fingerprint orientation field reconstruction by weighted discrete cosine transform orientation field represents the topological structure of the interleaved ridge and valley flows in fingerprint images although a number of methods have been proposed for orientation estimation reliable computation of orientation field is still a challenging problem due to the poor quality of some fingerprints this paper proposes a method to reconstruct fingerprint orientation field by weighted discrete cosine transform dct first the dct functions are used to build the basis atoms for linear representation of orientation field then the dct basis atoms of low and high orders are combined with the weights determined by singularity measurements for orientation reconstruction the weighted dct model is further extended for partial fingerprints to gradually and iteratively reconstruct the orientations in noisy or missing parts of fingerprints the proposed method can perform well in smoothing out the noise while maintaining the orientation details in singular regions extensive experiments have been done to compare the proposed method with some existing methods on nist and fvc fingerprint databases in terms of the reconstruction accuracy of orientation field fingerprint indexing performance and fingerprint recognition accuracy experimental results illustrate the effectiveness of the proposed method in reconstructing orientation fields especially for poor quality and partial fingerprints

detecting singular patterns in 2d vector fields using weighted laurent polynomial in this paper we propose a method for detecting patterns of interest in vector fields our method detects patterns in a scale and rotation invariant manner it works by approximating the vector field data locally using a laurent polynomial weighted by radial basis functions the proposed representation is able to model both analytic and non analytic flow fields invariance to scale and rotation is achieved by combining the linearity properties of the model coefficients and a scale space parameter of the radial basis functions promising detection results are obtained on a variety of fluid flow sequences

unsupervised image segmentation using markov random field models abstract we present two unsupervised segmentation algorithms based on hierarchical markov random field models for segmenting both noisy images and textured images each algorithm finds the the most likely number of classes their associated model parameters and generates a corresponding segmentation of the image into these classes this is achieved according to the maximum a posteriori criterion to facilitate this an mcmc algorithm is formulated to allow the direct sampling of all the above parameters from the posterior distribution of the image to allow the number of classes to be sampled a reversible jump is incorporated into the markov chain experimental results are presented showing rapid convergence of the algorithm to accurate solutions

texture segmentation using independent scale component wise riemannian covariance gaussian mixture model in kl measure based multi scale nonlinear structure tensor space this paper proposes a novel texture segmentation approach using independent scale component wise riemannian covariance gaussian mixture model icrgmm in kullback leibler kl measure based multi scale nonlinear structure tensor msnst space we use the independent scale distribution and full covariance structure to replace the covariant scale distribution and 1d variance structure used in our previous research to construct the optimal full covariance structure we define the full covariance on kl euclidean log euclidean and riemannian gradient mappings and compare their performances the comparison experiments demonstrate that the riemannian gradient mapping leads to its optimum properties over other choices when constructing the full covariance to estimate and update the statistical parameters more accurately the component wise expectation maximization for mixtures cem 2 algorithm is proposed instead of the originally used k means algorithm the superiority of the proposed icrgmm has been demonstrated based on texture clustering and graph cuts based texture segmentation using a large number of synthesis texture images and real natural scene textured images and further analyzed in terms of error ratio and modified f measure respectively

modeling orientation fields of fingerprints with rational complex functions abstract in this paper a novel model is proposed for the orientation field of fingerprints which can be expressed as the argument of a rational complex function it is suitable for all types of fingerprints experimental results show that it performs much better than the previous works

information theoretic active polygons for unsupervised texture segmentation curve evolution models used in image segmentation and based on image region information usually utilize simple statistics such as means and variances hence can not account for higher order nature of the textural characteristics of image regions in addition the object delineation by active contour methods results in a contour representation which still requires a substantial amount of data to be stored for subsequent multimedia applications such as visual information retrieval from databases polygonal approximations of the extracted continuous curves are required to reduce the amount of data since polygons are powerful approximators of shapes for use in later recognition stages such as shape matching and coding the key contribution of this paper is the development of a new active contour model which nicely ties the desirable polygonal representation of an object directly to the image segmentation process this model can robustly capture texture boundaries by way of higher order statistics of the data and using an information theoretic measure and with its nature of the ordinary differential equations this new variational texture segmentation model is unsupervised since no prior knowledge on the textural properties of image regions is used another contribution in this sequel is a new polygon regularizer algorithm which uses electrostatics principles this is a global regularizer and is more consistent than a local polygon regularization in preserving local features such as corners

adaptive segmentation of noisy and textured images abstract an image segmentation algorithm is described which is based on the integration of signal model parameter estimates and maximum a posteriori labelling the parameter estimation is based on either a maximum likelihood based method for a quadric signal model or a maximum pseudo likelihood based method for a gauss markov signal model the first case is applicable to standard grey level image segmentation as well as segmentation of shaded 3d surfaces while the second case is applicable to texture segmentation a key aspect of the algorithm is the incorporation of a coarse to fine processing strategy which limits the search for the optimum labelling at any one resolution to a subset of labellings which are consistent with the optimum labelling at the previous coarser resolution consistency is in terms of a prior label model which specifies the conditional probability of a given label in terms of the labelling at the previous level of resolution it is shown how such an approach leads to a simple relaxation procedure based on local pyramid node computations an extension of the algorithm is also described which performs accurate inter region boundary placement using a step wise refinement procedure based on a simple adaptive filter the problem of automatic determination of the number of regions is also addressed it is shown how a simple agglomerative clustering idea again based on pyramid node computations can effectively solve this problem

unsupervised segmentation of markov random field modeled textured images using selectionist relaxation among the existing texture segmentation methods those relying on markov random fields have retained substantial interest and have proved to be very efficient in supervised mode the use of markov random fields in unsupervised mode is however hampered by the parameter estimation problem the recent solutions proposed to overcome this difficulty rely on assumptions about the shapes of the textured regions or about the number of textures in the input image that may not be satisfied in practice in this paper an evolutionary approach selectionist relaxation is proposed as a solution to the problem of segmenting markov random field modeled textures in unsupervised mode in selectionist relaxation the computation is distributed among a population of units that iteratively evolves according to simple and local evolutionary rules a unit is an association between a label and a texture parameter vector the units whose likelihood is high are allowed to spread over the image and to replace the units that receive lower support from the data consequently some labels are growing while others are eliminated starting with an initial random population this evolutionary process eventually results in a stable labelization of the image which is taken as the segmentation in this work the generalized ising model is used to represent textured data because of the awkward nature of the partition function in this model a high temperature approximation is introduced to allow the evaluation of unit likelihoods experimental results on images containing various synthetic and natural textures are reported

definition and extraction of stable points from fingerprint images this paper introduces a new idea to define and extract singular points sps including cores and deltas from fingerprint images by a zone could be in theorem that describes harmonic relationships of a sp and its neighbors according to the theorem we can view sps as stable points attractors and rejectors by their rotations estimate core s direction and restore sp offset the proposed method is validated in the following two hierarchical aspects one is to test the reliability and accuracy of coarse sp detection by means of two indices ri and ac and the other is the accuracy of core s location and the stability of core s direction in terms of improved fingercode matching experiments on partial fvc2004 data sets show that it works well and outperforms three other methods

geodesic active regions and level set methods for supervised texture segmentation this paper presents a novel variational framework to deal with frame partition problems in computer vision this framework exploits boundary and region based segmentation modules under a curve based optimization objective function the task of supervised texture segmentation is considered to demonstrate the potentials of the proposed framework the textured feature space is generated by filtering the given textured images using isotropic and anisotropic filters and analyzing their responses as multi component conditional probability density functions the texture segmentation is obtained by unifying region and boundary based information as an improved geodesic active contour model the defined objective function is minimized using a gradient descent method where a level set approach is used to implement the obtained pde according to this pde the curve propagation towards the final solution is guided by boundary and region based segmentation forces and is constrained by a regularity force the level set implementation is performed using a fast front propagation algorithm where topological changes are naturally handled the performance of our method is demonstrated on a variety of synthetic and real textured frames

simultaneous parameter estimation and segmentation of gibbs random fields using simulated annealing an adaptive segmentation algorithm is developed which simultaneously estimates the parameters of the underlying gibbs random field grf and segments the noisy image corrupted by additive independent gaussian noise the algorithm which aims at obtaining the maximum a posteriori map segmentation is a simulated annealing algorithm that is interrupted at regular intervals for estimating the grf parameters maximum likelihood ml estimates of the parameters based on the current segmentation are used to obtain the next segmentation it is proven that the parameter estimates and the segmentations converge in distribution to the ml estimate of the parameters and the map segmentation with those parameter estimates respectively due to computational difficulties however only an approximate version of the algorithm is implemented the approximate algorithm is applied on several two and four region images with different noise levels and with first order and second order neighborhoods

complete classification of shape functions of self similar solutions abstract in this paper we study some asymptotic profiles of shape functions of self similar solutions to the initial boundary value problem with neumann boundary condition for the generalized kpz equation u t u x x u x q where q is positive number the shapes of solutions of the corresponding nonlinear ordinary differential equation are of very different nature the properties depend on the critical value q 1 3 2 2 and initial data as usual

theoretical analysis of multispectral image segmentation criteria markov random field mrf image segmentation algorithms have been extensively studied and have gained wide acceptance however almost all of the work on them has been experimental this provides a good understanding of the performance of existing algorithms but not a unified explanation of the significance of each component to address this issue we present a theoretical analysis of several mrf image segmentation criteria standard methods of signal detection and estimation are used in the theoretical analysis which quantitatively predicts the performance at realistic noise levels the analysis is decoupled into the problems of false alarm rate parameter selection neyman pearson and receiver operating characteristics detection threshold expected a priori boundary roughness and supervision only the performance inherent to a criterion with perfect global optimization is considered the analysis indicates that boundary and region penalties are very useful while distinct mean penalties are of questionable merit region penalties are far more important for multispectral segmentation than for greyscale this observation also holds for gauss markov random fields and for many separable within class pdfs to validate the analysis we present optimization algorithms for several criteria theoretical and experimental results agree fairly well

stochastic and deterministic networks for texture segmentation several texture segmentation algorithms based on deterministic and stochastic relaxation principles and their implementation on parallel networks are described the segmentation process is posed as an optimization problem and two different optimality criteria are considered the first criterion involves maximizing the posterior distribution of the intensity field given the label field maximum a posteriori estimate the posterior distribution of the texture labels is derived by modeling the textures as gauss markov random fields gmrfs and characterizing the distribution of different texture labels by a discrete multilevel markov model a stochastic learning algorithm is proposed this iterated hill climbing algorithm combines fast convergence of deterministic relaxation with the sustained exploration of the stochastic algorithms but is guaranteed to find only a local minimum the second optimality criterion requires minimizing the expected percentage of misclassification per pixel by maximizing the posterior marginal distribution and the maximum posterior marginal algorithm is used to obtain the corresponding solution all these methods implemented on parallel networks can be easily extended for hierarchical segmentation results of the various schemes in classifying some real textured images are presented

global models for the orientation field of fingerprints an approach based on quadratic differentials quadratic differentials naturally define analytic orientation fields on planar surfaces we propose to model orientation fields of fingerprints by specifying quadratic differentials models for all fingerprint classes such as arches loops and whorls are laid out these models are parametrised by few geometrically interpretable parameters which are invariant under euclidean motions we demonstrate their ability in adapting to given observed orientation fields and we compare them to existing models using the fingerprint images of the nist special database 4 we also illustrate that these model allow for extrapolation into unobserved regions this goes beyond the scope of earlier models for the orientation field as those are restricted to the observed planar fingerprint region within the framework of quadratic differentials we are able to verify analytically penrose s formula for the singularities on a palm l s penrose dermatoglyphics scientific american vol 221 no 6 pp 73 84 1969 potential applications of these models are the use of their parameters as indices of large fingerprint databases as well as the definition of intrinsic coordinates for single fingerprint images

fingerprint pattern classification abstract this paper presents a new method for fingerprint classification in this method fingerprint images are divided into 32 32 subregions to obtain direction pattern next the relaxation smoothing process with singularity detection and convergency checking is performed starting from the singular regions found feature parameters of the fingerprint are obtained by extracting major flow line traces the result of the experiments shows that this approach is capable of classifying fingerprint patterns into more than ten categories

modelling fingerprint ridge orientation using legendre polynomials the estimation of fingerprint ridge orientation is an essential step in every automatic fingerprint verification system the importance of ridge orientation can be deflected from the fact that it is inevitably used for detecting describing and matching fingerprint features such as minutiae and singular points in this paper we propose a novel method for fingerprint ridge orientation modelling using legendre polynomials one of the main problems it addresses is smoothing orientation data while preserving details in high curvature areas especially singular points we show that singular points which result in a discontinuous orientation field can be modelled by the zero poles of legendre polynomials the models parameters are obtained in a two staged optimization procedure another advantage of the proposed method is a very compact representation of the orientation field using only 56 coefficients we have carried out extensive experiments using a state of the art fingerprint matcher and a singular point detector moreover we compared the proposed method with other state of the art fingerprint orientation estimation algorithms we can report significant improvements in both singular point detection and matching rates

a semilinear heat equation with singular initial data we first prove existence and uniqueness of non negative solutions of the equation in in the range 1 p n when initial data u x 0 a x 2 p 1 x 0 for a 0 it is proved that the maximal and minimal solutions are self similar with the form where g g a satisfies after uniqueness is proved the asymptotic behaviour of solutions of is studied in particular we show that the case for a 0 is also considered and a sharp decay rate of the above equation is derived in the final we reveal existence of solutions of the first and third equations above which change sign

exact mean integrated squared error an exact and easily computable expression for the mean integrated squared error mise for the kernel estimator of a general normal mixture density is given for gaussian kernels of arbitrary order this provides a powerful new way of understanding density estimation which complements the usual tools of simulation and asymptotic analysis the family of normal mixture densities is very flexible and the formulae derived allow simple exact analysis for a wide variety of density shapes a number of applications of this method giving important new insights into kernel density estimation are presented among these is the discovery that the usual asymptotic approximations to the mise can be quite inaccurate especially when the underlying density contains substantial fine structure and also strong evidence that the practical importance of higher order kernels is surprisingly small for moderate sample sizes 1 introduction substantial research has been devoted to kernel density estimation this is because it provides a simple yet appealing context in which to study problems and issues that arise in all types of nonparametric curve estimation this includes regression spectral density and hazard estimation and also a variety of other estimators including histograms splines and orthogonal series three important and useful tools for understanding the behavior of nonparametric curve estimators are asymptotic analysis simulation and numerical calculation of error criteria each of these methods provides many useful insights into the complicated structure present in the study of curve estimation however each has its limitations as well rhe strength of asymptotic analysis is that it frequently allows simultaneous study of many different specific examples through general results applying to entire classes of settings the weakness of asymptotics is that they only describe behavior in the limit this is still very useful in many situations because the asymptotics describe the actual situation quite well however it is less useful when the asymptotics have not yet kicked in that is in studying situations where the asymptotically dominant effect has not taken over yet perhaps the biggest drawback to asymptotics is that it is very difficult to determine in a given situation which of these possibilities is occurring

a level set model for image classification we present a supervised classification model based on a variational approach this model is devoted to find an optimal partition composed of homogeneous classes with regular interfaces the originality of the proposed approach concerns the definition of a partition by the use of level sets each set of regions and boundaries associated to a class is defined by a unique level set function we use as many level sets as different classes and all these level sets are moving together thanks to forces which interact in order to get an optimal partition we show how these forces can be defined through the minimization of a unique fonctional the coupled partial differential equations pde related to the minimization of the functional are considered through a dynamical scheme given an initial interface set zero level set the different terms of the pde s are governing the motion of interfaces such that at convergence we get an optimal partition as defined above each interface is guided by internal forces regularity of the interface and external ones data term no vacuum no regions overlapping several experiments were conducted on both synthetic and real images

annihilating filter based low rank hankel matrix approach for image inpainting in this paper we propose a patch based image inpainting method using a low rank hankel structured matrix completion approach the proposed method exploits the annihilation property between a shift invariant filter and image data observed in many existing inpainting algorithms in particular by exploiting the commutative property of the convolution the annihilation property results in a low rank block hankel structure data matrix and the image inpainting problem becomes a low rank structured matrix completion problem the block hankel structured matrices are obtained patch by patch to adapt to the local changes in the image statistics to solve the structured low rank matrix completion problem we employ an alternating direction method of multipliers with factorization matrix initialization using the low rank matrix fitting algorithm as a side product of the matrix factorization locally adaptive dictionaries can be also easily constructed despite the simplicity of the algorithm the experimental results using irregularly subsampled images as well as various images with globally missing patterns showed that the proposed method outperforms existing state of the art image inpainting methods

stochastic relaxation on partitions with connected components and its application to image segmentation we present a new method of segmentation in which images are segmented in partitions with connected components we give computationally inexpensive algorithms for probability simulation and simulated annealing on the space of partitions with connected components of a general graph in particular hastings algorithms 1970 and generalized metropolis algorithms are defined to avoid heavy computation to achieve segmentation we propose a hierarchical approach which at each step minimizes a cost function on the space of partitions with connected components of a graph the algorithm is applied to segment gray level color and textured images

maximum likelihood parameter estimation of textures using a wold decomposition based model we present a solution to the problem of modeling parameter estimation and synthesis of natural textures the texture field is assumed to be a realization of a regular homogeneous random field which can have a mixed spectral distribution on the basis of a 2 d wold like decomposition the field is represented as a sum of a purely indeterministic component a harmonic component and a countable number of evanescent fields we present a maximum likelihood solution to the joint parameter estimation problem of these components from a single observed realization of the texture field the proposed solution is a twostage algorithm in the first stage we obtain an estimate for the number of harmonic and evanescent components in the field and a suboptimal initial estiqate for the parameters of their spectral supports in the second stage we refine these initial estimates by iterative maximization of the likelihood function of the observed data by introducing appropriate parameter transformations the highly nonlinear least squares problem that results from the maximization of the likelihood function is transformed into a separable least squares problem in this new problem the solution for the unknown spectral supports of the harmonic and evanescent components reduces the problem of solving for the transformed parameters of the field to linear least squares solution of the transformation equations then provides a complete solution of the field model parameter estimation problem the wold based model and the resulting analysis and synthesis algorithms are seen applicable to a wide variety of texture types found in natural images the support or shape of the analyzed texture patch may be arbitrary our model is very efficient in terms of the number of parameters required to represent and faithfully reconstruct the original texture

a variational model for image classification and restoration we present a variational model devoted to image classification coupled with an edge preserving regularization process the discrete nature of classification i e to attribute a label to each pixel has led to the development of many probabilistic image classification models but rarely to variational ones in the last decade the variational approach has proven its efficiency in the field of edge preserving restoration we add a classification capability which contributes to provide images composed of homogeneous regions with regularized boundaries a region being defined as a set of pixels belonging to the same class the soundness of our model is based on the works developed on the phase transition theory in mechanics the proposed algorithm is fast easy to implement and efficient we compare our results on both synthetic and satellite images with the ones obtained by a stochastic model using a potts regularization

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

fuzzy model based clustering and its application in image segmentation a fuzzy generalized gaussian density ggd segmentation model is proposed a ggd based agglomerative fuzzy algorithm is developed a two stage fuzzy ggd segmentation algorithm is proposed this paper proposes a novel fuzzy model based unsupervised learning algorithm with boundary correction for image segmentation we propose a fuzzy generalized gaussian density ggd segmentation model and the ggd based agglomerative fuzzy algorithm for grouping image pixels the merits of algorithm are that it is not sensitive to initial parameters and that the number of groups can be estimated via the validation technique to minimize the objective function of the model we define a dissimilarity measure based on the kullbackleibler divergence of the ggds that computes the discrepancy between ggds in the space of generalized probability distributions to effectively segment images with various textures we propose a two stage fuzzy ggd segmentation algorithm the first stage adopts the proposed fuzzy algorithm to obtain initial segmentation and the second stage improves initial segmentation by image boundary correction experimental results show that our proposed method has a promising performance compared with existing approaches

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

turbo segmentation of textured images we consider the problem of semi supervised segmentation of textured images existing model based approaches model the intensity field of textured images as a gauss markov random field to take into account the local spatial dependencies between the pixels classical bayesian segmentation consists of also modeling the label field as a markov random field to ensure that neighboring pixels correspond to the same texture class with high probability well known relaxation techniques are available which find the optimal label field with respect to the maximum a posteriori or the maximum posterior mode criterion but these techniques are usually computationally intensive because they require a large number of iterations to converge in this paper we propose a new bayesian framework by modeling two dimensional textured images as the concatenation of two one dimensional hidden markov autoregressive models for the lines and the columns respectively a segmentation algorithm which is similar to turbo decoding in the context of error correcting codes is obtained based on a factor graph approach the proposed method estimates the unknown parameters using the expectation maximization algorithm

likelihood calculation for a class of multiscale stochastic models with application to texture discrimination a class of multiscale stochastic models based on scale recursive dynamics on trees has previously been introduced theoretical and experimental results have shown that these models provide an extremely rich framework for representing both processes which are intrinsically multiscale e g 1 f processes as well as 1d markov processes and 2d markov random fields moreover efficient optimal estimation algorithms have been developed for these models by exploiting their scale recursive structure the authors exploit this structure in order to develop a computationally efficient and parallelizable algorithm for likelihood calculation they illustrate one possible application to texture discrimination and demonstrate that likelihood based methods using the algorithm achieve performance comparable to that of gaussian markov random field based techniques which in general are prohibitively complex computationally

game theoretic integration for image segmentation robust segmentation of structures from an image is essential for a variety of image analysis problems however the conventional methods of region based segmentation and gradient based boundary finding are often frustrated by poor image quality here we propose a method to integrate the two approaches using game theory in an effort to form a unified approach that is robust to noise and poor initialization this combines the perceptual notions of complete boundary information using edge data and shape priors with gray level homogeneity using two computational modules the novelty of the method is that this is a bidirectional framework whereby both computational modules improve their results through mutual information sharing a number of experiments were performed both on synthetic datasets and datasets of real images to evaluate the new approach and it is shown that the integrated method typically performs better than conventional gradient based boundary finding

pixel level singular point detection from multi scale gaussian filtered orientation field singular point as a global feature plays an important role in fingerprint recognition inconsistent detection of singular points apparently gives an affect to fingerprint alignment classification and verification accuracy this paper proposes a novel approach to pixel level singular point detection from the orientation field obtained by multi scale gaussian filters initially a robust pixel level orientation field is estimated by a multi scale averaging framework then candidate singular points in pixel level are extracted from the complex angular gradient plane derived directly from the pixel level orientation field the candidate singular points are finally validated via a cascade framework comprised of nested poincare indices and local feature based classification experimental results over the fvc 2000 db2 confirm that the proposed method achieves robust and accurate orientation field estimation and consistent pixel level singular point detection the experimental results exhibit a low computational cost with better performance thus the proposed method can be employed in real time fingerprint recognition

structural texture similarity metrics for image analysis and retrieval we develop new metrics for texture similarity that accounts for human visual perception and the stochastic nature of textures the metrics rely entirely on local image statistics and allow substantial point by point deviations between textures that according to human judgment are essentially identical the proposed metrics extend the ideas of structural similarity and are guided by research in texture analysis synthesis they are implemented using a steerable filter decomposition and incorporate a concise set of subband statistics computed globally or in sliding windows we conduct systematic tests to investigate metric performance in the context of known item search the retrieval of textures that are identical to the query texture this eliminates the need for cumbersome subjective tests thus enabling comparisons with human performance on a large database our experimental results indicate that the proposed metrics outperform peak signal to noise ratio psnr structural similarity metric ssim and its variations as well as state of the art texture classification metrics using standard statistical measures

bayesian pixel classification using spatially variant finite mixtures and the generalized em algorithm a spatially variant finite mixture model is proposed for pixel labeling and image segmentation for the case of spatially varying mixtures of gaussian density functions with unknown means and variances an expectation maximization em algorithm is derived for maximum likelihood estimation of the pixel labels and the parameters of the mixture densities an a priori density function is formulated for the spatially variant mixture weights a generalized em algorithm for maximum a posteriori estimation of the pixel labels based upon these prior densities is derived this algorithm incorporates a variation of gradient projection in the maximization step and the resulting algorithm takes the form of grouped coordinate ascent gaussian densities have been used for simplicity but the algorithm can easily be modified to incorporate other appropriate models for the mixture model component densities the accuracy of the algorithm is quantitatively evaluated through monte carlo simulation and its performance is qualitatively assessed via experimental images from computerized tomography ct and magnetic resonance imaging mri

a sequence based extension of mean field annealing using the forward backward algorithm application to image segmentation optimization based on mean field theory including mean field annealing mfa is widely used for discrete optimization label assignment problems defined on the pixel sites of an image one formulation of mfa is via maximum entropy where one seeks the joint distribution over the random assignments subject to an average level of cost mfa is obtained by assuming the assignments at each pixel are statistically independent given the observed image alternatively we make the less restrictive assumption of independent row labelings the independence assumption means that at each step mfa optimizes over only one pixel whereas our method jointly optimizes over an entire row i e our method is less greedy in principle an mfa extension could be developed that explicitly re estimates the row labeling distributions but such an approach is in practice infeasible even so we can indirectly implement this re estimation by re estimating quantities that determine the row labeling distributions these quantities are the a posteriori site probabilities re estimable via the well known forward backward f b algorithm thus our algorithm which descends in the me lagrangian free energy consists of iterative application of f b to the image rows columns at convergence maximum a posteriori site labeling is performed our method was applied to segmentation of both real and synthetic noise corrupted images it achieved lower markov random field model potentials and better segmentations compared with other methods and in high noise standard mfa

local anisotropy analysis for non smooth images identification of local anisotropy and determination of principal axes are addressed through different methods that are designed to be tolerant to the non smooth character of images on the pixel scale these different tools are validated on various examples and their performances are compared the most powerful robust and accurate method consists in computing the curvature tensor of the auto correlation function of regularized images using fast fourier transforms

systematic methods for the computation of the directional fields and singular points of fingerprints the first subject of the paper is the estimation of a high resolution directional field of fingerprints traditional methods are discussed and a method based on principal component analysis is proposed the method not only computes the direction in any pixel location but its coherence as well it is proven that this method provides exactly the same results as the averaged square gradient method that is known from literature undoubtedly the existence of a completely different equivalent solution increases the insight into the problem s nature the second subject of the paper is singular point detection a very efficient algorithm is proposed that extracts singular points from the high resolution directional field the algorithm is based on the poincare index and provides a consistent binary decision that is not based on postprocessing steps like applying a threshold on a continuous resemblance measure for singular points furthermore a method is presented to estimate the orientation of the extracted singular points the accuracy of the methods is illustrated by experiments on a live scanned fingerprint database

feature extraction for texture discrimination via random field models with random spatial interaction in this paper we attack the problem of distinguishing textured images of real surfaces using small samples we first analyze experimental data that results from applying ordinary conditional markov fields in the face of the disappointing performance of these models we introduce a random field with spatial interaction that is itself a random variable usually referred to as a random field in a random environment for this class of models we establish the power spectrum and the autocorrelation function as well defined quantities and we devise a scheme for the estimation of related parameters the new set of features that resulted from this approach was applied to real images accurate discrimination was observed even for boxes of size 10 spl times 16

a reference bayesian test for nested hypotheses and its relationship to the schwarz criterion abstract to compute a bayes factor for testing h 0 0 in the presence of a nuisance parameter priors under the null and alternative hypotheses must be chosen as in bayesian estimation an important problem has been to define automatic or reference methods for determining priors based only on the structure of the model in this article we apply the heuristic device of taking the amount of information in the prior on equal to the amount of information in a single observation then after transforming to be null orthogonal to we take the marginal priors on to be equal under the null and alternative hypotheses doing so and taking the prior on to be normal we find that the log of the bayes factor may be approximated by the schwarz criterion with an error of order o p n rather than the usual error of order o p 1 this result suggests the schwarz criterion should provide sensible approximate solutions to bayesian testing problems at least when the hypotheses are nested when

pattern analysis of dermoscopic images based on markov random fields in this paper a method for detecting different patterns in dermoscopic images is presented in order to diagnose a possible skin cancer physicians assess the lesion based on different rules while the most famous one is the abcd rule asymmetry border colour diameter the new tendency in dermatology is to classify the lesion performing a pattern analysis due to the colour textured appearance of these patterns this paper presents a novel method based on markov random field mrf extended for colour images that classifies images representing different dermatologic patterns first each image plane in l a b colour space is modelled as a mrf following a finite symmetric conditional model fscm coupling of colour components is taken into account by supposing that features of the mrf in the three colour planes follow a multivariate normal distribution performance is analysed in different colour spaces the best classification rate is 86 on average

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

non uniqueness of solutions to the cauchy problem for semilinear heat equations with singular initial data the cauchy problem for semilinear heat equations with singular initial data is studied where n 2 0 is a parameter and a 0 a 0 we show that when p n 2 n and n 2 p n 2 there exists a positive constant such that the problem has two positive self similar solutions and with if and no positive self similar solutions if furthermore for each fixed and in l rn as 0 where w0 is a non unique solution to the problem with zero initial data which is constructed by haraux and weissler

a nonlinear orientation model for global description of fingerprints a nonlinear orientation model for the representation of the orientation matrix of a fingerprint image is presented a general procedure to obtain the set of parameters describing this model is shown particularly a piecewise linear orientation function is analysed the model compared with a previous zero pole model shows substantial decrease in the orientation error the application of this model for efficient identity verification based on the ridge orientation is explored

singular points detection based on zero pole model in fingerprint images an algorithm is proposed which combines zero pole model and hough transform ht to detect singular points orientation of singular points is defined on the basis of the zero pole model which can further explain the practicability of zero pole model contrary to orientation field generation detection of singular points is simplified to determine the parameters of the zero pole model ht uses rather global information of fingerprint images to detect singular points this makes our algorithm more robust to noise than methods that only use local information as the zero pole model may have a little warp from actual fingerprint orientation field poincare index is used to make position adjustment in neighborhood of the detected candidate singular points experimental results show that our algorithm performs well and fast enough for real time application in database nist 4

multiscale representations of markov random fields recently a framework for multiscale stochastic modeling was introduced based on coarse to fine scale recursive dynamics defined on trees this model class has some attractive characteristics which lead to extremely efficient statistically optimal signal and image processing algorithms the authors show that this model class is also quite rich in particular they describe how 1 d markov processes and 2 d markov random fields mrfs can be represented within this framework the recursive structure of 1 d markov processes makes them simple to analyze and generally leads to computationally efficient algorithms for statistical inference on the other hand 2 d mrfs are well known to be very difficult to analyze due to their noncausal structure and thus their use typically leads to computationally intensive algorithms for smoothing and parameter identification in contrast their multiscale representations are based on scale recursive models and thus lead naturally to scale recursive algorithms which can be substantially more efficient computationally than those associated with mrf models in 1 d the multiscale representation is a generalization of the midpoint deflection construction of brownian motion the representation of 2 d mrfs is based on a further generalization to a midline deflection construction the exact representations of 2 d mrfs are used to motivate a class of multiscale approximate mrf models based on one dimensional wavelet transforms they demonstrate the use of these latter models in the context of texture representation and in particular they show how they can be used as approximations for or alternatives to well known mrf texture models

simple parallel hierarchical and relaxation algorithms for segmenting noncausal markovian random fields the modeling and segmentation of images by mrf s markov random fields is treated these are two dimensional noncausal markovian stochastic processes two conceptually new algorithms are presented for segmenting textured images into regions in each of which the data are modeled as one of c mrf s the algorithms are designed to operate in real time when implemented on new parallel computer architectures that can be built with present technology a doubly stochastic representation is used in image modeling here a gaussian mrf is used to model textures in visible light and infrared images and an autobinary or autoternary etc mrf to model a priori information about the local geometry of textured image regions for image segmentation the true texture class regions are treated either as a priori completely unknown or as a realization of a binary or ternary etc mrf in the former case image segmentation is realized as true maximum likelihood estimation in the latter case it is realized as true maximum a posteriori likelihood segmentation in addition to providing a mathematically correct means for introducing geometric structure the autobinary or ternary etc mrf can be used in a generative mode to generate image geometries and artificial images and such simulations constitute a very powerful tool for studying the effects of these models and the appropriate choice of model parameters the first segmentation algorithm is hierarchical and uses a pyramid like structure in new ways that exploit the mutual dependencies among disjoint pieces of a textured region

a very singular solution for the slow diffusion equation with nonlinear convection abstract we here investigate an existence and uniqueness of the nontrivial nonnegative solution of a nonlinear ordinary differential equation f m r f f f q 0 satisfying a specific decay rate lim r r f r 0 with 1 2 q m 1 and q m 2 q m 1 here m 1 m q m 1 and 1 or 1 such a solution arises naturally when we study a very singular solution for a slow diffusion equation with nonlinear convection u t u m x x u q x defined either on the whole real line or on the half line 0

fractional bayes factors for model comparison bayesian comparison of models is achieved simply by calculation of posterior probabilities of the models themselves however there are difficulties with this approach when prior information about the parameters of the various models is weak partial bayes factors offer a resoiution of the problem by setting aside part of the data as a training sampie the training sampie is used to obtain an initiai informative posterior distribution of the parameters in each model model comparison is then based on a bayes factor calculated from the remaining data properties of partial bayes factors are discussed particularly in the context of weak prior information and they are found to have advantages over other proposed methods of model comparison a new variant of the partial bayes factor the fractional bayes factor is advocated on grounds of consistency simplicity robustness and coherence

on the spatial distribution of fingerprint singularities fingerprint singularities play an important role in several fingerprint recognition and classification systems although some general relationships and constraints about the location of singularities in the different fingerprint classes are well known to the best of our knowledge no statistical models have been developed until now this paper studies the spatial distributions of singularity locations in nature and derives from a representative dataset of labelled samples the probability density functions of the four main fingerprint classes the results obtained can be directly exploited to improve the accuracy of many techniques relying on the position of singularities as confirmed by the results of two experiments on fingerprint classification and synthesis

unmasking multivariate outliers and leverage points abstract detecting outliers in a multivariate point cloud is not trivial especially when there are several outliers the classical identification method does not always find them because it is based on the sample mean and covariance matrix which are themselves affected by the outliers that is how the outliers get masked to avoid the masking effect we propose to compute distances based on very robust estimates of location and covariance these robust distances are better suited to expose the outliers in the case of regression data the classical least squares approach masks outliers in a similar way also here the outliers may be unmasked by using a highly robust regression method finally a new display is proposed in which the robust regression residuals are plotted versus the robust distances this plot classifies the data into regular observations vertical outliers good leverage points and bad leverage points several examples are discussed

detection of singular points in fingerprint images abstract this paper describes a technique for the detection of singular points in a fingerprint image the singular points namely the core and the delta points are used as points of registration in fingerprint matching this method makes use of structural information culled from the directional histograms of the directional image of a fingerprint the usage of the histogram allows for a fairly high degree of noise tolerance

wavelet based level set evolution for classification of textured images we present a supervised classification model based on a variational approach this model is specifically devoted to textured images we want to get a partition of an image composed of texture regions separated by regular interfaces each kind of texture defines a class we use a wavelet packet transform to analyze the textures characterized by their energy distribution in each sub band in order to have an image segmentation according to the classes we model the regions and their interfaces by level set functions we define a functional on these level sets whose minimizers define the optimal classification according to texture a system of coupled pdes is deduced from the functional by solving this system each region evolves according to its wavelet coefficients and interacts with the neighbor regions in order to obtain a partition with regular contours experiments are shown on synthetic and real images

a clustering method based on multidimensional texture analysis considering the analogy between image segmentation and cluster analysis the aim of this paper is to adapt statistical texture measures to describe the spatial distribution of multidimensional observations the main idea is to consider the cluster cores as domains characterized by their specific textures in the data space the distribution of the data points is first described as a multidimensional histogram defined on a multidimensional regular array of sampling points in order to evaluate locally a multidimensional texture a co occurrence matrix is introduced which characterizes the local distribution of the data points in the multidimensional data space several local texture features can be computed from this co occurrence matrix which accumulates spatial and statistical information on the data distribution in the neighborhoods of the sampling points texture features are selected according to their ability to discriminate different distributions of data points the sampling points where the local underlying texture is evaluated are categorized into different texture classes the points assigned to these classes tend to form connected components in the data space which are considered as the cores of the clusters

weighted parzen windows for pattern classification this paper introduces the weighted parzen window classifier the proposed technique uses a clustering procedure to find a set of reference vectors and weights which are used to approximate the parzen window kernel estimator classifier the weighted parzen window classifier requires less computation and storage than the full parzen window classifier experimental results showed that significant savings could be achieved with only minimal if any error rate degradation for synthetic and real data sets

texture segmentation using gaussian markov random fields and neural oscillator networks we propose an image segmentation method based on texture analysis our method is composed of two parts the first part determines a novel set of texture features derived from a gaussian markov random fields gmrf model unlike a gmrf based approach our method does not employ model parameters as features or require the extraction of features for a fixed set of texture types a priori the second part is a 2d array of locally excitatory globally inhibitory oscillator networks legion after being filtered for noise suppression features are used to determine the local couplings in the network when legion runs the oscillators corresponding to the same texture tend to synchronize whereas different texture regions tend to correspond to distinct phases in simulations a large system of differential equations is solved for the first time using a recently proposed method for integrating relaxation oscillator networks we provide results on real texture images to demonstrate the performance of our method

fingerprint reference point detection for image retrieval based on symmetry and variation reference point plays an important role in fingerprint identification systems the reference point is widely used for the fingerprint retrieval in large scale databases this paper proposes a novel algorithm for detecting a convex core point as a unique reference point consistently and accurately for all types of fingerprints in order to detect robust core point candidates a modified complex filter called semi radial symmetry filter is proposed to detect correctly rotational symmetries of core points moreover a vertical orientation variation feature called voriv feature is proposed to remove spurious core points and concave core points therefore the proposed technique computes the variation and symmetry combined energy vscome then the reference point is located by searching the global vscome maximum the experimental results on the public database fvc2004 db1 set a show that the proposed technique exhibits a very high robustness and gets the best performance in comparing with other approaches in literature

bayesian density estimation and inference using mixtures abstract we describe and illustrate bayesian inference in models for density estimation using mixtures of dirichlet processes these models provide natural settings for density estimation and are exemplified by special cases where data are modeled as a sample from mixtures of normal distributions efficient simulation methods are used to approximate various prior posterior and predictive distributions this allows for direct inference on a variety of practical issues including problems of local versus global smoothing uncertainty about density estimates assessment of modality and the inference on the numbers of components also convergence results are established for a general class of normal mixture models

practical bayesian density estimation using mixtures of normals abstract mixtures of normals provide a flexible model for estimating densities in a bayesian framework there are some difficulties with this model however first standard reference priors yield improper posteriors second the posterior for the number of components in the mixture is not well defined if the reference prior is used third posterior simulation does not provide a direct estimate of the posterior for the number of components we present some practical methods for coping with these problems finally we give some results on the consistency of the method when the maximum number of components is allowed to grow with the sample size

variational problems related to self similar solutions of the heat equation on demontre la compacite de certains plongements de sobolev sous de bonnes conditions sur les poids k x exp x c 2 r n r on considere des methodes variationnelles pour la resolution d equations elliptiques

fingerprint ridge orientation field reconstruction using the best quadratic approximation by orthogonal polynomials in two discrete variables this paper proposes a novel algorithm for reconstructing the fingerprint orientation field fof the basic idea of the algorithm is to reconstruct the ridge orientation by using the best quadratic approximation by orthogonal polynomials in two discrete variables we first estimate the local region orientation by the linear projection analysis lpa based on the vector set of point gradients and then reconstruct the ridge orientation field using the best quadratic approximation by orthogonal polynomials in two discrete variables in the sine domain in this way we solve the problem that is difficult to accurately extract low quality fingerprint image orientation fields the experiments with the database of fvc 2004 show that compared to the state of the art fingerprint orientation estimation algorithms the proposed method is more accurate and more robust against noise and is able to better estimate the fof of low quality fingerprint images with large areas of noise

classification of singular solutions of a nonlinear heat equation on considere toutes les solutions non negatives de l equation de la chaleur non lineaire avec absorption u t u m u p dans q r n 0 t pour n 1 m p 1 avec des conditions initiales u x 0 0 pour x 0

a unified texture model based on a 2 d wold like decomposition a unified texture model that is applicable to a wide variety of texture types found in natural images is presented this model leads to the derivation of texture analysis and synthesis algorithms designed to estimate the texture parameters and to reconstruct the original texture field from these parameters the texture field is assumed to be a realization of a regular homogeneous random field which is characterized in general by a mixed spectral distribution the texture field is orthogonally decomposed into a purely indeterministic component and a deterministic component the deterministic component is further orthogonally decomposed into a harmonic component and a generalized evanescent component both analytical and experimental results show that the deterministic components should be parameterized separately from the purely indeterministic component the model is very efficient in terms of the number of parameters required to faithfully represent textures reconstructed textures are practically indistinguishable from the originals

a combination model for orientation field of fingerprints orientation field is a global feature of fingerprints that is very important in automatic fingerprint identification systems afis establishing an accurate and concise model for orientation fields will not only improve the performance of orientation estimation but also make it feasible to apply orientation information in the matching process in this paper a novel model for the orientation field of fingerprints is proposed we use a polynomial model to approximate the orientation field globally and a point charge model at each singular point to improve the approximation locally these two models are combined together by a weight function experimental results are provided to illustrate the fact that this combination model is more accurate and robust with respect to noise compared with the previous works the application of the model is discussed at the end

probability density function estimation with the frequency polygon transform a probability density function estimator is proposed which is based on frequency polygons its convergence to the true density is formally proved a mode finding algorithm is also proposed as an alternative to mean shift our approach outperforms histogram and kernel based estimators in synthetic and real datasets our proposal is shown to be suitable to object tracking in video sequences most current nonparametric approaches to probability density function estimation are based on the kernel density estimator also known as the parzen window estimator a usual alternative is the multivariate histogram which features a low computational complexity multivariate frequency polygons have often been neglected even though they share many of the advantages of the histograms while they are continuous unlike the histograms here we build on our previous work on histograms in order to propose a new probability density estimator which is based on averaging multivariate frequency polygons the convergence of the estimator is formally proved experiments are carried out with synthetic and real machine learning datasets finally image denoising and object tracking applications are also considered

nonparametric maximum likelihood estimation by the method of sieves maximum likelihood estimation often fails when the parameter takes values in an infinite dimensional space for example the maximum likelihood method cannot be applied to the completely nonparametric estimation of a density function from an operatorname iid sample the maximum of the likelihood is not attained by any density in this example as in many other examples the parameter space positive functions with area one is too big but the likelihood method can often be salvaged if we first maximize over a constrained subspace of the parameter space and then relax the constraint as the sample size grows this is grenander s method of sieves application of the method sometimes leads to new estimators for familiar problems or to a new motivation for an already well studied technique we will establish some general consistency results for the method and then we will focus on three applications

estimation and choice of neighbors in spatial interaction models of images some aspects of statistical inference for a class of spatial interaction models for finite images are presented primarily the simultaneous autoregressive sar models and conditional markov cm models each of these models is characterized by a set of neighbors a set of coefficients and a noise sequence of specified characteristics we are concerned with two problems the estimation of the unknown parameters in both sar and cm models and the choice of an appropriate model from a class of such competing models assuming gaussian distributed variables we discuss maximum likelihood ml estimation methods in general the ml scheme leads to nonlinear optimization problems to avoid excessive computation an iterative scheme is given for sar models which gives approximate ml estimates in the gaussian case and reasonably good estimates in some non gaussian situations as well likewise for cm models an easily computable consistent estimate is given the asymptotic mean squared error mse of this estimate for a four neighbor cm model is shown tn be substantially less than the mse of the popular coding estimate asymptotically consistent decision rules are given for choosing an appropriate sar or cm model the usefulness of the estimation scheme and the decision rule for the choice of neighbors is illustrated by using synthetic patterns synthetic patterns obeying known sar and cm models are generated and the models corresponding to true and several competing neighbor sets are fitted the estimation scheme yields estimates close to the parameters of the true models and the decision rule for the choice of neighbors picks up the true model from the class of competing models

a markov pixon information approach for low level image description the problem of extracting information from an image which corresponds to early stage processing in vision is addressed we propose a new approach the mpi approach which simultaneously provides a restored image a segmented image and a map which reflects the local scale for representing the information embedded in a bayesian framework this approach is based on an information prior a pixon model and two markovian priors this model based approach is oriented to detect and analyze small parabolic patches in a noisy environment the number of clusters and their parameters are not required for the segmentation process the mpi approach is applied to the analysis of statistical parametric maps obtained from fmri experiments

density estimation with confidence sets exemplified by superclusters and voids in the galaxies abstract a method is presented for forming both a point estimate and a confidence set of semiparametric densities the final product is a three dimensional figure that displays a selection of density estimates for a plausible range of smoothing parameters the boundaries of the smoothing parameter are determined by a nonparametric goodness of fit test that is based on the sample spacings for each value of the smoothing parameter our estimator is selected by choosing the normal mixture that maximizes a function of the sample spacings a point estimate is selected from this confidence set by using the method of cross validation an algorithm to find the mixing distribution that maximizes the spacings functional is presented these methods are illustrated with a data set from the astronomy literature the measurements are velocities at which galaxies in the corona borealis region are moving away from our galaxy if the galaxies are clustered the velocity density will be multimodal with clusters correspond

a variational formulation for fingerprint orientation modeling orientation pattern is an important feature for characterizing fingerprint and plays critical roles in fingerprint recognition and fingerprint classification this paper proposes a framework for modeling the fingerprint orientation field based on the variational principle where the orientation pattern can be estimated through solving the associated euler lagrange equation compared with existing methods our proposed method has the following features firstly it does not require any prior information about the structure of the acquired fingerprint such as location of singular point s secondly it explicitly provides freedom for modeling the singularity in the orientation field thirdly it has less number of parameters comparison has been made with respect to state of the arts in fingerprint orientation modeling in terms of modeling accuracy fingerprint enhancement and singular point detection advantages of the proposed method are demonstrated

model based classification methods of global patterns in dermoscopic images in this paper different model based methods of classification of global patterns in dermoscopic images are proposed global patterns identification is included in the pattern analysis framework the melanoma diagnosis method most used among dermatologists the modeling is performed in two senses first a dermoscopic image is modeled by a finite symmetric conditional markov model applied to l a b color space and the estimated parameters of this model are treated as features in turn the distribution of these features are supposed that follow different models along a lesion a gaussian model a gaussian mixture model and a bag of features histogram model for each case the classification is carried out by an image retrieval approach with different distance metrics the main objective is to classify a whole pigmented lesion into three possible patterns globular homogeneous and reticular an extensive evaluation of the performance of each method has been carried out on an image database extracted from a public atlas of dermoscopy the best classification success rate is achieved by the gaussian mixture model based method with a 78 44 success rate in average in a further evaluation the multicomponent pattern is analyzed obtaining a 72 91 success rate

image segmentation based on grabcut framework integrating multiscale nonlinear structure tensor in this paper we propose an interactive color natural image segmentation method the method integrates color feature with multiscale nonlinear structure tensor texture msnst feature and then uses grabcut method to obtain the segmentations the msnst feature is used to describe the texture feature of an image and integrated into grabcut framework to overcome the problem of the scale difference of textured images in addition we extend the gaussian mixture model gmm to msnst feature and gmm based on msnst is constructed to describe the energy function so that the texture feature can be suitably integrated into grabcut framework and fused with the color feature to achieve the more superior image segmentation performance than the original grabcut method for easier implementation and more efficient computation the symmetric kl divergence is chosen to produce the estimates of the tensor statistics instead of the riemannian structure of the space of tensor the conjugate norm was employed using locality preserving projections lpp technique as the distance measure in the color space for more discriminating power an adaptive fusing strategy is presented to effectively adjust the mixing factor so that the color and msnst texture features are efficiently integrated to achieve more robust segmentation performance last an iteration convergence criterion is proposed to reduce the time of the iteration of grabcut algorithm dramatically with satisfied segmentation accuracy experiments using synthesis texture images and real natural scene images demonstrate the superior performance of our proposed method

statistical approach to segmentation of single channel cerebral mr images a statistical model is presented that represents the distributions of major tissue classes in single channel magnetic resonance mr cerebral images using the model cerebral images are segmented into gray matter white matter and cerebrospinal fluid csf the model accounts for random noise magnetic field inhomogeneities and biological variations of the tissues intensity measurements are modeled by a finite gaussian mixture smoothness and piecewise contiguous nature of the tissue regions are modeled by a three dimensional 3 d markov random field mrf a segmentation algorithm based on the statistical model approximately finds the maximum a posteriori map estimation of the segmentation and estimates the model parameters from the image data the proposed scheme for segmentation is based on the iterative conditional modes icm algorithm in which measurement model parameters are estimated using local information at each site and the prior model parameters are estimated using the segmentation after each cycle of iterations application of the algorithm to a sample of clinical mr brain scans comparisons of the algorithm with other statistical methods and a validation study with a phantom are presented the algorithm constitutes a significant step toward a complete data driven unsupervised approach to segmentation of mr images in the presence of the random noise and intensity inhomogeneities

estimation of finite mixture distributions through bayesian sampling summary a formal bayesian analysis of a mixture model usually leads to intractable calculations since the posterior distribution takes into account all the partitions of the sample we present approximation methods which evaluate the posterior distribution and bayes estimators by gibbs sampling relying on the missing data structure of the mixture model the data augmentation method is shown to converge geometrically since a duality principle transfers properties from the discrete missing data chain to the parameters the fully conditional gibbs alternative is shown to be ergodic and geometric convergence is established in the normal case we also consider non informative approximations associated with improper priors assuming that the sample corresponds exactly to a k component mixture

unsupervised image segmentation using an unlabeled region process abstract the segmentation of a gray scale image of regions of uniform or slowly varying intensity is discussed a gibbsian representation is given for a partition of a lattice with no labels the homogeneity of each segment is measured by the sample variance of observations in a region merging algorithm the posterior probability of the segmentation is gradually increased it is shown how to simulate partitions with the gibbs sampler and how to combine information in simulated segmentations when the segments cannot be grouped into genuine classes the segmentation methods are preferable to classification by iterated conditional modes icm or by the maximizer of posterior marginals mpm

a novel algorithm for detecting singular points from fingerprint images fingerprint analysis is typically based on the location and pattern of detected singular points in the images these singular points cores and deltas not only represent the characteristics of local ridge patterns but also determine the topological structure i e fingerprint type and largely influence the orientation field in this paper we propose a novel algorithm for singular points detection after an initial detection using the conventional poincare index method a so called doric feature is used to remove spurious singular points then the optimal combination of singular points is selected to minimize the difference between the original orientation field and the model based orientation field reconstructed using the singular points a core delta relation is used as a global constraint for the final selection of singular points experimental results show that our algorithm is accurate and robust giving better results than competing approaches the proposed detection algorithm can also be used for more general 2d oriented patterns such as fluid flow motion and so forth

unsupervised texture segmentation using markov random field models the problem of unsupervised segmentation of textured images is considered the only explicit assumption made is that the intensity data can be modeled by a gauss markov random field gmrf the image is divided into a number of nonoverlapping regions and the gmrf parameters are computed from each of these regions a simple clustering method is used to merge these regions the parameters of the model estimated from the clustered segments are then used in two different schemes one being all approximation to the maximum a posterior estimate of the labels and the other minimizing the percentage misclassification error the proposed approach is contrasted with the algorithm of s lakshamanan and h derin 1989 which uses a simultaneous parameter estimation and segmentation scheme the results of the adaptive segmentation algorithm of lakshamanan and derin are compared with a simple nearest neighbor classification scheme to show that if enough information is available simple techniques could be used as alternatives to computationally expensive schemes

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