogram to get the initial seeds for clustering and then uses a closing operation and adaptive dilation to extract the number of clusters and their representative values and to include the suppressed bins during gaussian smoothing without a priori knowledge on the image this procedure also implicitly takes into account the statistical properties such as the shape connectivity and distribution of clusters intensive computer simulation has been performed and the results are discussed in this paper the results of the simulation show that the proposed segmentation algorithm is independent of the choice of color coordinates the shape of clusters and the type of images the segmentation results using the k means technique are also presented for comparison purposes

further improvement of free weighting matrices technique for systems with time varying delay a novel method is proposed in this note for stability analysis of systems with a time varying delay appropriate lyapunov functional and augmented lyapunov functional are introduced to establish some improved delay dependent stability criteria less conservative results are obtained by considering the additional useful terms which are ignored in previous methods when estimating the upper bound of the derivative of lyapunov functionals and introducing the new free weighting matrices the resulting criteria are extended to the stability analysis for uncertain systems with time varying structured uncertainties and polytopic type uncertainties numerical examples are given to demonstrate the effectiveness and the benefits of the proposed method

color images segmentation using scale space filter and markov random field a new hybrid method is presented that combines the scale space filter ssf and markov random field mrf for color image segmentation the fundamental idea of the ssf is to use the convolution of gaussian functions and image histogram to generate a scale space image and then find the proper interval bounded by the local extrema of the derivatives the gaussian function is with zero mean and varied standard deviation using the ssf the different scaled histogram is separated into intervals corresponding to peaks and valleys the mrf makes use of the property that each pixel in an image has some relationship with other pixels the basic construction of an mrf is a joint probability given the original data the original data is the image that is obtained from the source and the result is called the label image because the mrf needs a number of segments before it converges to the global minimum the ssf is exploited to do coarse segmentation cs and then mrf is used to do fine segmentation fs of the images basically the former is histogram based segmentation whereas the latter is neighborhood based segmentation finally experimental results obtained from using ssf alone mrf using iterated conditional mode icm and mrf using gibbs sampling are compared

a dynamic niching clustering algorithm based on individual connectedness and its application to color image segmentation in this paper a dynamic niching clustering algorithm based on individual connectedness dnic is proposed for unsupervised classification with no prior knowledge it aims to automatically evolve the optimal number of clusters as well as the cluster centers of the data set based on the proposed adaptive compact k distance neighborhood algorithm more specifically with the adaptive selection of the number of the nearest neighbor and the individual connectedness algorithm dnic often achieves several sets of connecting individuals and each set composes an independent niche in practice each set of connecting individuals corresponds to a homogeneous cluster and this ensures the separability of an arbitrary data set theoretically an application of the dnic clustering algorithm in color image segmentation is also provided experimental results demonstrate that the dnic clustering algorithm has high performance and flexibility highlightsthe dnic aims to automatically evolve the number of clusters and the cluster centers a simpler representation is adopted that each individual represents a single center an algorithm is proposed to select the optimal number of the neighbor automatically an individual connectedness algorithm is proposed to dynamic identify the niches the dynamic niching is accomplished without assuming any a priori knowledge

reliable anti synchronization conditions for bam memristive neural networks with different memductance functions this paper is concerned with anti synchronization results for a class of memristor based bidirectional associate memory bam neural networks with different memductance functions and time varying delays based on drive response system concept differential inclusions theory and lyapunov stability theory some sufficient conditions are obtained to guarantee the reliable asymptotic anti synchronization criterion for memristor based bam networks the memristive bam neural network is formulated for two types of memductance functions sufficient results are derived in terms of linear matrix inequalities lmis finally the effectiveness of the proposed criterion is demonstrated through numerical example

multiscale gradient watersheds of color images we present a new framework for the hierarchical segmentation of color images the proposed scheme comprises a nonlinear scale space with vector valued gradient watersheds our aim is to produce a meaningful hierarchy among the objects in the image using three image components of distinct perceptual significance for a human observer namely strong edges smooth segments and detailed segments the scale space is based on a vector valued diffusion that uses the additive operator splitting numerical scheme furthermore we introduce the principle of the dynamics of contours in scale space that combines scale and contrast information the performance of the proposed segmentation scheme is presented via experimental results obtained with a wide range of images including natural and artificial scenes

a less conservative robust stability test for linear uncertain time delay systems this note concerns with the robust stability of linear uncertain systems with state delay the uncertainty is assumed to be of polytopic type a less conservative delay dependent linear matrix inequality lmi method is presented based on a new lyapunov krasovskii functional the present method incorporates a relaxed parameter dependent technique combined with a recently proposed idea of introducing free weighting matrices when confined to delay free case the present result is also less conservative than existing stability tests using parameter dependent methods numerical examples are given to show the less conservativeness of the results

dissipativity analysis for singular systems with time varying delays this paper is concerned with the dissipativity analysis problem for singular systems with time varying delays a delay dependent criterion is established to guarantee the dissipativity of the underlying systems using the delay partitioning technique all the results given in this paper are not only dependent upon the time delay but also dependent upon the number of delay partitions the effectiveness and the reduced conservatism of the derived results are demonstrated by two illustrative examples

a stochastic model for internal hiv dynamics in this paper we analyse a stochastic model representing hiv internal virus dynamics the stochasticity in the model is introduced by parameter perturbation which is a standard technique in stochastic population modelling we show that the model established in this paper possesses non negative solutions as this is essential in any population dynamics model we also carry out analysis on the asymptotic behaviour of the model we approximate one of the variables by a mean reverting process and find out the mean and variance of this process numerical simulations conclude the paper

region oriented compression of color images using fuzzy inference and fast merging a new region based lossy compression scheme for color images is proposed the segmentation method belongs to the split and merge category splitting is carried out using the watershed transform in the merging stage a fuzzy color preserving rule based system and a novel one dimensional graph structure are introduced to provide accurate results with reduced computational complexity the compression part is based on the shape adaptive dct with dc correction method the quantization matrices used have been designed according to the properties of the employed transform promising perceptual results for the low bit rate range compared to previously reported compression methods have also been reported

an algorithmic introduction to numerical simulation of stochastic differential equations a practical and accessible introduction to numerical methods for stochastic differential equations is given the reader is assumed to be familiar with euler s method for deterministic differential equations and to have at least an intuitive feel for the concept of a random variable however no knowledge of advanced probability theory or stochastic processes is assumed the article is built around 10 matlab programs and the topics covered include stochastic integration the euler maruyama method milstein s method strong and weak convergence linear stability and the stochastic chain rule

wirtinger based multiple integral inequality for stability of time delay systems abstractnote that the conservatism of the delay dependent stability criteria can be reduced by increasing the integral terms in lyapunov krasovskii functional lkf this brief revisits the stability problem for a class of linear time delay systems via multiple integral approach the novelty of this brief lies in that a wirtinger based multiple integral inequality is employed to estimate the derivative of a class of lkf with multiple integral terms based on these innovations a new delay dependent stability criterion is derived in terms of linear matrix inequalities two numerical examples are exploited to demonstrate the effectiveness and superiority of the proposed method

smooth dynamic output feedback control for multiple time delay systems with nonlinear uncertainties the dynamic output feedback control problem is considered for a class of systems with multiple time delays and nonlinear uncertainties based on the control input matrix and output matrix we decompose the system into two subsystems the dynamic compensator is designed for the first subsystem and then the output feedback controller is constructed based on the compensator and the second subsystem by using the introduced new lyapunov krasovskii functional we show that the solution of the resultant closed loop system converges exponentially to an adjustable bounded region compared with the previous works the developed controller in this paper is memoryless and smooth which only uses the system output the control design conditions are relaxed because of the developed dynamic compensator the result is further extended to the general nonlinear case the corresponding dynamic nonlinear output feedback control method is proposed finally simulations are performed to show the potential of the proposed methods

pyramid segmentation algorithms revisited the main goal of this work is to compare pyramidal structures proposed to solve segmentation tasks segmentation algorithms based on regular and irregular pyramids are described together with the data structures and decimation procedures which encode and manage the information in the pyramid in order to compare the different segmentation algorithms we have employed three types of quality measurements the shift variance measure the f function and the q function

generalized flooding and multicue pde based image segmentation image segmentation remains an important but hard to solve problem since it appears to be application dependent with usually no a priori information available regarding the image structure moreover the increasing demands of image analysis tasks in terms of segmentation results quality introduce the necessity of employing multiple cues for improving image segmentation results in this paper we attempt to incorporate cues such as intensity contrast region size and texture in the segmentation procedure and derive improved results compared to using individual cues separately we emphasize on the overall segmentation procedure and we propose efficient simplification operators and feature extraction schemes capable of quantifying important characteristics like geometrical complexity rate of change in local contrast variations and orientation that eventually favor the final segmentation result based on the well known morphological paradigm of watershed transform segmentation which exploits intensity contrast and region size criteria we investigate its partial differential equation pde formulation and we extend it in order to satisfy various flooding criteria thus making it applicable to a wider range of images going a step further we introduce a segmentation scheme that couples contrast criteria in flooding with texture information the modeling of the proposed scheme is done via pdes and the efficient incorporation of the available contrast and texture information is done by selecting an appropriate cartoon texture image decomposition scheme the proposed coupled segmentation scheme is driven by two separate image components artoon u for contrast information and texture component the performance of the proposed segmentation scheme is demonstrated through a complete set of experimental results and substantiated using quantitative and qualitative criteria

quantization based clustering algorithm in this paper a quantization based clustering algorithm qbca is proposed to cluster a large number of data points efficiently unlike previous clustering algorithms qbca places more emphasis on the computation time of the algorithm specifically qbca first assigns the data points to a set of histogram bins by a quantization function then it determines the initial centers of the clusters according to this point distribution finally qbca performs clustering at the histogram bin level rather than the data point level we also propose two approaches to improve the performance of qbca further i a shrinking process is performed on the histogram bins to reduce the number of distance computations and ii a hierarchical structure is constructed to perform efficient indexing on the histogram bins finally we analyze the performance of qbca theoretically and experimentally and show that the approach 1 can be easily implemented 2 identifies the clusters effectively and 3 outperforms most of the current state of the art clustering approaches in terms of efficiency

a fuzzy region growing approach for segmentation of color images segmentation is one of the most important preprocessing steps towards pattern recognition and image understanding and a significant step towards image compression and coding with detecting edges most of the large segments can be found and separated from others by edge pixels it is however the pixels on edge locations or those in high detailed areas whose association to adjacent segments must be found a pixel can be a part of the closest segment or in association with the neighboring pixels from a new smaller segment in this paper two segmentation algorithms are presented one is used for fine segmentation towards compression and coding of images and the other for coarse segmentation towards other applications like object recognition and image understanding edge detection and region growing approaches are combined to find large and crisp segments for coarse segmentation segments can grow or expand based on two fuzzy criteria the fuzzy region growing and expanding approaches presented here use histogram tables for fine segmentation the procedures introduced here can be used in any order or combination to yield the best result for any particular application or image type

color image segmentation using histogram thresholding fuzzy c means hybrid approach this paper presents a novel histogram thresholding fuzzy c means hybrid htfcm approach that could find different application in pattern recognition as well as in computer vision particularly in color image segmentation the proposed approach applies the histogram thresholding technique to obtain all possible uniform regions in the color image then the fuzzy c means fcm algorithm is utilized to improve the compactness of the clusters forming these uniform regions experimental results have demonstrated that the low complexity of the proposed htfcm approach could obtain better cluster quality and segmentation results than other segmentation approaches that employing ant colony algorithm

sensitivity analysis and feedback control of noise induced extinction for competition chemostat model with mutualism abstract we consider a stochastic chemostat model involving two obligate mutualistic species feeding on a limiting substrate it is shown that the spatial arrangement of the random states near the deterministic coexistence equilibrium is enlarged as noise intensity increases more precisely in this paper based on the technique of stochastic sensitivity functions a confidence ellipse of the random states is constructed and a critical value for the noise intensity is established it is shown that a new dynamic phenomenon of noise induced extinction can be observed when the noise intensity passes the critical value we then propose some feedback control strategies which can reduce the size of the confidence ellipse so that to prevent the noise induced extinction

technical communique delay range dependent stability for systems with time varying delay this paper is concerned with the stability analysis for systems with time varying delay in a range an appropriate type of lyapunov functionals is proposed to investigate the delay range dependent stability problem the present results may improve the existing ones due to a method to estimate the upper bound of the derivative of lyapunov functional without ignoring some useful terms and the introduction of additional terms into the proposed lyapunov functional which take into account the range of delay numerical examples are given to demonstrate the effectiveness and the benefits of the proposed method

a stochastic sis epidemic model with saturating contact rate abstract in this paper we formulate and investigate the dynamics of a sis epidemic model with saturating contact rate and its corresponding stochastic differential equation version for the deterministic epidemic model we show that the disease free equilibrium is global asymptotically stable if the basic reproduction number is less than unity and if the basic reproduction number is greater than unity model 1 2 admits a unique endemic equilibrium which is locally asymptotically stable by analyzing the corresponding characteristic equations for the stochastic epidemic model the existence and uniqueness of the positive solution are proved by employing the lyapunov analysis method the basic reproduction number r 0 s is proved to be a sharp threshold which determines whether there is an endemic outbreak or not if r 0 s 1 and under mild extra conditions the disease can be eradicated almost surely whereas if r 0 s 1 it has a stationary distribution which leads to the stochastic persistence of the disease finally numerical simulations are presented to illustrate our theoretical results

global stability of multi group epidemic models with distributed delays abstract we investigate a class of multi group epidemic models with distributed delays we establish that the global dynamics are completely determined by the basic reproduction number r 0 more specifically we prove that if r 0 1 then the disease free equilibrium is globally asymptotically stable if r 0 1 then there exists a unique endemic equilibrium and it is globally asymptotically stable our proof of global stability of the endemic equilibrium utilizes a graph theoretical approach to the method of lyapunov functionals

hierarchy of stability criterion for time delay systems based on multiple integral approach abstract taking a class of time delay systems as research object this brief aims at developing a theoretical support on the hierarchy of stability criterion which is derived by the multiple integral approach and free weighting matrix technique the hierarchy implies that the conservatism of stability criterion can be reduced by increasing the ply of integral terms in lyapunov krasovskii functional lkf together with three numerical experiments the hierarchy of stability criterion is further shown

relaxation using models from quantum mechanics abstract we make an analogy between a single pixel and its first order neighbours in an image and single particle models from quantum physics the analogy is created by identifying the observed grey levels and the noise variance with the potential function and the energy of the physical system the approach is used to set up novel and strongly data dependent local weights in relaxation schemes both image restoration and segmentation are considered the new methods competed favourably with the besag icm and rosenfeld relaxation on several simulated and real images

novel summation inequalities and their applications to stability analysis for systems with time varying delay the inequality technique plays an important role in stability analysis for time delay systems this technical note presents a new sequence of novel summation inequalities by introducing some free matrices which includes the newly developed wirtinger based and free matrix based summation inequalities as special cases moreover the idea can be easily extended to the multiple summation inequality case based on the proposed inequalities relaxed stability conditions are obtained for systems with time varying delay numerical examples are given to demonstrate the effectiveness of the proposed approach

abel lemma based finite sum inequality and its application to stability analysis for linear discrete time delay systems this paper is concerned with stability of linear discrete time delay systems note that a tighter estimation on a finite sum term appearing in the forward difference of some lyapunov functional leads to a less conservative delay dependent stability criterion by using abel lemma a novel finite sum inequality is established which can provide a tighter estimation than the ones in the literature for the finite sum term applying this abel lemma based finite sum inequality a stability criterion for linear discrete time delay systems is derived it is shown through numerical examples that the stability criterion can provide a larger admissible maximum upper bound than stability criteria using a jensen type inequality approach and a free weighting matrix approach

a stochastic method for bayesian estimation of hidden markov random field models with application to a color model we propose a new stochastic algorithm for computing useful bayesian estimators of hidden markov random field hmrf models that we call exploration selection estimation ese procedure the algorithm is based on an optimization algorithm of o francois called the exploration selection e s algorithm the novelty consists of using the a posteriori distribution of the hmrf as exploration distribution in the e s algorithm the ese procedure computes the estimation of the likelihood parameters and the optimal number of region classes according to global constraints as well as the segmentation of the image in our formulation the total number of region classes is fixed but classes are allowed or disallowed dynamically this framework replaces the mechanism of the split and merge of regions that can be used in the context of image segmentation the procedure is applied to the estimation of a hmrf color model for images whose likelihood is based on multivariate distributions with each component following a beta distribution meanwhile a method for computing the maximum likelihood estimators of beta distributions is presented experimental results performed on 100 natural images are reported we also include a proof of convergence of the e s algorithm in the case of nonsymmetric exploration graphs

unsupervised hierarchical image segmentation through fuzzy entropy maximization we present an unsupervised multilevel segmentation scheme for automatically segmenting grayscale and color images fuzzy 2 partition entropy is combined with graph cut to form a bi level segmentation operator that splits a given region into 2 parts based on both global optimal threshold and local spatial coherence a multilevel segmentation scheme iteratively performs on selected regions and color channels producing a coarse to fine hierarchy of segments the presented algorithm is evaluated using the berkeley segmentation database and achieves competitive results compared with the state of the art methods the fuzzy c partition entropy has been widely adopted as a global optimization technique for finding the optimal thresholds when performing multilevel gray image segmentation nevertheless existing fuzzy c partition entropy approaches generally have two limitations i e partition number cneeds to be manually tuned for different input and the methods can process grayscale images only to address these two limitations an unsupervised multilevel segmentation algorithm is presented in this paper the core step of our algorithm is a bi level segmentation operator which uses binary graph cuts to maximize both fuzzy 2 partition entropy and segmentation smoothness by iteratively performing this bi level segmentation operator multilevel image segmentation is achieved in a hierarchical manner starting from the input color image our algorithm first picks the color channel that can best segment the image into two labels and then iteratively selects channels to further split each labels until convergence the experimental results demonstrate the presented hierarchical segmentation scheme can efficiently segment both grayscale and color images quantitative evaluations over classic gray images and the berkeley segmentation database show that our method is comparable to the state of the art multi scale segmentation methods yet has the advantage of being unsupervised efficient and easy to implement

boundary detection by constrained optimization a statistical framework is used for finding boundaries and for partitioning scenes into homogeneous regions the model is a joint probability distribution for the array of pixel gray levels and an array of labels in boundary finding the labels are binary zero or one representing the absence or presence of boundary elements in partitioning the label values are generic two labels are the same when the corresponding scene locations are considered to belong to the same region the distribution incorporates a measure of disparity between certain spatial features of block pairs of pixel gray levels using the kolmogorov smirnov nonparametric measures of difference between the distributions of these features the number of model parameters is minimized by forbidding label configurations which are assigned probability zero the maximum a posteriori estimator of boundary placements and partitionings is examined the forbidden states introduce constraints into the calculation of these configurations stochastic relaxation methods are extended to accommodate constrained optimization

color image segmentation using adaptive mean shift and statistical model based methods in this paper we propose an unsupervised segmentation algorithm for color images based on gaussian mixture models gmms the number of mixture components is determined automatically by adaptive mean shift in which local clusters are estimated by repeatedly searching for higher density points in feature vector space for the estimation of parameters of gmms the mean field annealing expectation maximization em is employed the mean field annealing em provides a global optimal solution to overcome the local maxima problem in a mixture model by combining the adaptive mean shift and the mean field annealing em natural color images are segmented automatically without over segmentation or isolated regions the experiments show that the proposed algorithm can produce satisfactory segmentation without any a priori information

new results on stability of discrete time systems with time varying state delay this note is concerned with the stability analysis of discrete time systems with time varying state delay by defining new lyapunov functions and by making use of novel techniques to achieve delay dependence several new conditions are obtained for the asymptotic stability of these systems the merit of the proposed conditions lies in their less conservativeness which is achieved by circumventing the utilization of some bounding inequalities for cross products between two vectors and by paying careful attention to the subtle difference between the terms sigmam k dk k 1 middot and sigma m k dm k 1 middot which is largely ignored in the existing literature these conditions are shown via several examples to be much less conservative than some existing result

multiscale roughness measure for color image segmentation color image segmentation is always an important technique in image processing system highly precise segmentation with low computation complexity can be achieved through roughness measurement which approximate the color histogram based on rough set theory however due to the imprecise description of neighborhood similarity the existing roughness measure tends to over focus on the trivial homogeneous regions but is not accurate enough to measure the color homogeneity this paper aims to construct a multiscale roughness measure through simulating the human vision we apply the theories of linear scale space and rough sets to generate the hierarchical roughness of color distribution under multiple scales this multiscale roughness can tolerate the disturbance of trivial regions and also can provide the multilevel homogeneity representation in vision which therefore produces precise and intuitive segmentation results furthermore we propose roughness entropy for scale selection the optimal scale for segmentation is decided by the entropy variation the proposed method shows the encouraging performance in the experiments based on berkeley segmentation database

stochastic relaxation gibbs distributions and the bayesian restoration of images we make an analogy between images and statistical mechanics systems pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice like physical system the assignment of an energy function in the physical system determines its gibbs distribution because of the gibbs distribution markov random field mrf equivalence this assignment also determines an mrf image model the energy function is a more convenient and natural mechanism for embodying picture attributes than are the local characteristics of the mrf for a range of degradation mechanisms including blurring nonlinear deformations and multiplicative or additive noise the posterior distribution is an mrf with a structure akin to the image model by the analogy the posterior distribution defines another imaginary physical system gradual temperature reduction in the physical system isolates low energy states annealing or what is the same thing the most probable states under the gibbs distribution the analogous operation under the posterior distribution yields the maximum a posteriori map estimate of the image given the degraded observations the result is a highly parallel relaxation algorithm for map estimation we establish convergence properties of the algorithm and we experiment with some simple pictures for which good restorations are obtained at low signal to noise ratios

a multiple integral approach to stability of neutral time delay systems this brief discusses the problem of delay dependent stability analysis of neutral time delay systems first a multiple integral inequality is proposed based on the integral inequality a novel delay dependent stability criterion is established via a new lyapunov functional including the multiple integral terms a numerical example is given to illustrate the less conservatism of the proposed method

an lmi based algorithm for designing suboptimal static cal h_2 cal h_ infty output feedback controllers we consider the problem of designing a suboptimal cal h 2 cal h infty feedback control law for a linear time invariant control system when a complete set of state variables is not available this problem can be necessarily restated as a nonconvex optimization problem with a bilinear multiobjective functional under suitably chosen linear matrix inequality lmi constraints to solve such a problem we propose an lmi based procedure which is a sequential linearization programming approach the properties and the convergence of the algorithm are discussed in detail finally several numerical examples for static cal h 2 cal h infty output feedback problems demonstrate the applicability of the considered algorithm and also verify the theoretical results numerically

h control of discrete time linear systems with time varying delays in state this paper deals with the h control problems for discrete time linear systems with time varying delays in state in this paper the h control law is assumed to be a memoryless state feedback and the size of time varying delays considered is upper bounded the solution of the h control problem for discrete time linear systems with time varying delays in state can be obtained by solving an h control problem for auxiliary discrete time linear systems with no time delays

on the statistical analysis of dirty pictures may 7th 1986 professor a f m smith in the chair summary a continuous two dimensional region is partitioned into a fine rectangular array of sites or pixels each pixel having a particular colour belonging to a prescribed finite set the true colouring of the region is unknown but associated with each pixel there is a possibly multivariate record which conveys imperfect information about its colour according to a known statistical model the aim is to reconstruct the true scene with the additional knowledge that pixels close together tend to have the same or similar colours in this paper it is assumed that the local characteristics of the true scene can be represented by a nondegenerate markov random field such information can be combined with the records by bayes theorem and the true scene can be estimated according to standard criteria however the computational burden is enormous and the reconstruction may reflect undesirable largescale properties of the random field thus a simple iterative method of reconstruction is proposed which does not depend on these large scale characteristics the method is illustrated by computer simulations in which the original scene is not directly related to the assumed random field some complications including parameter estimation are discussed potential applications are mentioned briefly

global stability problem for coupled systems of differential equations on networks the global stability problem of equilibria is investigated for coupled systems of differential equations on networks using results from graph theory we develop a systematic approach that allows one to construct global lyapunov functions for large scale coupled systems from building blocks of individual vertex systems the approach is applied to several classes of coupled systems in engineering ecology and epidemiology and is shown to improve existing results

robust h infinity control for a class of neutral type systems via sliding mode observer in this paper the problem of observer based h infinity integral sliding mode control ismc is investigated for a class of uncertain neutral type systems with time delay ntstd an ismc strategy is put forward based on the sliding mode observer a new sufficient condition of robust asymptotic stability for the overall systems composed of the error system and sliding mode dynamics with disturbance attenuation level is derived via linear matrix inequality lmi then a smc law is designed such that the reachability of the sliding surface defined in the state estimate space can be guaranteed finally a simulation example is presented to demonstrate the feasibility of the obtained result

nonstandard finite difference scheme for a diffusive within host virus dynamics model with both virus to cell and cell to cell transmissions abstract in this paper we first propose a diffusive within host virus dynamics model with both virus to cell and cell to cell transmissions then we consider the discretization of the model by using nonstandard finite difference scheme it is then followed by the investigation of the global stability of the equilibria of the discrete model our study shows that if the basic reproduction number r 0 1 then the infection free equilibrium is globally asymptotically stable however if r 0 1 then the infection equilibrium is globally asymptotically stable numerical simulations are presented to illustrate our theoretical results

brief paper dynamics of a multigroup sir epidemic model with stochastic perturbation in this paper we introduce stochasticity into a multigroup sir susceptible infective and recovered model the stochasticity in the model is introduced by parameter perturbation which is a standard technique in stochastic population modeling in the deterministic models the basic reproduction number r 0 is a threshold which completely determines the persistence or extinction of the disease we carry out a detailed analysis on the asymptotic behavior of the stochastic model also regarding of the value of r 0 if r email protected 1 the solution of the model is oscillating around a steady state which is the disease free equilibrium of the corresponding deterministic model whereas if r 0 1 there is a stationary distribution which means that the disease will prevail

robust h control for uncertain discrete time systems with time varying delays via exponential output feedback controllers this paper considers the problem of robust h control for uncertain discrete systems with time varying delays the system under consideration is subject to time varying norm bounded parameter uncertainties in both the state and measured output matrices attention is focused on the design of a full order exponential stable dynamic output feedback controller which guarantees the exponential stability of the closed loop system and reduces the effect of the disturbance input on the controlled output to a prescribed level for all admissible uncertainties in terms of a linear matrix inequality lmi a sufficient condition for the solvability of this problem is presented which is dependent on the size of the delay when this lmi is feasible the explicit expression of the desired output feedback controller is also given finally an example is provided to demonstrate the effectiveness of the proposed approach

voronoi region based adaptive unsupervised color image segmentation color image segmentation is a crucial step in many computer vision and pattern recognition applications this paper introduces an adaptive and unsupervised approach based on voronoi regions to solve the color image segmentation problem the proposed method uses a hybrid of spatial and feature space dirichlet tessellation followed by inter voronoi region proximal cluster merging to automatically find the number of clusters and cluster centroids in an image since the voronoi regions are much smaller compared to the whole image voronoi region wise clustering improves the efficiency and accuracy of the number of clusters and cluster centroid estimation process the proposed method was compared with four other adaptive unsupervised cluster based image segmentation algorithms on three image segmentation evaluation benchmarks the experimental results reported in this paper confirm that the proposed method outperforms the existing algorithms in terms of the image segmentation quality and results in much lower average execution time per image highlightswe propose a hybrid of dirichlet tessellation to automatically segment an image spatial dirichlet tessellation adaptively divides an image into voronoi regions feature space dirichlet tessellation adaptively clusters pixels in voronoi regions inter voronoi region proximal cluster merging automatically finds the final clusters

global stability of two group sir model with random perturbation abstract in this paper we discuss the two group sir model introduced by guo li and shuai h b guo m y li z shuai global stability of the endemic equilibrium of multigroup sir epidemic models can appl math q 14 2006 259 284 allowing random fluctuation around the endemic equilibrium we prove the endemic equilibrium of the model with random perturbation is stochastic asymptotically stable in the large in addition the stability condition is obtained by the construction of lyapunov function finally numerical simulations are presented to illustrate our mathematical findings

on stability of discrete time lti systems with varying time delays this technical note concerns robust stability analysis of discrete time linear time invariant lti systems with varying time delays the stability problem is tackled under the integral quadratic constraint iqc framework the time delay system in question is viewed as feedback interconnection of an lti stable operator and the so called delay difference operator for which iqc characterizations are required in order to apply iqc analysis one of the main contributions of the technical note is to derive novel iqc characterizations for the delay difference operator based on the assumption that the time varying delay parameter and its variation are bounded a set of new stability criteria emerges as the result numerical examples are given to illustrate the effectiveness of the new stability criteria another distinct contribution is the idea of introducing bounds on the so called m step total variation and m step average variation of the time varying delay parameter which is proven to be useful in stability analysis

global analysis and numerical simulations of a novel stochastic eco epidemiological model with time delay abstract in this paper a new stochastic eco epidemiological model is proposed and investigated and the high dimensional model includes time delay and a general incidence rate which make the study more complex first the existence and uniqueness of the global positive solution with any positive initial value are verified second using stochastic analysis methods and some differential inequality techniques we explore the long time asymptotic properties of the stochastic delayed system furthermore we prove that the solution of the stochastic delayed system has a unique stationary distribution and it has ergodic property third the sufficient conditions of permanence in mean or extinction for the ecological populations are obtained finally we introduce a series of computer simulations to show the performance of the theoretical results

new result on exponential stability for neutral stochastic linear system with time varying delay abstract this paper mainly considers the problem for the exponential stability of neutral stochastic linear system with time varying delay in the light of a coupled delay integral inequality and using the stochastic analytic technique the exponential stability criteria for such systems are given in term of the algebraic inequalities regardless of whether the time varying delay is differentiable or not an illustrative numerical example is given to show the effectiveness of the obtained result

exclusion and persistence in deterministic and stochastic chemostat models abstract we first introduce and analyze a variant of the deterministic single substrate chemostat model in this model microbe removal and growth rates depend on biomass concentration with removal terms increasing faster than growth terms using a comparison principle we show that persistence of all species is possible in this scenario then we turn to modelling the influence of random fluctuations by setting up and analyzing a stochastic differential equation in particular we show that random effects may lead to extinction in scenarios where the deterministic model predicts persistence on the other hand we also establish some stochastic persistence results

auxiliary function based summation inequalities and their applications to discrete time systems auxiliary function based summation inequalities are addressed in this technical note by constructing appropriate auxiliary functions several new summation inequalities are obtained a novel sufficient criterion for asymptotic stability of discrete time systems with time varying delay is obtained in terms of linear matrix inequalities the advantage of the proposed method is demonstrated by two classical examples from the literature

genetic based stereo algorithm and disparity map evaluation in this paper a new genetic based stereo algorithm is presented our motivation is to improve the accuracy of the disparity map by removing the mismatches caused by both occlusions and false targets in our approach the stereo matching problem is considered as an optimization problem the algorithm first takes advantage of multi view stereo images to detect occlusions and therefore removes mismatches caused by visibility problems by optimizing the compatibility between corresponding points and the continuity of the disparity map using a genetic algorithm mismatches caused by false targets are removed the quadtree structure is used to implement the multi resolution framework since nodes at different level of the quadtree cover different number of pixels selecting nodes at different levels gives a similar effect as adjusting the window size at different locations of the image the experimental results show that our approach can generate more accurate disparity maps than two existing approaches in addition we introduce a new disparity map evaluation technique which is developed based on a similar technique employed in the image segmentation area comparing with two existing evaluation approaches the new technique can evaluate the disparity maps generated without additional knowledge of the scene such as the correct depth information or novel views

augmented lyapunov functional approach to stability of uncertain neutral systems with time varying delays in this paper the problem of delay dependent stability for uncertain neutral systems with time varying delays is considered the parameter uncertainties are assumed to be norm bounded by constructing an augmented lyapunov functionals a novel delay dependent stability criterion for the system is established in terms of lmis linear matrix inequalities six numerical examples are given to show the effectiveness of proposed method

a stochastic model of aids and condom use in this paper we introduce stochasticity into a model of aids and condom use via the technique of parameter perturbation which is standard in stochastic population modelling we show that the model established in this paper possesses non negative solutions as desired in any population dynamics we also carry out a detailed analysis on asymptotic stability both in probability one and in pth moment our results reveal that a certain type of stochastic perturbation may help to stabilise the underlying system

relaxed cal h _ infty stabilization conditions for discrete time fuzzy systems with interval time varying delays to derive less conservative delay and range dependent h infin stabilization conditions for discrete time takagi sugeno t s fuzzy systems with interval time varying delays the use of a fuzzy weighting dependent lyapunov krasovskii functional fwlkf in which all variables are set to be affinely or quadratically dependent on fuzzy weighting functions is proposed subsequently parameterized linear matrix inequality plmi based h infin stabilization conditions are derived by following the free weighting matrix approach to fully exploit the convexity of fuzzy weighting functions the derived plmis are sequentially replaced by a finite set of lmis by considering all possible conditions associated with fuzzy weighting functions

deformable prototypes for encoding shape categories in image databases abstract we describe a method for shape based image database search that uses deformable prototypes to represent categories rather than directly comparing a candidate shape with all shape entries in the database shapes are compared in terms of the types of nonrigid deformations differences that relate them to a small subset of representative prototypes to solve the shape correspondence and alignment problem we employ the technique of em modal matching an information preserving shape decomposition for matching describing and comparing shapes despite sensor variations and nonrigid deformations in modal matching shape is decomposed into an ordered basis of orthogonal principal components we demonstrate the utility of this approach for shape comparison in 2 d image databases

multiresolution 3 d range segmentation using focus cues this paper describes a novel system for computing a three dimensional 3 d range segmentation of an arbitrary visible scene using focus information the process of range segmentation is divided into three steps an initial range classification a surface merging process and a 3 d multiresolution range segmentation first range classification is performed to obtain quantized range estimates the range classification is performed by analyzing focus cues within a bayesian estimation framework a combined energy functional measures the degree of focus and the gibbs distribution of the class field the range classification provides an initial range segmentation second a statistical merging process is performed to merge the initial surface segments this gives a range segmentation at a coarse resolution third 3 d multiresolution range segmentation 3 d mrs is performed to refine the range segmentation into finer resolutions the proposed range segmentation method does not require initial depth estimates it allows the analysis of scenes containing multiple objects and it provides a rich description of the 3 d structure of a scene

color image segmentation advances and prospects abstract image segmentation is very essential and critical to image processing and pattern recognition this survey provides a summary of color image segmentation techniques available now basically color segmentation approaches are based on monochrome segmentation approaches operating in different color spaces therefore we first discuss the major segmentation approaches for segmenting monochrome images histogram thresholding characteristic feature clustering edge detection region based methods fuzzy techniques neural networks etc then review some major color representation methods and their advantages disadvantages finally summarize the color image segmentation techniques using different color representations the usage of color models for image segmentation is also discussed some novel approaches such as fuzzy method and physics based method are investigated as well

delay dependent robust stabilization of uncertain state delayed systems this paper concerns a problem of robust stabilization of uncertain state delayed systems a new delay dependent stabilization condition using a memoryless controller is formulated in terms of matrix inequalities an algorithm involving convex optimization is proposed to design a controller guaranteeing a suboptimal maximal delay such that the system can be stabilized for all admissible uncertainties

efficient multilevel image segmentation through fuzzy entropy maximization and graph cut optimization the fuzzy c partition entropy has been widely adopted as a global optimization technique for finding the optimized thresholds for multilevel image segmentation however it involves expensive computation as the number of thresholds increases and often yields noisy segmentation results since spatial coherence is not enforced in this paper an iterative calculation scheme is presented for reducing redundant computations in entropy evaluation the efficiency of threshold selection is further improved through utilizing the artificial bee colony algorithm as the optimization technique finally instead of performing thresholding for each pixel independently the presented algorithm oversegments the input image into small regions and uses the probabilities of fuzzy events to define the costs of different label assignments for each region the final segmentation results is computed using graph cut which produces smooth segmentation results the experimental results demonstrate the presented iterative calculation scheme can greatly reduce the running time and keep it stable as the number of required thresholds increases quantitative evaluations over 20 classic images also show that the presented algorithm outperforms existing multilevel segmentation approaches

robust output feedback controller design via local bmi optimization the problem of designing a globally optimal full order output feedback controller for polytopic uncertain systems is known to be a non convex np hard optimization problem that can be represented as a bilinear matrix inequality optimization problem for most design objectives in this paper a new approach is proposed to the design of locally optimal controllers it is iterative by nature and starting from any initial feasible controller it performs local optimization over a suitably defined non convex function at each iteration the approach features the properties of computational efficiency guaranteed convergence to a local optimum and applicability to a very wide range of problems furthermore a fast but conservative lmi based procedure for computing an initially feasible controller is also presented the complete approach is demonstrated on a model of one joint of a real life space robotic manipulator

an adaptive unsupervised approach toward pixel clustering and color image segmentation this paper proposes an adaptive unsupervised scheme that could find diverse applications in pattern recognition as well as in computer vision particularly in color image segmentation the algorithm named ant colony fuzzy c means hybrid algorithm afha adaptively clusters image pixels viewed as three dimensional data pieces in the rgb color space the ant system as algorithm is applied for intelligent initialization of cluster centroids which endows clustering with adaptivity considering algorithmic efficiency an ant subsampling step is performed to reduce computational complexity while keeping the clustering performance close to original one experimental results have demonstrated afha clustering s advantage of smaller distortion and more balanced cluster centroid distribution over fcm with random and uniform initialization quantitative comparisons with the x means algorithm also show that afha makes a better pre segmentation scheme over x means we further extend its application to natural image segmentation taking into account the spatial information and conducting merging steps in the image space extensive tests were taken to examine the performance of the proposed scheme results indicate that compared with classical segmentation algorithms such as mean shift and normalized cut our method could generate reasonably good or better image partitioning which illustrates the method s practical value

on the color image segmentation algorithm based on the thresholding and the fuzzy c means techniques abstract in this paper a segmentation algorithm for color images based on the thresholding and the fuzzy c means fcm techniques is presented the scale space filter is used as a tool for analyzing the histograms of three color components the methodology uses a coarse fine concept to reduce the computational burden required for the fcm the coarse segmentation attempts to segment coarsely using the thresholding technique while the fine segmentation assigns the pixels which remain unclassified after the coarse segmentation to the closest class using the fcm attempts also have been made to compare the performance of the proposed algorithm with other existing algorithms ohlander s rosenfeld s and bezdek s intensive computer simulation has been performed and the results are discussed in this paper the simulation results indicate that the proposed algorithm yields the most accurate segmented image on the color coordinate proposed by ohta et al while requiring a reasonable amount of computational effort

weight selection in w k means algorithm with an application in color image segmentation in this paper a weight selection procedure in the w k means algorithm is proposed based on the statistical variation viewpoint this approach can solve the w k means algorithm s problem that the clustering quality is greatly affected by the initial value of weight after the statistics of data the weights of data are designed to provide more information for the character of w k means algorithm so as to improve the precision furthermore the corresponding computational complexity is analyzed as well we compare the clustering results of the w k means algorithm with the different initialization methods results from color image segmentation illustrate that the proposed procedure produces better segmentation than the random initialization according to liu and yang s 1994 evaluation function

dynamical behavior of a stochastic multigroup sir epidemic model abstract in this paper we study a multigroup sir epidemic model in a stochastic environment by constructing a suitable stochastic lyapunov function we establish sufficient conditions for the existence of a stationary distribution of the positive solutions to the model then we establish sufficient conditions for extinction of the diseases the results reveal that the smaller white noise can ensure the persistence of the diseases while the larger white noise can lead to the extinction of the diseases finally some examples and numerical simulations are provided to illustrate the theoretical results