periodicity directionality and randomness wold features for image modeling and retrieval one of the fundamental challenges in pattern recognition is choosing a set of features appropriate to a class of problems in applications such as database retrieval it is important that image features used in pattern comparison provide good measures of image perceptual similarities we present an image model with a new set of features that address the challenge of perceptual similarity the model is based on the 2d wold decomposition of homogeneous random fields the three resulting mutually orthogonal subfields have perceptual properties which can be described as periodicity directionality and randomness approximating what are indicated to be the three most important dimensions of human texture perception the method presented improves upon earlier wold based models in its tolerance to a variety of local inhomogeneities which arise in natural textures and its invariance under image transformation such as rotation an image retrieval algorithm based on the new texture model is presented different types of image features are aggregated for similarity comparison by using a bayesian probabilistic approach the effectiveness of the wold model at retrieving perceptually similar natural textures is demonstrated in comparison to that of two other well known pattern recognition methods the wold model appears to offer a perceptually more satisfying measure of pattern similarity while exceeding the performance of these other methods by traditional pattern recognition criteria examples of natural scene wold texture modeling are also presented

spacetime texture representation and recognition based on a spatiotemporal orientation analysis this paper is concerned with the representation and recognition of the observed dynamics i e excluding purely spatial appearance cues of spacetime texture based on a spatiotemporal orientation analysis the term spacetime texture is taken to refer to patterns in visual spacetime x y t that primarily are characterized by the aggregate dynamic properties of elements or local measurements accumulated over a region of spatiotemporal support rather than in terms of the dynamics of individual constituents examples include image sequences of natural processes that exhibit stochastic dynamics e g fire water and windblown vegetation as well as images of simpler dynamics when analyzed in terms of aggregate region properties e g uniform motion of elements in imagery such as pedestrians and vehicular traffic spacetime texture representation and recognition is important as it provides an early means of capturing the structure of an ensuing image stream in a meaningful fashion toward such ends a novel approach to spacetime texture representation and an associated recognition method are described based on distributions histograms of spacetime orientation structure empirical evaluation on both standard and original image data sets shows the promise of the approach including significant improvement over alternative state of the art approaches in recognizing the same pattern from different viewpoints

texture classification using non euclidean minkowski dilation abstract this study presents a new method to extract meaningful descriptors of gray scale texture images using minkowski morphological dilation based on the l p metric the proposed approach is motivated by the success previously achieved by bouligand minkowski fractal descriptors on texture classification in essence such descriptors are directly derived from the morphological dilation of a three dimensional representation of the gray level pixels using the classical euclidean metric in this way we generalize the dilation for different values of p in the l p metric euclidean is a particular case when p 2 and obtain the descriptors from the cumulated distribution of the distance transform computed over the texture image the proposed method is compared to other state of the art approaches such as local binary patterns and textons for example in the classification of two benchmark data sets uiuc and outex the proposed descriptors outperformed all the other approaches in terms of rate of images correctly classified the interesting results suggest the potential of these descriptors in this type of task with a wide range of possible applications to real world problems

spatiotemporal patterns and localized structures in nonlinear optics we show that in a degenerate optical parametric oscillator with saturable losses for the frequency down converted field the steady state can be destabilized via either a hopf or a turing instability the relative order between the two bifurcations is controlled by the linear loss of the saturable absorber if the turing bifurcation is subcritical and the hopf bifurcation occurs in the hysteresis domain involving the homogeneous and inhomogeneous states steady localized structures are generated below the hopf bifurcation and time periodic localized structures are generated above the hopf bifurcation

spatiotemporal dynamics near a codimension two point spatiotemporal dynamics resulting from the interaction of two instabilities breaking respectively spatial and temporal symmetries are studied in the framework of the amplitude equation formalism the corresponding bifurcation scenarios feature steady hopf bistability with corresponding localized structures but also different types of mixed states some of these mixed modes result from self induced subharmonic instabilities of the pure steady and hopf modes the bifurcation schemes are then used to organize the results of numerical simulations of a one dimensional reaction diffusion model these dynamics are relevant to experimental chemical systems featuring a codimension two turing hopf point but also to any experimental setup where homogeneous temporal oscillations and spatial patterns are obtained for nearby values of parameters s1063 651x 96 03707 5

a theory for one dimensional asynchronous chemical waves we present a theory for an experimentally observed phenomenon of one dimensional asynchronous waves it has been proposed that two oppositely moving travelling wave states can localize an oscillatory core structure a class of even and odd parity localized solution of the same system has been explicitly shown it has been argued that a combination of these local and global states in an extended system can produce asynchronous waves we also produce numerical results in support of our analytic predictions

robustness analysis of identification and adaptive control for stochastic systems abstract this paper gives a preliminary robustness analysis for stochastic systems with unmodelled dynamics the extended least squares algorithm and stochastic adaptive control for tracking are defined on the basis of the modelled part of the system and it is shown that the parameter estimate is closed to the true one and the tracking error is small if the unmodelled dynamics is small

a robust stochastic adaptive controller the adaptive tracking problem is considered for discrete time stochastic systems consisting of a modeled part being a stable armax process and unmodeled dynamics dominated by a small constant epsilon multiplied by a quantity independent of epsilon but tending to infinity as the past input output and noise grow the adaptive control law proposed is switched at stopping times and is disturbed by a sequence of random vectors bounded by an arbitrary small but fixed constant sigma it is shown that the closed loop system is globally stable the estimation errors for parameters contained in the modeled part of the order epsilon and the tracking error differs from the minimum tracking error for systems without unmodeled dynamics by value of o epsilon sup 2 o sigma sup 2

adaptive control via consistent estimation for deterministic systems abstract for multidimensional discrete time deterministic systems the optimal adaptive control has been derived by use of a probabilistic method so that when the reference signal is an arbitrary bounded random sequence the tracking error and the estimation error based on a projection algorithm go to zero with a near exponential convergence rate for this the basic step is to prove the consistency of estimates when the condition number of the matrix consisting of regressors diverges to infinity in other words when the persistent excitation condition is not satisfied

consistent order estimation for linear stochastic feedback control systems carma model abstract a new criterion cic is introduced to estimate orders p0 q0 r0 of the linear stochastic feedback control system with correlated noise described by a carma model it is proved that the estimate is strongly consistent when the upper bounds for p0 q0 and r0 are available but neither the stability condition nor the ergodicity of the input and output are imposed on the system

unsupervised texture segmentation of images using tuned matched gabor filters recent studies have confirmed that the multichannel gabor decomposition represents an excellent tool for image segmentation and boundary detection unfortunately this approach when used for unsupervised image analysis tasks imposes excessive storage requirements due to the nonorthogonality of the basis functions and is computationally highly demanding in this correspondence we propose a novel method for efficient image analysis that uses tuned matched gabor filters the algorithmic determination of the parameters of the gabor filters is based on the analysis of spectral feature contrasts obtained from iterative computation of pyramidal gabor transforms with progressive dyadic decrease of elementary cell sizes the method requires no a priori knowledge of the analyzed image so that the analysis is unsupervised computer simulations applied to different classes of textures illustrate the matching property of the tuned gabor filters derived using our determination algorithm also their capability to extract significant image information and thus enable an easy and efficient low level image analysis will be demonstrated

geometric invariants and object recognition we discuss the role of the general invariance concept in object recognition and review the classical and recent literature on projective invariance invariants help solve major problems of object recognition for instance different images of the same object often differ from each other because of the different viewpoint from which they were obtained to match the two images common methods thus need to find the correct viewpoint a difficult problem that can involve search in a high dimensional space of all possible points of view and or finding point correspondences geometric invariants are shape descriptors computed from the geometry of the shape that remain unchanged under geometric transformations such as changing the viewpoint thus they can be matched without search deformations of objects are another important class of geometric changes for which invariance is useful

texture roughness analysis and synthesis via extended self similar ess model the 2d fractional brownian motion fbm model provides a useful tool to model textured surfaces whose roughness is scale invariant to represent textures whose roughness is scale dependent we generalize the fbm model to the extended self similar ess model in this research we present an estimation algorithm to extract the model parameters from real texture data furthermore a new incremental fourier synthesis algorithm is proposed to generate the 2d realizations of the ess model finally the estimation and rendering methods are combined to synthesize real textured surfaces

using spin images for efficient object recognition in cluttered 3d scenes we present a 3d shape based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion recognition is based on matching surfaces by matching points using the spin image representation the spin image is a data level shape descriptor that is used to match surfaces represented as surface meshes we present a compression scheme for spin images that results in efficient multiple object recognition which we verify with results showing the simultaneous recognition of multiple objects from a library of 20 models furthermore we demonstrate the robust performance of recognition in the presence of clutter and occlusion through analysis of recognition trials on 100 scenes

a theory for multiresolution signal decomposition the wavelet representation multiresolution representations are effective for analyzing the information content of images the properties of the operator which approximates a signal at a given resolution were studied it is shown that the difference of information between the approximation of a signal at the resolutions 2 sup j 1 and 2 sup j where j is an integer can be extracted by decomposing this signal on a wavelet orthonormal basis of l sup 2 r sup n the vector space of measurable square integrable n dimensional functions in l sup 2 r a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi x this decomposition defines an orthogonal multiresolution representation called a wavelet representation it is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters wavelet representation lies between the spatial and fourier domains for images the wavelet representation differentiates several spatial orientations the application of this representation to data compression in image coding texture discrimination and fractal analysis is discussed

texture classification by wavelet packet signatures this correspondence introduces a new approach to characterize textures at multiple scales the performance of wavelet packet spaces are measured in terms of sensitivity and selectivity for the classification of twenty five natural textures both energy and entropy metrics were computed for each wavelet packet and incorporated into distinct scale space representations where each wavelet packet channel reflected a specific scale and orientation sensitivity wavelet packet representations for twenty five natural textures were classified without error by a simple two layer network classifier an analyzing function of large regularity d sub 20 was shown to be slightly more efficient in representation and discrimination than a similar function with fewer vanishing moments d sub 6 in addition energy representations computed from the standard wavelet decomposition alone 17 features provided classification without error for the twenty five textures included in our study the reliability exhibited by texture signatures based on wavelet packets analysis suggest that the multiresolution properties of such transforms are beneficial for accomplishing segmentation classification and subtle discrimination of texture

a parametric texture model based on joint statistics of complex wavelet coefficients we present a universal statistical model for texture images in the context of an overcomplete complex wavelet transform the model is parameterized by a set of statistics computed on pairs of coefficients corresponding to basis functions at adjacent spatial locations orientations and scales we develop an efficient algorithm for synthesizing random images subject to these constraints by iteratively projecting onto the set of images satisfying each constraint and we use this to test the perceptual validity of the model in particular we demonstrate the necessity of subgroups of the parameter set by showing examples of texture synthesis that fail when those parameters are removed from the set we also demonstrate the power of our model by successfully synthesizing examples drawn from a diverse collection of artificial and natural textures

will the pls criterion for order estimation work with aml and a posteriori prediction error the general formula of the pls predictive least squares criterion for order estimation is worked out under the assumption that the parameter estimates are calculated via the aml approximate maximum likelihood a particular case is then carefully analysed and it is shown that depending on the system generating the data the pls critetion using the a posteriori prediction error can surprisingly almost surely overestimate the true order

wavelet domain multifractal analysis for static and dynamic texture classification in this paper we propose a new texture descriptor for both static and dynamic textures the new descriptor is built on the wavelet based spatial frequency analysis of two complementary wavelet pyramids standard multiscale and wavelet leader these wavelet pyramids essentially capture the local texture responses in multiple high pass channels in a multiscale and multiorientation fashion in which there exists a strong power law relationship for natural images such a power law relationship is characterized by the so called multifractal analysis in addition two more techniques scale normalization and multiorientation image averaging are introduced to further improve the robustness of the proposed descriptor combining these techniques the proposed descriptor enjoys both high discriminative power and robustness against many environmental changes we apply the descriptor for classifying both static and dynamic textures our method has demonstrated excellent performance in comparison with the state of the art approaches in several public benchmark datasets

asymptotically optimal adaptive control with consistent parameter estimates for the discrete time linear stochastic systems with unknown coefficients we give an adaptive control by which both strong consistency of the parameter estimates and asymptotic optimality for the tracking system are achieved simultaneously this is done by disturbing the signal that is to be tracked and the disturbance consists of a sequence of random vectors with covariance matrices tending to zero the main result is essentially based on some criteria for consistency of parameter estimate for the system without monitoring which are also demonstrated in the paper the existence of adaptive control is also discussed

robust recursive identification of multidimensional linear regression models stochastic adaptive estimation and control algorithms involving recursive prediction estimates have guaranteed convergence rates when the noise is not too coloured as when a positive real condition on the noise mode is satisfied moreover the whiter the noise environment the more robust are the algorithms this paper shows that for linear regression signal models the suitable introduction of while noise into the estimation algorithm can make it more robust without compromising on convergence rates indeed there are guaranteed attractive convergence rates independent of the process noise colour no positive real condition is imposed on the noise model

nonstationary time series identification abstract in this paper strongly consistent estimates are given for both unknown parameters and orders of the nonstationary time series where the nonstationarity arises because 1 at any time n a feedback control is added to the usual arma process which is successfully applied to modelling economic systems and 2 det a z may have zeros on the unit circle in addition to those located outside the unit disk where a z is the matrix polynomial corresponding to the ar part of the arma process

viewpoint invariant texture description using fractal analysis image texture provides a rich visual description of the surfaces in the scene many texture signatures based on various statistical descriptions and various local measurements have been developed existing signatures in general are not invariant to 3d geometric transformations which is a serious limitation for many applications in this paper we introduce a new texture signature called the multifractal spectrum mfs the mfs is invariant under the bi lipschitz map which includes view point changes and non rigid deformations of the texture surface as well as local affine illumination changes it provides an efficient framework combining global spatial invariance and local robust measurements intuitively the mfs could be viewed as a better histogram with greater robustness to various environmental changes and the advantage of capturing some geometrical distribution information encoded in the texture experiments demonstrate that the mfs codes the essential structure of textures with very low dimension and thus represents an useful tool for texture classification

consistent estimation of the order of stochastic control systems a consistent estimate of the order of feedback control systems with unknown matrix coefficients estimated by the least squares method is derived by minimizing a modified version of the bayesian information criterion

spatiotemporal dynamics near a supercritical turing hopf bifurcation in a two dimensional reaction diffusion system pattern formation in semiconductor heterostructures is studied on the basis of a spatially two dimensional model of reaction diffusion type in particular we investigate the neighborhood of a codimension two turing hopf instability by analytical methods amplitude equations are derived which predict the absence of mixed modes but extended ranges of bistability between homogeneous oscillatory states and hexagonal turing patterns our results are confirmed by numerical simulations the features are not confined to a neighborhood of the bifurcation point so that the conclusions of the weakly nonlinear analysis explain the observations in large portions of the parameter space at least qualitatively

multiple resolution texture analysis and classification textures are classified based on the change in their properties with changing resolution the area of the gray level surface is measured at serveral resolutions this area decreases at coarser resolutions since fine details that contribute to the area disappear fractal properties of the picture are computed from the rate of this decrease in area and are used for texture comparison and classification the relation of a texture picture to its negative and directional properties are also discussed

shape matching and object recognition using shape contexts we present a novel approach to measuring similarity between shapes and exploit it for object recognition in our framework the measurement of similarity is preceded by 1 solving for correspondences between points on the two shapes 2 using the correspondences to estimate an aligning transform in order to solve the correspondence problem we attach a descriptor the shape context to each point the shape context at a reference point captures the distribution of the remaining points relative to it thus offering a globally discriminative characterization corresponding points on two similar shapes will have similar shape contexts enabling us to solve for correspondences as an optimal assignment problem given the point correspondences we estimate the transformation that best aligns the two shapes regularized thin plate splines provide a flexible class of transformation maps for this purpose the dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points together with a term measuring the magnitude of the aligning transform we treat recognition in a nearest neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image results are presented for silhouettes trademarks handwritten digits and the coil data set

computing local surface orientation and shape from texture forcurved surfaces shape from texture is best analyzed in two stages analogous to stereopsis and structure from motion a computing the texture distortion from the image and b interpreting the texture distortion to infer the orientation and shape of the surface in the scene we model the texture distortion for a given point and direction on the image plane as an affine transformation and derive the relationship between the parameters of this transformation and the shape parameters we have developed a technique for estimating affine transforms between nearby image patches which is based on solving a system of linear constraints derived from a differential analysis one need not explicitly identify texels or make restrictive assumptions about the nature of the texture such as isotropy we use non linear minimization of a least squares error criterion to recover the surface orientation slant and tilt and shape principal curvatures and directions based on the estimated affine transforms in a number of different directions a simple linear algorithm based on singular value decomposition of the linear parts of the affine transforms provides the initial guess for the minimization procedure experimental results on both planar and curved surfaces under perspective projection demonstrate good estimates for both orientation and shape a sensitivity analysis yields predictions for both computer vision algorithms and human perception of shape from texture

optimal adaptive control and consistent parameter estimates for armax model withquadratic cost we consider the multidimensional armax model a z y n b z u n c z w n with loss function j u mathop overline lim limits n to infty frac 1 n sum limits i 1 n left y i tau q 1 y i u i tau q 2 u i right where the coefficients in the matrix polynomials a z b z and c z are unknown conditions used here are 1 stability of a z and full rank of a p 2 strictly positive realness of c z frac 1 2 i and 3 controllability and observability of a matrix triple consisting of coefficients in a z b z and q 1 on the basis of the estimates given by the stochastic gradient algorithm for unknown parameters an adaptive control law is recursively defined it is proved that the parameter estimates are strongly consistent and the quadratic loss function reaches its minimum this paper also includes some general theorems on parameter estimation on which the results about adaptive control are essentially based

texture classification using windowed fourier filters we define a distance between textures for texture classification from texture features based on windowed fourier filters the definition of the distance relies on an interpretation of our texture attributes in terms of spectral density when the texture can be considered as a gaussian random field the distance between textures is then defined as a symmetrized kullback distance which is a simple function of the attributes and does not require any normalization an experimental analysis using gabor filters and in particular a comparison to quadratic distances shows the efficiency and robustness of the method

convergence rate of least squares identification and adaptive control for stochastic systems the strong consistency and the convergence rate of least squares identification for the multidimensional armax model are established under some decaying excitation conditions which are satisfied if both input and output do not grow too fast and the attenuating excitation technique is applied the parameter identification results are applied to adaptive control systems with a quadratic loss function the rate of convergence of the loss function to its minimum is also obtained

classifying dynamic textures via spatiotemporal fractal analysis the large scale images and videos are one kind of the main source of big data dynamic texture dt is essential for understanding the video sequences with spatio temporal similarities this paper presents a powerful tool called dynamic fractal analysis to dt description and classification which integrates rich description of dt with strong robustness to environmental changes the proposed dynamic fractal spectrum dfs for dt sequences is composed of two components the first one is a volumetric dynamic fractal spectrum component v dfs that captures the stochastic self similarities of dt sequences by treating them as 3d volumes the second one is a multi slice dynamic fractal spectrum component s dfs that encodes fractal structures of repetitive dt patterns on 2d slices along different views of the 3d volume to fully exploit various types of dynamic patterns in dt five measurements of dt pixels are collected for the analysis on dt sequences from different perspectives we evaluated our method on four publicly available benchmark datasets all the experimental results have demonstrated the excellent performance of our method in comparison with state of the art approaches highlightsthe dynamic multi fractal analysis is developed for dt description our method is very discriminative and robust to environmental changes a computational acceleration scheme is provided for the proposed descriptor our method exhibits excellent performance on four benchmark datasets

extended least squares and their applications to adaptive control and prediction in linear systems herein strong consistency of recursive extended least squares is established under considerably weaker assumptions than previously assumed in the literature the argument used to establish consistency also leads to certain basic properties of adaptive predictors based on these recursive estimators making use of these properties of the adaptive predictors simple modifications of the astrom wittenmark self tuning regulator are proposed and shown to be asymptotically optimal

generic spatiotemporal dynamics near codimension two turing hopf bifurcations the coupling of spatial and temporal symmetry breaking instabilities is studied in a two variable reaction diffusion model describing semiconductor transport a variety of spatiotemporal patterns corresponding to pure hopf and turing modes localized patterns and mixed turing hopf modes including subharmonic spatiotemporal spiking are found by organizing the results in a time scale versus space scale diagram and by comparing them with a chemical reaction diffusion model it is shown that such behavior is generic for a class of extended nonlinear dynamic systems near codimension two turing hopf bifurcations

morphology based multifractal estimation for texture segmentation multifractal analysis is becoming more and more popular in image segmentation community in which the box counting based multifractal dimension estimations are most commonly used however in spite of its computational efficiency the regular partition scheme used by various box counting methods intrinsically produces less accurate results in this paper a novel multifractal estimation algorithm based on mathematical morphology is proposed and a set of new multifractal descriptors namely the local morphological multifractal exponents is defined to characterize the local scaling properties of textures a series of cubic structure elements and an iterative dilation scheme are utilized so that the computational complexity of the morphological operations can be tremendously reduced both the proposed algorithm and the box counting based methods have been applied to the segmentation of texture mosaics and real images the comparison results demonstrate that the morphological multifractal estimation can differentiate texture images more effectively and provide more robust segmentations

texture segmentation using fractal dimension this paper deals with the problem of recognizing and segmenting textures in images for this purpose the authors employ a technique based on the fractal dimension fd and the multi fractal concept six fd features are based on the original image the above average high gray level image the below average low gray level image the horizontally smoothed image the vertically smoothed image and the multi fractal dimension of order two a modified box counting approach is proposed to estimate the fd in combination with feature smoothing in order to reduce spurious regions to segment a scene into the desired number of classes an unsupervised k means like clustering approach is used mosaics of various natural textures from the brodatz album as well as microphotographs of thin sections of natural rocks are considered and the segmentation results to show the efficiency of the technique supervised techniques such as minimum distance and k nearest neighbor classification are also considered the results are compared with other techniques

filters random fields and maximum entropy frame towards a unified theory for texture modeling this article presents a statistical theory for texture modeling this theory combines filtering theory and markov random field modeling through the maximum entropy principle and interprets and clarifies many previous concepts and methods for texture analysis and synthesis from a unified point of view our theory characterizes the ensemble of images i with the same texture appearance by a probability distribution f i on a random field and the objective of texture modeling is to make inference about f i given a set of observed texture examples in our theory texture modeling consists of two steps 1 a set of filters is selected from a general filter bank to capture features of the texture these filters are applied to observed texture images and the histograms of the filtered images are extracted these histograms are estimates of the marginal distributions of f i this step is called feature extraction 2 the maximum entropy principle is employed to derive a distribution p i which is restricted to have the same marginal distributions as those in 1 this p i is considered as an estimate of f i this step is called feature fusion a stepwise algorithm is proposed to choose filters from a general filter bank the resulting model called frame filters random fields and maximum entropy is a markov random field mrf model but with a much enriched vocabulary and hence much stronger descriptive ability than the previous mrf models used for texture modeling gibbs sampler is adopted to synthesize texture images by drawing typical samples from p i thus the model is verified by seeing whether the synthesized texture images have similar visual appearances to the texture images being modeled experiments on a variety of 1d and 2d textures are described to illustrate our theory and to show the performance of our algorithms these experiments demonstrate that many textures which are previously considered as from different categories can be modeled and synthesized in a common framework

lorentzian type soliton solutions of ac driven complex ginzburg landau equation abstract fractional transform solutions of modified complex ginzburg landau equation phase locked to a time dependent force have been found the reported solutions are necessarily of the lorentzian type containing trigonometric hyperbolic and pure cnoidal functions numerical simulations indicated that the periodic solutions and soliton solutions are quite stable against finite perturbations

classifying surface texture while simultaneously estimating illumination direction we propose a novel classifier that both classifies surface texture and simultaneously estimates the unknown illumination conditions a new formal model of the dependency of texture features on lighting direction is developed which shows that their mean vectors are trigonometric functions of the illuminations tilt and slant angles this is used to develop a probabilistic description of feature behaviour which forms the basis of the new classifier given a feature set from an image of an unknown texture captured under unknown illumination conditions the algorithm first estimates the most likely illumination direction for each possible texture class these estimates are used to calculate the class likelihoods and the classification is made accordingly the ability of the classifier to estimate illuminant direction and to assign the correct class was tested on 55 real texture samples in two stages the classifier was able to accurately estimate both the tilt and the slant angles of the light source for the majority of textures and gave a 98 classification rate

a markov random field model based approach to unsupervised texture segmentation using local and global spatial statistics many studies have proven that statistical model based texture segmentation algorithms yield good results provided that the model parameters and the number of regions be known a priori in this correspondence we present an unsupervised texture segmentation method that does not require knowledge about the different texture regions their parameters or the number of available texture classes the proposed algorithm relies on the analysis of local and global second and higher order spatial statistics of the original images the segmentation map is modeled using an augmented state markov random field including an outlier class that enables dynamic creation of new regions during the optimization process a bayesian estimate of this map is computed using a deterministic relaxation algorithm results on real world textured images are presented