a new method for adding a parameter to a family of distributions with application to the exponential and weibull families summary a new way of introducing a parameter to expand a family of distributions is introduced and applied to yield a new two parameter extension of the exponential distribution which may serve as a competitor to such commonly used two parameter families of life distributions as the weibull gamma and lognormal distributions in addition the general method is applied to yield a new three parameter weibull distribution families expanded using the method introduced here have the property that the minimum of a geometric number of independent random variables with common distribution in the family has a distribution again in the family bivariate versions are also considered

random effects cox models a poisson modelling approach summary we propose a poisson modelling approach to nested random effects cox proportional hazards models an important feature of this approach is that the principal results depend only on the first and second moments of the unobserved random effects the orthodox best linear unbiased predictor approach to random effects poisson modelling techniques enables us to justify appropriate consistency and optimality the explicit expressions for the random effects given by our approach facilitate incorporation of a relatively large number of random effects the use of the proposed methods is illustrated through the reanalysis of data from a large scale cohort study of particulate air pollution and mortality previously reported by pope et al 1995

multivariate elliptically contoured distributions for repeated measurements summary the multivariate power exponential distribution a member of the multivariate elliptically contoured family provides a useful generalization of the multivariate normal distribution for the modeling of repeated measurements both light and heavy tailed distributions are included the covariance matrix retains its interpretation so that it can easily be structured for serial dependence and several levels of variance components a crossover trial on insulin applied to rabbits with a series of repeated measurements within each period is analyzed by means of this distribution using autocorrelation and two levels of variance components

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

diffusions for global optimizations we seek a global minimum of u 0 1 n to r the solution to d dt x t nabla u x t will find local minima the solution to dx t nabla u x t dt sqrt 2t dw t where w is standard n dimensional brownian motion and the boundaries are reflecting will concentrate near the global minima of u at least when temperature t is small the equilibrium distribution for x t is gibbs with density pi t x alpha exp u x t this suggests setting t t t downarrow 0 to find the global minima of u we give conditions on u x and t t such that the solution to dx t nabla u x t dt sqrt 2t dw t converges weakly to a distribution concentrated on the global minima of u

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

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

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

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

modelling irregularly sampled profiles of non negative dog triglyceride responses under different distributional assumptions general methodology for modelling series of non negative data observed at unequally spaced times is developed the parameterization enables both the importance of the serial association as well the order of this dependence to be expressed an example is given where the effects of three fibre based diets on dog triglyceride profiles are analysed and compared many different types of models based on common distributions such as the normal exponential gamma weibull and log normal observations are presented comparison of possibly non nested models fitted on the same data set is made using the akaike criterion

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

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

zero inflated poisson regression with an application to defects in manufacturing zero inflated poisson zip regression is a model for count data with excess zeros it assumes that with probability p the only possible observation is 0 and with probability 1 p a poisson random variable is observed for example when manufacturing equipment is properly aligned defects may be nearly impossible but when it is misaligned defects may occur according to a poisson distribution both the probability p of the perfect zero defect state and the mean number of defects in the imperfect state may depend on covariates sometimes p and are unrelated other times p is a simple function of such as p l 1 t for an unknown constant t in either case zip regression models are easy to fit the maximum likelihood estimates mle s are approximately normal in large samples and confidence intervals can be constructed by inverting likelihood ratio tests or using the approximate normality of the mle s simulations suggest that the confidence intervals based on likelihood ratio test

statistical analysis of repeated events forming renewal processes for each of several individuals a sequence of repeated events forming a renewal process is observed up to some censoring time the object is to estimate the average interevent time over the population of individuals as well as the variation of interevent times within and between individuals medical motivation comes from gastroenterology and concerns the occurrence of certain cyclic movements in the small bowel during the fasting state two statistical models are considered one is the standard variance component model adapted to censored data and the other is a recent intensity based model with a random proportionality factor representing interindividual variation these models are applied to the motility data and their advantages are discussed the intensity based model allows simple empirical bayes estimation of the expected interevent times for an individual in the presence of censoring

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

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

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

response surfaces for overdispersion in the study of the conditions for fish eggs hatching summary response surface methodology originally developed for determining optimal conditions in industrial experiments was early adapted to experiments in marine ecology however these involved studying the shape of the complete response surface not only detecting the optimum and often had counts or durations as the response variable thus nonlinear nonnormal response models were required for counts binomial and beta binomial models have been used the latter because of substantial over dispersion in closely controlled experiments over dispersion among units held under the same conditions might indicate that some mishap has occurred in conducting the study one possible check is to model the dispersion as a second response surface this procedure is used to show that over dispersion in fish egg hatching experiments has a biological explanation in that it occurs only under suboptimal hatching conditions

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

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

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

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

mixtures of multivariate power exponential distributions summary an expanded family of mixtures of multivariate power exponential distributions is introduced while fitting heavy tails and skewness have received much attention in the model based clustering literature recently we investigate the use of a distribution that can deal with both varying tail weight and peakedness of data a family of parsimonious models is proposed using an eigen decomposition of the scale matrix a generalized expectation maximization algorithm is presented that combines convex optimization via a minorization maximization approach and optimization based on accelerated line search algorithms on the stiefel manifold lastly the utility of this family of models is illustrated using both toy and benchmark data

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

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

a general family of distributions for longitudinal dependence with special reference to event histories event histories play an increasingly important role in medical studies examples include times between recurrences of tumours as with bladder cancer and between repeated infections as with chronic granulotomous disease a general method for generating new distributions is proposed by introducing an intensity function into a density this procedure yields as special cases several distributions already proposed in the literature the families of distributions based on the pareto distribution are of particular interest for event history analysis because of their relationship to the laplace transform of a gamma distribution they can yield multivariate distributions with longitudinal serial dependence by a procedure similar to updating in the kalman filter and with uniform dependence in a similar way to copulas for longitudinal dependence several such updating procedures are proposed

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

nonlinear modeling of serial immunologic data a case study abstract this article concerns the analysis of serial immunologic data from a clinical trial of immunosuppressive chemotherapy in the treatment of multiple sclerosis the goal of the analysis is to relate levels of drug dose to levels of an immunologic outcome variable i propose a new nonlinear model for the analysis of such data the model assumes that the mean function is the solution of an ordinary differential equation in time parameters of which are related to the dose via a regression function the defining differential equation is that which gives rise to the generalized logistic function a flexible form that includes a number of popular growth models we fit the model which accounts for random effects and time series autocorrelation by maximum likelihood results suggest strong treatment effects in two active drug groups and a small but significant effect in a placebo group these findings agree well with previously reported analyses of clinical outcomes from the trial an empirical comparis

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

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

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

double exponential families and their use in generalized linear regression abstract in one parameter exponential families such as the binomial and poisson the variance is a function of the mean double exponential families allow the introduction of a second parameter that controls variance independently of the mean double families are used as constituent distributions in generalized linear regressions in which both means and variances are allowed to depend on observed covariates the theory is applied to two examples a logistic regression and a large two way contingency table in such cases the binomial model of variance is often untrustworthy for example because genuine random sampling was infeasible the subjects may have been obtained in clumps so that the statistician should really be using smaller sample sizes clumped sampling is just one of many possible causes of overdispersion a habitual source of concern to users of binomial and poisson models this article concerns a class of regression families that allow the statistician to model overdispersion while carrying

on convexity of error rates in digital communications convexity properties of error rates of a class of decoders including the maximum likelihood min distance one as a special case are studied for arbitrary constellations bit mapping and coding earlier results obtained for the additive white gaussian noise channel are extended to a wide class of noise densities including unimodal and spherically invariant noise under these broad conditions symbol and bit error rates are shown to be convex functions of the signal to noise ratio snr in the high snr regime with an explicitly determined threshold which depends only on the constellation dimensionality and minimum distance thus enabling an application of the powerful tools of convex optimization to such digital communication systems in a rigorous way it is the decreasing nature of the noise power density around the decision region boundaries that ensures the convexity of symbol error rates in the general case the known high low snr bounds of the convexity concavity regions are tightened and no further improvement is shown to be possible in general the high snr bound fits closely into the channel coding theorem all codes including capacity achieving ones whose decision regions include the hardened noise spheres from the noise sphere hardening argument in the channel coding theorem satisfy this high snr requirement and thus has convex error rates in both snr and noise power we conjecture that all capacity achieving codes have convex error rates convexity properties in signal amplitude and noise power are also investigated some applications of the results are discussed in particular it is shown that fading is convexity preserving and is never good in low dimensions under spherically invariant noise which may also include any linear diversity combining

approximate inference in generalized linear mixed models statistical approaches to overdispersion correlated errors shrinkage estimation and smoothing of regression relationships may be encompassed within the framework of the generalized linear mixed model glmm given an unobserved vector of random effects observations are assumed to be conditionally independent with means that depend on the linear predictor through a specified link function and conditional variances that are specified by a variance function known prior weights and a scale factor the random effects are assumed to be normally distributed with mean zero and dispersion matrix depending on unknown variance components for problems involving time series spatial aggregation and smoothing the dispersion may be specified in terms of a rank deficient inverse covariance matrix approximation of the marginal quasi likelihood using laplace s method leads eventually to estimating equations based on penalized quasilikelihood or pql for the mean parameters and pseudo likelihood for the variances im

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

linear growth curve analysis based on exponential dispersion models the paper introduces a class of linear growth curve models based on combinations of exponential dispersion models the models have three components namely a latent markov growth process with stationary and independent increments a noise component and a component describing variation between subjects the models allow a wide range of continuous discrete and mixed distributions estimation for the models is based on an estimating equation derived from an em like algorithm combined with kalman smoothing

exponential dispersion models on etudie les proprietes generales de la classe des modeles de dispersion exponentiels qui est la generalisation multivariable des modeles de distribution d erreur de nelder et wedderburn 1972

generalized norton simon models of tumour growth this paper considers the analysis of serial data on the growth of tumours in laboratory rodents i propose a model a generalization of the tumour growth model of norton and simon that leads to a rich family of growth and decay curves the model assumes that unperturbed growth follows the generalized logistic form it accommodates time varying treatment effects through an effective dose function i fit two such models to data on a human prostate tumour growing in nude mice and compare the fitted curves and dose functions with a non parametric curve and dose function estimated from a cubic spline model all three models account for both random animal effects and autocorrelation monte carlo results suggest that a maximum likelihood estimates of growth parameters are biased although not severely and b standard errors are conservative in small samples but become increasingly accurate in larger samples

maximum likelihood discriminant analysis on the plane using a markovian model of spatial context abstract a method is presented for utilising spatial information in performing discriminant analysis on multivariate data at each point on a regular lattice as for example with landsat the spatial information is modelled as a particularly simple markov random field the discriminant analysis is based on the maximum likelihood method the results are compared with alternatives by monte carlo simulation and seem to be encouraging

structural waveguide behaviour of a beam plate system structures which have a constant cross section normal to a longitudinal axis can be considered as waveguides in which vibration can propagate in the form of various waves in the longitudinal direction the dynamic behaviour of such systems can be found by using a fourier transform approach in terms of wavenumbers in the longitudinal direction analytical solutions are available for simple infinitely long waveguides whereas for more complex waveguides numerical approaches have been developed using finite element techniques to describe the cross section in the present paper an analytical fourier transform approach is used to find the dynamic behaviour of a system consisting of two parallel beams coupled by a plate when a point force is applied to one of the beams multiple waves occur in the longitudinal direction the number of waves depending on the number of modes of the equivalent cross section however the motion of the driven beam is shown to be dominated by the contribution from only one or two waves at each frequency these having wavenumbers closest to that of the uncoupled beam the motion of the plate is also shown to be dominated by these wavenumbers for excitation on the beam experimental results are obtained on beam plate beam systems with identical and non identical beams which show good agreement with the predictions in particular these confirm that the plate response is dominated by waves with wavenumbers in the beam direction that follow those of the excited beam

on the covariance of two noncentral f random variables and the variance of the estimated liner discriminant function summary the covariance of two noncentral f random variables with a common covariance matrix is obtained from which the variance of their difference follows easily an application of this latter result in discriminant analysis is then given

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

families of multivariate distributions abstract for many years there has been an interest in families of bivariate distributions with marginals as parameters genest and mackay 1986a b showed that several such families that appear in the literature can be derived by a unified method a similar conclusion is obtained in this article through the use of mixture models these models might be regarded as multivariate proportional hazards models with random constants of proportionality the mixture models are useful for two purposes first they make some properties of the derived distributions more transparent the positive dependency property of association is sometimes exposed and a method for simulation of data from the distributions is suggested but the mixture models also allow derivation of several new families of bivariate distributions with marginals as parameters and they indicate obvious multivariate extensions some of the new families of bivariate distributions given in this article extend known distributions by adding a parameter

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

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

analysis of field experiments by least squares smoothing summary assuming a smooth trend plus independent error model for the environmental effects in the yields of a field plot experiment least squares smoothing methods are developed to estimate both the treatment effects and the unknown trend treatment estimates are closely related to those resulting from a generalized least squares analysis in which the covariance structure for the environmental effects has a particular form however the main emphases are on the accuracy of treatment estimates under a fixed smooth trend plus error model and the exploratory power of the basic method to isolate trend effects of unknown form although the detailed development is for the one dimensional case generalizations of the smoothness concept and extensions to two dimensions are also discussed application of the basic method is illustrated on three data sets and the results compared with other analyses

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

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

a mixture model with detection limits for regression analyses of antibody response to vaccine antibody concentration values as determined by quantitative assays often are left censored due to detection limits or limits established for purpose of specificity standard analyses which assume the data arise from a single lognormal response distribution may not be appropriate when more observations are censored than would be expected under such a model interference from maternal antibodies due to vaccination at an early age for example could result in a high proportion of nonresponders to vaccine a mixture model consisting of a censored lognormal distribution and a point distribution located below the detection limit is proposed for such situations antibody data from a study of measles vaccine are used to illustrate the utility of this approach and the interpretation of the model parameters

a distributional model for repeated failure time measurements summary a standard model for repeated measurements is based on the multivariate normal distribution with equicorrelated variables a corresponding analysis is developed for weibull failure times the resulting distribution has some attractive properties for practical application to multivariate failure times

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

a review of some extensions to generalized linear models although generalized linear models are reasonably well known they are not as widely used in medical statistics as might be appropriate with the exception of logistic log linear and some survival models at the same time the generalized linear modelling methodology is decidedly outdated in that more powerful methods involving wider classes of distributions non linear regression censoring and dependence among responses are required limitations of the generalized linear modelling approach include the need for the iterated weighted least squares iwls procedure for estimation and deviances for inferences these restrict the class of models that can be used and do not allow direct comparisons among models from different distributions powerful non linear optimization routines are now available and comparisons can more fruitfully be made using the complete likelihood function the link function is an artefact necessary for iwls to function with linear models but that disappears once the class is extended to truly non linear models restricting comparisons of responses under different treatments to differences in means can be extremely misleading if the shape of the distribution is changing this may involve changes in dispersion or of other shape related parameters such as the skewness in a stable distribution with the treatments or covariates any exact likelihood function defined as the probability of the observed data takes into account the fact that all observable data are interval censored thus directly encompassing the various types of censoring possible with duration type data in most situations this can now be as easily used as the traditional approximate likelihood based on densities finally methods are required for incorporating dependencies among responses in models including conditioning on previous history and on random effects one important procedure for constructing such likelihoods is based on kalman filtering

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

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

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

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

mixed poisson regression models with covariate dependent rates this paper studies a class of poisson mixture models that includes covariates in rates this model contains poisson regression and independent poisson mixtures as special cases estimation methods based on the em and quasi newton algorithms properties of these estimates a model selection procedure residual analysis and goodness of fit test are discussed a monte carlo study investigates implementation and model choice issues this methodology is used to analyze seizure frequency and ames salmonella assay data

generalized nonlinear models for pharmacokinetic data summary phase i trials to study the pharmacokinetic properties of a new drug generally involve a restricted number of healthy volunteers because of the nature of the group involved in such studies the appropriate distributional assumptions are not always obvious these model assumptions include the actual distribution but also the ways in which the dispersion of responses is allowed to vary over time and the fact that small concentrations of a substance are not easily detectable and hence are left censored we propose that a reasonably wide class of generalized nonlinear models allowing for left censoring be considered now that this is feasible with current computer power and sophisticated statistical packages these modelling strategies are applied to a phase i study of the drug flosequinan and its metabolite this drug was developed for the treatment of heart failure because the metabolite also exhibits an active pharmacologic effect study of both the parent drug and the metabolite is of interest

a hybrid method for the vibration analysis of complex structural acoustic systems a new method is presented for the analysis of complex dynamic systems which is based on partitioning the system degrees of freedom into a global set and a local set the global equations of motion are formulated and solved in a standard deterministic manner although due account is taken of the presence of the local degrees of freedom via an approach which is analogous to fuzzy structure theory the local equations of motion are formulated and solved by using statistical energy analysis sea with due account being taken of power input from the global degrees of freedom the method can be considered to encompass a number of existing analysis techniques and to form a flexible framework within which a number of detailed modeling strategies can be devised a simple rod system provides an initial application and it is shown that the method yields very good results from low to high frequency excitation

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

a non gaussian state space model and application to prediction of records on developpe une classe de modeles qui generalisent le travail de smith 1979 et bathes 1975 pour des donnees censurees

a statistical model for positron emission tomography abstract positron emission tomography pet still in its research stages is a technique that promises to open new medical frontiers by enabling physicians to study the metabolic activity of the body in a pictorial manner much as in x ray transmission tomography and other modes of computerized tomography the quality of the reconstructed image in pet is very sensitive to the mathematical algorithm to be used for reconstruction in this article we tailor a mathematical model to the physics of positron emissions and we use the model to describe the basic image reconstruction problem of pet as a standard problem in statistical estimation from incomplete data we describe various estimation procedures such as the maximum likelihood ml method using the em algorithm the method of moments and the least squares method a computer simulation of a pet experiment is then used to demonstrate the ml and the least squares reconstructions the main purposes of this article are to report on what we believe is an

graphical and recursive models for contingency tables summary we discuss two classes of models for contingency tables graphical and recursive models both of which arise from restrictions that are expressible as conditional independencies of variable pairs the first of these is a subclass of hierarchical log linear models each of its models can be represented by an undirected graph in the second class each model corresponds to a particular kind of a directed graph instead and can be characterized by a nontrivial factorization of the joint distribution in terms of response variables we derive decomposable or multiplicative models as the intersecting class this result has useful consequences for exploratory types of analysis as well as for the model interpretation we can give an aid for detecting well fitting decomposable models in a transformation of the observed contingency table and each decomposable model may be interpreted with the help of an undirected or directed graph

bayesian inference for stable distributions abstract very little work on stable distribution parameter estimation and inference appears in the literature due to the nonexistence of the probability density function this has led in particular to a dearth of bayesian work in this area but bayesian computation via markov chain monte carlo allows us to sample from the distribution of the parameters of the stable distributions by exploiting a particular mathematical representation involving the stable density

a dynamic frailty model for multivariate survival data summary we consider the statistical modeling of data consisting of many study subjects with serially correlated multivariate survival responses the ordinary frailty model handles the serial correlation in such data by introducing an unobserved multiplicative random effect term called the frailty in the hazard function the frailties are often assumed to be identical for the survival times from the same unit we have generalized the frailty model by allowing the frailties to vary stochastically with the indices we have proposed a simple scheme to update the dynamic frailties this approach assumes that the random effects are gamma distributed at each occurrence the two gamma parameters are updated according to the past information in terms of their marginal distributions the dynamic frailties form a multiplicative random walk this approach results in a tractable likelihood the small sample behavior of the mle is studied via a simulation experiment the model is then illustrated with a data set from an animal carcinogenesis experiment

a semiparametric model for regression analysis of interval censored failure time data left right and interval censored response time data arise in a variety of settings including the analyses of data from laboratory animal carcinogenicity experiments clinical trials and longitudinal studies for such incomplete data the usual regression techniques such as the cox 1972 journal of the royal statistical society series b 34 187 220 proportional hazards model are inapplicable in this paper we present a method for regression analysis which accommodates interval censored data we present applications of this methodology to data sets from a study of breast cancer patients who were followed for cosmetic response to therapy a small animal tumorigenicity study and a clinical trial

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

a hybrid mode fourier transform approach for estimating the vibrations of beam stiffened plate systems in this paper a hybrid mode fourier transform approach is described for estimating the vibration response of a structure such as a beam stiffened plate with excitation applied to the beam the beam is defined deterministically in terms of its modes whereas the plate is treated approximately by assuming it extends to infinity equilibrium and continuity conditions are approximated along the interface between the beam and the plate in the wavenumber domain by a fourier transform method consequently both the dynamic response of the beam and the power transmitted to the plate can be simply estimated meanwhile the dynamic interactions of the coupled system can be determined these depend on the correlations between the modal properties of the beam and the wave motions of the plate expressions are given for the effective mass density and effective loss factor the plate applies to each mode of the beam when a locally reacting plate approximation is incorporated into the mode fourier transform procedure a simpler locally reacting impedance method can be developed the results are discussed and compared to those of fuzzy structure theory numerical examples are presented