non markovian quantum dynamics correlated projection superoperators and hilbert space averaging the time convolutionless tcl projection operator technique allows a systematic analysis of the non markovian quantum dynamics of open systems we present a class of projection superoperators that project the states of the total system onto certain correlated system environment states it is shown that the application of the tcl technique to this class of correlated superoperators enables the nonperturbative treatment of the dynamics of system environment models for which the standard approach fails in any finite order of the coupling strength we demonstrate further that the correlated superoperators correspond to the idea of a best guess of conditional quantum expectations which is determined by a suitable hilbert space average the general approach is illustrated by means of the model of a spin that interacts through randomly distributed couplings with a finite reservoir consisting of two energy bands extensive numerical simulations of the full schrodinger equation of the model reveal the power and efficiency of the method

holomorphic isometries of mathbb b m into bounded symmetric domains arising from linear sections of minimal embeddings of their compact duals we study general properties of images of holomorphic isometric embeddings of complex unit balls mathbb b m into irreducible bounded symmetric domains varomega of rank at least 2 in particular we show that such holomorphic isometries with the minimal normalizing constant arise from linear sections varlambda of the compact dual x c of varomega the question naturally arises as to which linear sections z varlambda cap varomega are actually images of holomorphic isometries of complex unit balls we study the latter question in the case of bounded symmetric domains varomega of type iv alias lie balls i e bounded symmetric domains dual to hyperquadrics we completely classify images of all holomorphic isometric embeddings of complex unit balls into such bounded symmetric domains varomega especially we show that there exist holomorphic isometric embeddings of complex unit balls of codimension 1 incongruent to the examples constructed by mok proc am math soc 144 4515 4525 2016 from varieties of minimal rational tangents and that moreover any holomorphic isometric embedding f mathbb b m rightarrow varomega extends to a holomorphic isometric embedding f mathbb b n 1 rightarrow varomega dim varomega n the case of lie balls is particularly relevant because holomorphic isometric embeddings of complex unit balls of sufficiently large dimensions into an irreducible bounded symmetric domain other than a type iv domain are expected to be more rigid

correlation effects in a discrete quantum random walk we introduce memory dependent discrete time quantum random walk models by adding uncorrelated memory terms and also by modifying the hamiltonian of the walker to include couplings with memory keeping agents we next study numerically the correlation effects in these models we also propose a correlation exponent as a relevant and promising tool for investigation of correlation or memory hence non markovian effects our analysis can easily be applied to more realistic models in which different regimes may emerge because of competition between different underlying physical mechanisms

a coupling regeneration scheme for diagnosing convergence in markov chain monte carlo algorithms abstract here i propose a convergence diagnostic for markov chain monte carlo mcmc algorithms based on couplings of a markov chain with an auxiliary chain that is periodically restarted from a fixed parameter value the diagnostic provides a mechanism for estimating the specific constants governing the rate of convergence of geometrically and uniformly ergodic chains and provides a lower bound on the effective sample size of a mcmc run it also provides a simple procedure for obtaining what is with high probability an independent sample from the stationary distribution

triplet markov fields for the classification of complex structure data we address the issue of classifying complex data we focus on three main sources of complexity namely the high dimensionality of the observed data the dependencies between these observations and the general nature of the noise model underlying their distribution we investigate the recent triplet markov fields and propose new models in this class designed for such data and in particular allowing very general noise models in addition our models can handle the inclusion of a learning step in a consistent way so that they can be used in a supervised framework one advantage of our models is that whatever the initial complexity of the noise model parameter estimation can be carried out using state of the art bayesian clustering techniques under the usual simplifying assumptions as generative models they can be seen as an alternative in the supervised case to discriminative conditional random fields identifiability issues underlying the models in the nonsupervised case are discussed while the models performance is illustrated on simulated and real data exhibiting the mentioned various sources of complexity

operator correlations and quantum regression theorem in non markovian lindblad rate equations non markovian lindblad rate equations arise from alternative microscopic interactions such as quantum systems coupled to composite reservoirs where extra degrees of freedom mediate the interaction between the system and a markovian reservoir as well as from systems coupled to complex structured reservoirs whose action can be well approximated by a direct sum of markovian sub reservoirs budini in phys rev a 74 053815 2006 the purpose of this paper is two fold first for both kinds of interactions we find general expressions for the system operator correlations written in terms of the lindblad rate propagator secondly we find the conditions under which a quantum regression hypothesis is valid we show that a non markovian quantum regression theorem can only be granted in a stationary regime being a necessary condition the fulfillment of a detailed balance condition this result is independent of the underlying microscopic interaction providing a criterion for the validity of the regression hypothesis in non markovian lindblad like master equations as an example we study the correlations of a two level system coupled to different kind of reservoirs

outperforming the gibbs sampler empirical estimator for nearest neighbor random fields given a markov chain sampling scheme does the standard empirical estimator make best use of the data we show that this is not so and construct better estimators we restrict attention to nearest neighbor random fields and to gibbs samplers with deterministic sweep but our approach applies to any sampler that uses reversible variable at a time updating with deterministic sweep the structure of the transition distribution of the sampler is exploited to construct further empirical estimators that are combined with the standard empirical estimator to reduce asymptotic variance the extra computational cost is negligible when the random field is spatially homogeneous symmetrizations of our estimator lead to further variance reduction the performance of the estimators is evaluated in a simulation study of the ising model

positivity preserving non markovian master equations a general class of integrodifferential non markovian master equations is developed which is representative of the dynamics of small subsystems interacting with open reservoirs with memory conditions which guarantee positivity of the subsystem reduced density are established

a continuous counting observation and posterior quantum dynamics a stochastic model for a continuous photon counting measurement is proposed an equation for the generating functional of the statistics of counts is found a stochastic linear equation for the unnormalised posterior state vector of atoms continuously collapsing without mixing is derived by using quantum stochastic calculus methods

optimization of integer wavelet transforms based on difference correlation structures in this paper a novel lifting integer wavelet transform based on difference correlation structure dccs liwt is proposed first we establish a relationship between the performance of a linear predictor and the difference correlations of an image the obtained results provide a theoretical foundation for the following construction of the optimal lifting filters then the optimal prediction lifting coefficients in the sense of least square prediction error are derived dccs liwt puts heavy emphasis on image inherent dependence a distinct feature of this method is the use of the variance normalized autocorrelation function of the difference image to construct a linear predictor and adapt the predictor to varying image sources the proposed scheme also allows respective calculations of the lifting filters for the horizontal and vertical orientations experimental evaluation shows that the proposed method produces better results than the other well known integer transforms for the lossless image compression

jump diffusion unravelling of a non markovian generalized lindblad master equation the correlated projection technique has been successfully applied to derive a large class of highly non markovian dynamics the so called non markovian generalized lindblad type equations or lindblad rate equations in this article general unravelings are presented for these equations described in terms of jump diffusion stochastic differential equations for wave functions we show also that the proposed unraveling can be interpreted in terms of measurements continuous in time but with some conceptual restrictions the main point in the measurement interpretation is that the structure itself of the underlying mathematical theory poses restrictions on what can be considered as observable and what is not such restrictions can be seen as the effect of some kind of superselection rule finally we develop a concrete example and discuss possible effects on the heterodyne spectrum of a two level system due to a structured thermal like bath with memory

edge detection and linear feature extraction using a 2 d random field model the edge detection problem is posed as one of detecting step discontinuities in the observed correlated image using directional derivatives estimated with a random field model specifically the method consists of representing the pixels in a local window by a 2 d causal autoregressive ar model whose parameters are adaptively estimated using a recursive least squares algorithm the directional derivatives are functions of parameter estimates an edge is detected if the second derivative in the direction of the estimated maximum gradient is negatively sloped and the first directional derivative and a local estimate of variance satisfy some conditions because the ordered edge detector may not detect edges of all orientations well the image scanned in four different directions and the union of the four edge images is taken as the final output the performance of the edge detector is illustrated using synthetic and real images comparisons to other edge detectors are given a linear feature extractor that operates on the edges produced by the ar model is presented

delineating buildings by grouping lines with mrfs traditionally markov random field mrf models have been used in low level image analysis the article presents an mrf based scheme to perform object delineation the proposed edge based approach involves extracting straight lines from the edge map of an image then an mrf model is used to group these lines to delineate buildings in aerial images

discriminative random fields in this research we address the problem of classification and labeling of regions given a single static natural image natural images exhibit strong spatial dependencies and modeling these dependencies in a principled manner is crucial to achieve good classification accuracy in this work we present discriminative random fields drfs to model spatial interactions in images in a discriminative framework based on the concept of conditional random fields proposed by lafferty et al 2001 the drfs classify image regions by incorporating neighborhood spatial interactions in the labels as well as the observed data the drf framework offers several advantages over the conventional markov random field mrf framework first the drfs allow to relax the strong assumption of conditional independence of the observed data generally used in the mrf framework for tractability this assumption is too restrictive for a large number of applications in computer vision second the drfs derive their classification power by exploiting the probabilistic discriminative models instead of the generative models used for modeling observations in the mrf framework third the interaction in labels in drfs is based on the idea of pairwise discrimination of the observed data making it data adaptive instead of being fixed a priori as in mrfs finally all the parameters in the drf model are estimated simultaneously from the training data unlike the mrf framework where the likelihood parameters are usually learned separately from the field parameters we present preliminary experiments with man made structure detection and binary image restoration tasks and compare the drf results with the mrf results

genetic algorithms for ambiguous labelling problems abstract consistent labelling problems frequently have more than one solution most work in the field has aimed at disambiguating early in the interpretation process using only local evidence this paper starts with a review of the literature on labelling problems and ambiguity based on this review we propose a strategy for simultaneously extracting multiple related solutions to the consistent labelling problem in a preliminary experimental study we show that an appropriately modified genetic algorithm is a robust tool for finding multiple solutions to the consistent labelling problem these solutions are related by common labellings of the most strongly constrained junctions we have proposed three run time measures of algorithm performance the maximum fitness of the genetic algorithm s population its shannon entropy and the total hamming distance between its distinct members the results to date indicate that when the shannon entropy falls below a certain threshold new solutions are unlikely to emerge and that most of the diversity in the population disappears within the first few generations

markov random field texture models we consider a texture to be a stochastic possibly periodic two dimensional image field a texture model is a mathematical procedure capable of producing and describing a textured image we explore the use of markov random fields as texture models the binomial model where each point in the texture has a binomial distribution with parameter controlled by its neighbors and number of tries equal to the number of gray levels was taken to be the basic model for the analysis a method of generating samples from the binomial model is given followed by a theoretical and practical analysis of the method s convergence examples show how the parameters of the markov random field control the strength and direction of the clustering in the image the power of the binomial model to produce blurry sharp line like and blob like textures is demonstrated natural texture samples were digitized and their parameters were estimated under the markov random field model a hypothesis test was used for an objective assessment of goodness of fit under the markov random field model overall microtextures fit the model well the estimated parameters of the natural textures were used as input to the generation procedure the synthetic microtextures closely resembled their real counterparts while the regular and inhomogeneous textures did not

the measure on the original space from a product measure abstract the measurability problems can arise in gaussian and empirical processes underlying nonparametric and semiparametric problems critical in modern statistics and machine learning the usual applications are high dimensional data in random fields to ensure the validity of recovering a true original complex object from its observed images treated in a product space that appears in existing high dimensional data applications widely arising in physical biological and social sciences and engineering we begin with the results on the algebra of the original space induced by a given map f t mapping from to a product measure space t f t t we study the certain measurability issues on with the induced measure from the image measure t of the product space by establishing the outer measure restricted to the trace algebra we further investigate the measure on under the map f t to obtain the conditions for the identifiability equality t f t 1 to hold so that a practical and rigorous foundation is provided for the nontrivial applications to work meaningfully

compound gauss markov random fields for image estimation algorithms for obtaining approximations to statistically optimal estimates for images modeled as compound gauss markov random fields are discussed the authors consider both the maximum a posteriori probability map estimate and the minimum mean squared error mmse estimate for image estimation and image restoration compound image models consist of several submodels having different characteristics along with an underlying structure model which govern transitions between these image submodels two different compound random field models are employed the doubly stochastic gaussian dsg random field and a compound gauss markov cgm random field the authors present map estimators for dsg and cgm random fields using simulated annealing a fast converging algorithm called deterministic relaxation which however converges to only a locally optimal map estimate is also presented as an alternative for reducing computational loading on sequential machines for comparison purposes the authors include results on the fixed lag smoothing mmse estimator for the dsg field and its suboptimal m algorithm approximation

parameter estimation and tissue segmentation from multispectral mr images a statistical method is developed to classify tissue types and to segment the corresponding tissue regions from relaxation time t sub 1 t sub 2 and proton density p sub d weighted magnetic resonance images the method assumes that the distribution of image intensities associated with each tissue type can be expressed as a multivariate likelihood function of three weighted signal intensity values t sub 1 t sub 2 p sub d at each location within that tissue regions the method further assumes that the underlying tissue regions are piecewise contiguous and can be characterized by a markov random field prior in classifying the tissue types the method models the likelihood of realizing the images as a finite multivariate mixture function the class parameters associated with the tissue types i e the weighted intensity means variances and correlation coefficients of the multivariate function as well as the number of voxels within regions of the tissue types of are estimated by maximum likelihood the estimation fits the class parameters to the image data via the expectation maximization algorithm the number of classes associated with the tissue types is determined by the information criterion of minimum description length the method segments the tissue regions given the estimated class parameters by maximum a posteriori probability the prior is constructed by the tissue region membership of the first and second order neighborhood the method is tested by a few sets of t sub 1 t sub 2 and p sub d weighted images of the brain acquired with a 1 5 tesla whole body scanner the number of classes and the associated class parameters are automatically estimated the regions of different brain tissues are satisfactorily segmented

3 d contextual bayesian classifiers we extend a series of multivariate bayesian two dimensional 2 d contextual classifiers to three dimensional 3 d by specifying a simultaneous gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its six nearest 3 d neighbors

bayesian lasso for semiparametric structural equation models there has been great interest in developing nonlinear structural equation models and associated statistical inference procedures including estimation and model selection methods in this paper a general semiparametric structural equation model ssem is developed in which the structural equation is composed of nonparametric functions of exogenous latent variables and fixed covariates on a set of latent endogenous variables a basis representation is used to approximate these nonparametric functions in the structural equation and the bayesian lasso method coupled with a markov chain monte carlo mcmc algorithm is used for simultaneous estimation and model selection the proposed method is illustrated using a simulation study and data from the affective dynamics and individual differences adid study results demonstrate that our method can accurately estimate the unknown parameters and correctly identify the true underlying model

bayesian modeling of measurement error in predictor variables using item response theory it is shown that measurement error in predictor variables can be modeled using item response theory irt the predictor variables that may be defined at any level of an hierarchical regression model are treated as latent variables the normal ogive model is used to describe the relation between the latent variables and dichotomous observed variables which may be responses to tests or questionnaires it will be shown that the multilevel model with measurement error in the observed predictor variables can be estimated in a bayesian framework using gibbs sampling in this article handling measurement error via the normal ogive model is compared with alternative approaches using the classical true score model examples using real data are given

a tree structured markov random field model for bayesian image segmentation we present a new image segmentation algorithm based on a tree structured binary mrf model the image is recursively segmented in smaller and smaller regions until a stopping condition local to each region is met each elementary binary segmentation is obtained as the solution of a map estimation problem with the region prior modeled as an mrf since only binary fields are used and thanks to the tree structure the algorithm is quite fast and allows one to address the cluster validation problem in a seamless way in addition all field parameters are estimated locally allowing for some spatial adaptivity to improve segmentation accuracy a split and merge procedure is also developed and a spatially adaptive mrf model is used numerical experiments on multispectral images show that the proposed algorithm is much faster than a similar reference algorithm based on flat mrf models and its performance in terms of segmentation accuracy and map smoothness is comparable or even superior

gradient tree boosting for training conditional random fields conditional random fields crfs provide a flexible and powerful model for sequence labeling problems however existing learning algorithms are slow particularly in problems with large numbers of potential input features and feature combinations this paper describes a new algorithm for training crfs via gradient tree boosting in tree boosting the crf potential functions are represented as weighted sums of regression trees which provide compact representations of feature interactions so the algorithm does not explicitly consider the potentially large parameter space as a result gradient tree boosting scales linearly in the order of the markov model and in the order of the feature interactions rather than exponentially as in previous algorithms based on iterative scaling and gradient descent gradient tree boosting also makes it possible to use instance weighting as in c4 5 and surrogate splitting as in cart to handle missing values experimental studies of the effectiveness of these two methods as well as standard imputation and indicator feature methods show that instance weighting is the best method in most cases when feature values are missing at random

there is no quantum regression theorem the onsager regression hypothesis states that the regression of fluctuations is governed by macroscopic equations describing the approach to equilibrium it is here asserted that this hypothesis fails in the quantum case this is shown first by explicit calculation for the example of quantum brownian motion of an oscillator and then in general from the fluctuation dissipation theorem it is asserted that the correct generalization of the onsager hypothesis is the fluctuation dissipation theorem s0031 9007 96 00679 5

the local structure of a liouville vector field 1 introduction a liouville vector field on a symplectic manifold is a smooth vector field whose flow consists of conformally flat transformations nagano 7 characterized the cotangent bundle of the singular set of a 1 form whose exterior derivative is symplectic by means of a liouville field in this work we investigate the local structure of a liouville vector field of a 2n dimensional symplectic manifold p q which vanishes on a k dimensional isotropic submanifold q of p some of the eigenvalues of its linear part are assumed to be in resonance a vector field with this properties appears in a natural geometric problem it arises as the gradient field with respect to some kaihler metric of the square of the distance function to an isotropic submanifold in nagano s theorem the singular set is a lagrangian submanifold and the eigenvalues of the linear part of the liouville field at the singular points are 0 and 1 here we assume that at every point q of q the vector field vanishes and the eigenvalues of its 1 jet are 1 1 2 and 0 this means precisely that in some coordinates the 1 jet of at q is

the quantum state diffusion model applied to open systems a model of a quantum system interacting with its environment is proposed in which the system is represented by a state vector that satisfies a stochastic differential equation derived from a density operator equation such as the bloch equation and consistent with it the advantages of the numerical solution of these equations over the direct numerical solution of the density operator equations are described the method is applied to the nonlinear absorber cascades of quantum transitions second harmonic generation and a measurement reduction process the model provides graphic illustrations of these processes with statistical fluctuations that mimic those of experiments the stochastic differential equations originated from studies of the measurement problem in the foundations of quantum mechanics the model is compared with the quantum jump model of dalibard 1992 carmichael and others which originated among experimenters looking for intuitive pictures and rules of computation

a wavelet or lifting scheme based imputation method summary the paper proposes a new approach to imputation using the expected sparse representation of a surface in a wavelet or lifting scheme basis our method incorporates a bayesian mixture prior for these wavelet coefficients into a gibbs sampler to generate a complete posterior distribution for the variable of interest intuitively the estimator operates by borrowing strength from those observed neighbouring values to impute at the unobserved sites we demonstrate the strong performance of our estimator in both one and two dimensional imputation problems where we also compare its application with the standard imputation techniques of kriging and thin plate splines

application of piece wise regression to detecting internal structure of signal abstract the problem of restoring the underlying structure of a signal with the help of piece wise regression is considered the case of interest is that the domain of definition of a response function consists of a number of regions of smoothness the number of regions and the location of change points are not fixed in advance and they should be found by analyzing the signal corrupted with noise for a given number of smooth regions the best piece wise regression may be found with the help of an approach based on dynamic programming the selection of the best model the best number of regions of smoothness is performed with the help of a probabilistic estimate some properties of the estimate and the whole procedure are studied and the results of experiments are presented

metric rigidity theorems on hermitian locally symmetric spaces abstract let x be a compact quotient of an irreducible bounded symmetric domain of rank 2 by a discrete group of automorphisms without fixed points it is well known that the kahler einstein metric g on x carries seminegative curvature in the sense of griffiths i show that any hermitian metric h on x carrying seminegative curvature must be a constant multiple of g this can be applied to prove rigidity theorems of holomorphic maps from x into hermitian manifolds y k carrying seminegative curvature these results are also generalized to the case of quotients of finite volume on the other hand let xc gc be an irreducible compact hermitian symmetric manifold of rank 2 then gc is kahler and carries semipositive holomorphic bisectional curvature i prove that any kahler h on xc carrying semipositive holomorphic bisectional curvature must be equal to gc up to a constant multiple and up to a biholomorphic transformation of xc

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

continuous time open quantum random walks and non markovian lindblad master equations a new type of quantum random walks called open quantum random walks has been developed and studied in attal et al open quantum random walks preprint and central limit theorems for open quantum random walks preprint in this article we present a natural continuous time extension of these open quantum random walks this continuous time version is obtained by taking a continuous time limit of the discrete time open quantum random walks this approximation procedure is based on some adaptation of repeated quantum interactions theory attal and pautrat in annales henri poincare physique theorique 7 59 104 2006 coupled with the use of correlated projectors breuer in phys rev a 75 022103 2007 the limit evolutions obtained this way give rise to a particular type of quantum master equations these equations appeared originally in the non markovian generalization of the lindblad theory breuer in phys rev a 75 022103 2007 we also investigate the continuous time limits of the quantum trajectories associated with open quantum random walks we show that the limit evolutions in this context are described by jump stochastic differential equations finally we present a physical example which can be described in terms of open quantum random walks and their associated continuous time limits

on the foundations of relaxation labeling processes a large class of problems can be formulated in terms of the assignment of labels to objects frequently processes are needed which reduce ambiguity and noise and select the best label among several possible choices relaxation labeling processes are just such a class of algorithms they are based on the parallel use of local constraints between labels this paper develops a theory to characterize the goal of relaxation labeling the theory is founded on a definition of con sistency in labelings extending the notion of constraint satisfaction in certain restricted circumstances an explicit functional exists that can be maximized to guide the search for consistent labelings this functional is used to derive a new relaxation labeling operator when the restrictions are not satisfied the theory relies on variational cal culus it is shown that the problem of finding consistent labelings is equivalent to solving a variational inequality a procedure nearly identical to the relaxation operator derived under restricted circum stances serves in the more general setting further a local convergence result is established for this operator the standard relaxation labeling formulas are shown to approximate our new operator which leads us to conjecture that successful applications of the standard methods are explainable by the theory developed here observations about con vergence and generalizations to higher order compatibility relations are described

maximum likelihood estimation of nonlinear structural equation models the existing maximum likelihood theory and its computer software in structural equation modeling are established based on linear relationships among manifest variables and latent variables however models with nonlinear relationships are often encountered in social and behavioral sciences in this article an em type algorithm is developed for maximum likelihood estimation of a general nonlinear structural equation model to avoid computation of the complicated multiple integrals involved the e step is completed by a metropolis hastings algorithm it is shown that the m step can be completed efficiently by simple conditional maximization standard errors of the maximum likelihood estimates are obtained via louis s formula the methodology is illustrated with results from a simulation study and two real examples

on the structure of the master equation for a two level system coupled to a thermal bath we derive a master equation from the exact stochastic liouville von neumann sln equation stockburger and grabert 2002 phys rev lett 88 170407 the latter depends on two correlated noises and describes exactly the dynamics of an oscillator which can be either harmonic or present an anharmonicity coupled to an environment at thermal equilibrium the newly derived master equation is obtained by performing analytically the average over different noise trajectories it is found to have a complex hierarchical structure that might be helpful to explain the convergence problems occurring when performing numerically the stochastic average of trajectories given by the sln equation koch et al 2008 phys rev lett 100 230402 koch 2010 phd thesis fakultat mathematik und naturwissenschaften der technischen universitat dresden

iterative reweighted total generalized variation based poisson noise removal model total generalized variation tgv regularization model is one of the most effective methods for denoising and eliminating staircase effect however the tgv regularization model tends to blur edges as the existence of high order derivative in order to avoid the staircase effect while alleviating the edge blurring an iterative reweighted tgv based poisson noise removal model is presented under the assumption that each pixel of noisy image follows a poisson distribution the weight function incorporated in the tgv regularization term is derived from the expectation maximization em algorithm we design a new iterative weighted primal dual algorithm which is an improvement of the classic iterative reweighted algorithm numerical experimental results show the better performance of our model in removing noise effectively while preserving edges and eliminating staircase effect

3d face discriminant analysis using gauss markov posterior marginals we present a markov random field model for the analysis of lattices e g images or 3d meshes in terms of the discriminative information of their vertices the proposed method provides a measure field that estimates the probability of each vertex being discriminative or nondiscriminative for a given classification task to illustrate the applicability and generality of our framework we use the estimated probabilities as feature scoring to define compact signatures for three different classification tasks 1 3d face recognition 2 3d facial expression recognition and 3 ethnicity based subject retrieval obtaining very competitive results the main contribution of this work lies in the development of a novel framework for feature selection in scenaria in which the most discriminative information is smoothly distributed along a lattice

a markov random field model based approach to image interpretation an image is segmented into a collection of disjoint regions that form the nodes of an adjacency graph and image interpretation is achieved through assigning object labels or interpretations to the segmented regions or nodes using domain knowledge extracted feature measurements and spatial relationships between the various regions the interpretation labels are modeled as a markov random field mrf on the corresponding adjacency graph and the image interpretation problem is then formulated as a maximum a posteriori map estimation rule given domain knowledge and region based measurements simulated annealing is used to find this best realization or optimal map interpretation this approach also provides a systematic method for organizing and representing domain knowledge through appropriate design of the clique functions describing the gibbs distribution representing the pdf of the underlying mrf a general methodology is provided for the design of the clique functions results of image interpretation experiments on synthetic and real world images are described

phase unwrapping of mr phase images using poisson equation the authors have developed a technique based on a solution of the poisson equation to unwrap the phase in magnetic resonance mr phase images the method is based on the assumption that the magnitude of the inter pixel phase change is less than spl pi per pixel therefore the authors obtain an estimate of the phase gradient by wrapping the gradient of the original phase image the problem is then to obtain the absolute phase given the estimate of the phase gradient the least squares ls solution to this problem is shown to be a solution of the poisson equation allowing the use of fast poisson solvers the absolute phase is then obtained by mapping the ls phase to the nearest multiple of 2 k from the measured phase the proposed technique is evaluated using mr phase images and is proven to be robust in the presence of noise an application of the proposed method to the 3 point dixon technique for water and fat separation is demonstrated

algorithmic stability and sanity check bounds for leave one out cross validation in this article we prove sanity check bounds for the error of the leave one out cross validation estimate of the generalization error that is bounds showing that the worst case error of this estimate is not much worse than that of the training error estimate the name sanity check refers to the fact that although we often expect the leave one out estimate to perform considerably better than the training error estimate we are here only seeking assurance that its performance will not be considerably worse perhaps surprisingly such assurance has been given only for limited cases in the prior literature on cross validation any nontrivial bound on the error of leave one out must rely on some notion of algorithmic stability previous bounds relied on the rather strong notion of hypothesis stability whose application was primarily limited to nearest neighbor and other local algorithms here we introduce the new and weaker notion of error stability and apply it to obtain sanity check bounds for leave one out for other classes of learning algorithms including training error minimization procedures and bayesian algorithms we also provide lower bounds demonstrating the necessity of some form of error stability for proving bounds on the error of the leave one out estimate and the fact that for training error minimization algorithms in the worst case such bounds must still depend on the vapnik chervonenkis dimension of the hypothesis class

rigid body segmentation and shape description from dense optical flow under weak perspective we present an algorithm for identifying and tracking independently moving rigid objects from optical flow some previous attempts at segmentation via optical flow have focused on finding discontinuities in the flow field while discontinuities do indicate a change in scene depth they do not in general signal a boundary between two separate objects the proposed method uses the fact that each independently moving object has a unique epipolar constraint associated with its motion thus motion discontinuities based on self occlusion can be distinguished from those due to separate objects the use of epipolar geometry allows for the determination of individual motion parameters for each object as well as the recovery of relative depth for each point on the object the algorithm assumes an affine camera where perspective effects are limited to changes in overall scale no camera calibration parameters are required a kalman filter based approach is used for tracking motion parameters with time

relation between optimal nonlinearity and non gaussian noise enhancing a weak signal in a nonlinear system in the study of stochastic resonance it is often mentioned that nonlinearity can enhance a weak signal embedded in noise in order to give a systematic proof of the signal enhancement in nonlinear systems we derive an optimal nonlinearity that maximizes a signal to noise ratio snr the obtained optimal nonlinearity yields the maximum unbiased signal estimation performance which is known in the context of information theory it is found that a linear system is optimal for a gaussian noise but for a non gaussian noise there exist nonlinear systems that can achieve an snr higher than that obtained from linear systems this analysis refers to a system subjected to an additive non gaussian noise with a small signal input

selecting the number of classes under latent class regression a factor analytic analogue recently the regression extension of latent class analysis rlca model has received much attention in the field of medical research the basic rlca model summarizes shared features of measured multiple indicators as an underlying categorical variable and incorporates the covariate information in modeling both latent class membership and multiple indicators themselves to reduce complexity and enhance interpretability one usually fixes the number of classes in a given rlca often goodness of fit methods comparing various estimated models are used as a criterion to select the number of classes in this paper we propose a new method that is based on an analogous method used in factor analysis and does not require repeated fitting two ideas with application to many settings other than ours are synthesized in deriving the method a connection between latent class models and factor analysis and techniques of covariate marginalization and elimination a monte carlo simulation study is presented to evaluate the behavior of the selection procedure and compare to alternative approaches data from a study of how measured visual impairments affect older persons functioning are used for illustration

combining monte carlo and mean field like methods for inference in hidden markov random fields issues involving missing data are typical settings where exact inference is not tractable as soon as nontrivial interactions occur between the missing variables approximations are required and most of them are based either on simulation methods or on deterministic variational methods while variational methods provide fast and reasonable approximate estimates in many scenarios simulation methods offer more consideration of important theoretical issues such as accuracy of the approximation and convergence of the algorithms but at a much higher computational cost in this work we propose a new class of algorithms that combine the main features and advantages of both simulation and deterministic methods and consider applications to inference in hidden markov random fields hmrfs these algorithms can be viewed as stochastic perturbations of variational expectation maximization vem algorithms which are not tractable for hmrf we focus more specifically on one of these perturbations and we prove their almost sure convergence to the same limit set as the limit set of vem in addition experiments on synthetic and real world images show that the algorithm performance is very close and sometimes better than that of other existing simulation based and variational em like algorithms

3 d image denoising by local smoothing and nonparametric regression three dimensional 3 d images are becoming increasingly popular in image applications such as magnetic resonance imaging mri functional mri fmri and other image applications observed 3 d images often contain noise that should be removed beforehand for improving the reliability of subsequent image analyses in the literature most existing image denoising methods are for 2 d images their direct extensions to 3 d cases generally cannot handle 3 d images efficiently because the structure of 3 d images is often substantially more complicated than that of 2 d images for instance edge locations are surfaces in 3 d cases which are much more challenging to handle compared to edge curves in 2 d cases in this article we propose a novel 3 d image denoising procedure based on nonparametric estimation of a 3 d jump surface from noisy data one important feature of this method is its ability to preserve edges and major edge structures such as intersections of two edge surfaces pyramids pointed corn

snakes active contour models a snake is an energy minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges snakes are active contour models they lock onto nearby edges localizing them accurately scale space continuation can be used to enlarge the capture region surrounding a feature snakes provide a unified account of a number of visual problems including detection of edges lines and subjective contours motion tracking and stereo matching we have used snakes successfully for interactive interpretation in which user imposed constraint forces guide the snake near features of interest

application of the em algorithm for the multitarget multisensor tracking problem an important problem in surveillance and reconnaissance systems is the tracking of multiple moving targets in cluttered noise environments using outputs from a number of sensors possessing wide variations in individual characteristics and accuracies a number of approaches have been proposed for this multitarget multisensor tracking problem ranging from reasonably simple though ad hoc schemes to fairly complex but theoretically optimum approaches in this paper we describe an iterative procedure for time recursive multitarget multisensor tracking based on use of the expectation maximization em algorithm more specifically we pose the multitarget multisensor tracking problem as an incomplete data problem with the observable sensor outputs representing the incomplete data whereas the target associated sensor outputs constitute the complete data target updates at each time use an em based approach that calculates the maximum a posteriori map estimate of the target states under the assumption of appropriate motion models based on the outputs of disparate sensors the approach uses a markov random field mrf model of the associations between observations and targets and allows for estimation of joint association probabilities without explicit enumeration the advantage of this em based approach is that it provides a computationally efficient means for approaching the performance offered by theoretically optimum approaches that use explicit enumeration of the joint association probabilities we provide selected results illustrating the performance complexity characteristics of this em based approach compared with competing schemes

unified projection operator formalism in nonequilibrium statistical mechanics the method of projection operators which plays an important role in the field of nonequilibrium statistical mechanics has been established with the use of the liouville von neumann equation for a density matrix to eliminate irrelevant information from a whole system we formulate a unified and general projection operator method for dynamical variables the main features of our formalism parallel those for the liouville von neumann equation 1 two types of basic equations time convolution and time convolutionless decompositions are systematically obtained without specifying a projection operator 2 expansion formulas for both decompositions are also obtained 3 problems incorporating a time dependent liouville operator can be flexibly treated we apply the formulas to problems in random frequency modulation and low field resonance in conclusion our formalism yields a more direct and easier means of determining the average time evolution of an operator than the one for the liouville von neumann equation

em and beyond the basic theme of the em algorithm to repeatedly use complete data methods to solve incomplete data problems is also a theme of several more recent statistical techniques these techniques multiple imputation data augmentation stochastic relaxation and sampling importance resampling combine simulation techniques with complete data methods to attack problems that are difficult or impossible for em

the symplectic structure of kahler manifolds of nonpositive curvature on montre que la forme de kahler sur une variete de kahler complete simplement connexe w de courbure non positive est diffeomorphe a la forme symplectique standard sur r n ceci signifie que la structure symplectique sur un espace symetrique hermitien de type non compact est standard

digital image restoration using spatial interaction models this paper is concerned with developing fast nonrecursive algorithms for the minimum mean squared error restoration of degraded images the degradation is assumed to be due to a space invariant periodic nonseparable known point spread function and additive white noise our basic approach is to represent the images by a class of spatial interaction models namely the simultaneous autoregressve models and the conditional markov models defined on toroidal lattices and develop minimum mean squared error restoration algorithms using these models the restoration algorithms are optimal if the parameters characterizing the interaction models are exactly known however in practice the parameters are estimated from the images by using spatial interaction models we develop restoration algorithms that do not require the availability of the original image or its prototype the specific structure of the underlying lattice enables the implementation of the filters using fast fourier transform fft computations several restoration examples are given

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

segmentation given partial grouping constraints we consider data clustering problems where partial grouping is known a priori we formulate such biased grouping problems as a constrained optimization problem where structural properties of the data define the goodness of a grouping and partial grouping cues define the feasibility of a grouping we enforce grouping smoothness and fairness on labeled data points so that sparse partial grouping information can be effectively propagated to the unlabeled data considering the normalized cuts criterion in particular our formulation leads to a constrained eigenvalue problem by generalizing the rayleigh ritz theorem to projected matrices we find the global optimum in the relaxed continuous domain by eigendecomposition from which a near global optimum to the discrete labeling problem can be obtained effectively we apply our method to real image segmentation problems where partial grouping priors can often be derived based on a crude spatial attentional map that binds places with common salient features or focuses on expected object locations we demonstrate not only that it is possible to integrate both image structures and priors in a single grouping process but also that objects can be segregated from the background without specific object knowledge

quantum master equation for a system influencing its environment a perturbative quantum master equation is derived for a system interacting with its environment which is more general than the ones derived before our master equation takes into account the effect of the energy exchanges between the system and the environment and the conservation of energy in the finite total system this master equation describes relaxation mechanisms in isolated nanoscopic quantum systems in its most general form this equation is non markovian and a markovian version of it rules the long time relaxation we show that our equation reduces to the redfield equation in the limit where the energy of the system does not affect the density of state of its environment this master equation and the redfield one are applied to a spin environment model defined in terms of random matrices and compared with the solutions of the exact von neumann equation the comparison proves the necessity to allow energy exchange between the subsystem and the environment in order to correctly describe the relaxation in an isolated nanoscopic total system

convergence properties of the gibbs sampler for perturbations of gaussians 1993 and smith and roberts 1993 a host of other applications of these methods in the context of hierarchical bayesian models is described in all these examples the question arises as to the rate of convergence of the markov chain there is no unified theory to answer this question especially in the case of continuous densities on r n and currently many different approaches are being attempted for the different types of simulation methods one direction of research uses doeblin type bounds on bounded sets together with geometric drift conditions or some form of lyapounov functions to obtain rather general results on exponential convergence for continuous n densities on r see mengersen and tweedie 1996 for the metropolis algorithm and rosenthal 1995 for the gibbs sampler these results are then applied to certain special cases and yield bounds on the rate of convergence in variational norm distance another approach analyzes a discretized version of the problem using general discrete state space markov chain techniques see frieze kannan and polson 1994 finally there are attempts to use spectral analysis to obtain rates of convergence in the l sense where the markov chain operator is considered 2 as a bounded operator in the hilbert space of square integrable functions

on the shape of plane images this paper studies set patterns in mathbb r 2 and gives a definition of shape in terms of transformation groups where the transformations need not be rigid and linear but more structured than arbitrary homeomorphisms the resulting concept of shape classes and shape groups is studied analytically on such shape classes prior measures intended for bayesian image processing are introduced the priors are given as markov processes of the gibbs type on certain graphs and where the generators are simple geometric objects in mathbb r 2 for example line segments or other arcs these continuum based models differ from earlier lattice based ones in that they incorporate more shape information in the prior measures this leads to algorithms for pattern synthesis and image processing which have been implemented by apl code and applied in an extensive series of computer experiments since the algorithms are computer intensive a limit theorem for the prior measures is presented that is intended to sp

the indian buffet process an introduction and review the indian buffet process is a stochastic process defining a probability distribution over equivalence classes of sparse binary matrices with a finite number of rows and an unbounded number of columns this distribution is suitable for use as a prior in probabilistic models that represent objects using a potentially infinite array of features or that involve bipartite graphs in which the size of at least one class of nodes is unknown we give a detailed derivation of this distribution and illustrate its use as a prior in an infinite latent feature model we then review recent applications of the indian buffet process in machine learning discuss its extensions and summarize its connections to other stochastic processes

an integrated approach for scene understanding based on markov random field model in this paper we propose a markov random field model based approach as a unified and systematic way for modeling encoding and applying scene knowledge to the image understanding problem in our proposed scheme we formulate the image segmentation and interpretation problem as an integrated scheme and solve it through a general optimization algorithm more specifically the image is first segmented into a set of disjoint regions by a conventional region based segmentation technique which operates on image pixels and a region adjacency graph rag is then constructed from the resulting segmented regions based on the spatial adjacencies between regions our scheme then proceeds on the rag by defining the region merging and labeling problem based on the mrf models in the mrf model we specify the a priori knowledge about the optimal segmentation and interpretation in the form of clique functions and those clique functions are incorporated into the energy function to be minimized by a general optimization technique in the proposed scheme the image segmentation and interpretation processes cooperate in the simultaneous optimization process such that the erroneous segmentation and misinterpretation due to incomplete knowledge about each problem domain can be compensately recovered by continuous estimation of the single unified energy function we exploit the proposed scheme to segment and interpret natural outdoor scene images

bayes inference in the tobit censored regression model we consider the bayes estimation of the tobit censored regression model with normally distributed errors a simple condition for the existence of posterior moments is provided suitable versions of monte carlo procedures based on symmetric multivariate t distributions and laplacian approximations in a certain parametrization are developed and illustrated ideas involving data augmentation and gibbs sampling cf tanner and wong 1987 and gelfand and smith 1990 are also developed the methods are compared in two examples with diffuse priors and various combinations of sample sizes and degrees of censoring

the effect of source amplitude and phase in matched field source localization traditional matched field processing is typically employed for localization of a source with unknown amplitude and phase at transmission the spectral information of the source is estimated via maximum likelihood and is used for localization with the bartlett processor maximum a posteriori estimation of the spectrum of the source is here proposed along with the estimation of source location and noise variance where the joint posterior probability distribution of all parameters is computed with gibbs sampling it is shown that the proposed method has a higher probability of correct localization than the traditional approach

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

multiple window parallel adaptive boundary finding in computer vision the problem considered in this paper is the estimation of highly variable object boundaries in noisy images boundaries may be those of a tank in an ir image a spinal canal in a cat scan a cloud in a visible light image etc or they may be internal to an object such as the boundary between a spherical surface and a cylindrical surface in a manufactured object the focus of the paper is on parallel multiple window boundary estimation algorithms here the image field is parti tioned into an array of rectangular windows and boundary finders are run simultaneously within the windows the boundary segments found within the windows are then seamed together to obtain meaningful global boundaries the entire procedure is treated within a maximum likelihood estimation framework that we have developed for boundary finding although our multiple window estimation approach can be used with a number of local boundary finding algorithms we concen trate on one which is based on dynamic programming and will produce the true maximum likelihood boundary some theoretical considera tions for boundary model design and boundary finding runtime are covered included is the use of a low computational cost f test for test ing whether a window contains a boundary and an analytical treatment which shows that use of coarse pixels with a chi square test or an f test improves the probability of correctly recognizing whether a boundary is present in a window

multigrid tomographic inversion with variable resolution data and image spaces a multigrid inversion approach that uses variable resolutions of both the data space and the image space is proposed since the computational complexity of inverse problems typically increases with a larger number of unknown image pixels and a larger number of measurements the proposed algorithm further reduces the computation relative to conventional multigrid approaches which change only the image space resolution at coarse scales the advantage is particularly important for data rich applications where data resolutions may differ for different scales applications of the approach to bayesian reconstruction algorithms in transmission and emission tomography with a generalized gaussian markov random field image prior are presented both with a poisson noise model and with a quadratic data term simulation results indicate that the proposed multigrid approach results in significant improvement in convergence speed compared to the fixed grid iterative coordinate descent method and a multigrid method with fixed data resolution

the diastatic exponential of a symmetric space let m g be a real analytic kahler manifold we say that a smooth map expp w m from a neighbourhood w of the origin of tpm into m is a diastatic exponential at p if it satisfies begin array lll left d rm exp p right 0 rm id t pm d p left rm exp p left v right right g p left v v right forall v in w end array where dp is calabi s diastasis function at p the usual exponential expp obviously satisfied these equations when dp is replaced by the square of the geodesics distance from p in this paper we prove that for every point p of an hermitian symmetric space of noncompact type m there exists a globally defined diastatic exponential centered in p which is a diffeomorphism and it is uniquely determined by its restriction to polydisks an analogous result holds true in an open dense neighbourhood of every point of m the compact dual of m we also provide a geometric interpretation of the symplectic duality map recently introduced in di scala and loi adv math 217 2336 2352 2008 in terms of diastatic exponentials as a byproduct of our analysis we show that the symplectic duality map pulls back the reproducing kernel of m to the reproducing kernel of m

approximate random variate generation from infinitely divisible distributions with applications to bayesian inference summary stochastic processes with independent increments play a central role in bayesian nonparametric inference the distributions of the increments of these processes aside from fixed points of discontinuity are infinitely divisible and their laplace and or fourier transforms in the levy representation are usually known conventional bayesian inference in this context has been limited largely to providing point estimates of the random quantities of interest although markov chain monte carlo methods have been used to obtain a fuller analysis in the context of dirichlet process priors in this paper we propose and implement a general method for simulating infinitely divisible random variates when their fourier or laplace transforms are available in the levy representation theoretical justification is established by proving a convergence theorem that is a sampling form of a classical theorem in probability the results provide a method for implementing bayesian nonparametric inference by using a wide range of stochastic processes as priors

on the equivalence of soft wavelet shrinkage total variation diffusion total variation regularization and sides soft wavelet shrinkage total variation tv diffusion tv regularization and a dynamical system called sides are four useful techniques for discontinuity preserving denoising of signals and images in this paper we investigate under which circumstances these methods are equivalent in the one dimensional case first we prove that haar wavelet shrinkage on a single scale is equivalent to a single step of space discrete tv diffusion or regularization of two pixel pairs in the translationally invariant case we show that applying cycle spinning to haar wavelet shrinkage on a single scale can be regarded as an absolutely stable explicit discretization of tv diffusion we prove that space discrete tv diffusion and tv regularization are identical and that they are also equivalent to the sides system when a specific force function is chosen afterwards we show that wavelet shrinkage on multiple scales can be regarded as a single step diffusion filtering or regularization of the laplacian pyramid of the signal we analyze possibilities to avoid gibbs like artifacts for multiscale haar wavelet shrinkage by scaling the thresholds finally we present experiments where hybrid methods are designed that combine the advantages of wavelets and pde variational approaches these methods are based on iterated shift invariant wavelet shrinkage at multiple scales with scaled thresholds

symplectic duality of symmetric spaces let be a complex n dimensional hermitian symmetric space endowed with the hyperbolic form hyp denote by m fs the compact dual of m hyp where fs is the fubini study form on m our first result is theorem 1 1 where with the aid of the theory of jordan triple systems we construct an explicit symplectic duality namely a diffeomorphism satisfying and for the pull back of m where 0 is the restriction to m of the flat kahler form of the hermitian positive jordan triple system associated to m amongst other properties of the map m we also show that it takes complete complex and totally geodesic submanifolds of m through the origin to complex linear subspaces of as a byproduct of the proof of theorem 1 1 we get an interesting characterization theorem 5 3 of the bergman form of a hermitian symmetric space in terms of its restriction to classical complex and totally geodesic submanifolds passing through the origin

generalized flows for optimal inference in higher order mrf map use of higher order clique potentials in mrf map problems has been limited primarily because of the inefficiencies of the existing algorithmic schemes we propose a new combinatorial algorithm for computing optimal solutions to 2 label mrf map problems with higher order clique potentials the algorithm runs in time o 2 kn 3 in the worst case k is size of clique and n is the number of pixels a special gadget is introduced to model flows in a higher order clique and a technique for building a flow graph is specified based on the primal dual structure of the optimization problem the notions of the capacity of an edge and a cut are generalized to define a flow problem we show that in this flow graph when the clique potentials are submodular the max flow is equal to the min cut which also is the optimal solution to the problem we show experimentally that our algorithm provides significantly better solutions in practice and is hundreds of times faster than solution schemes like dual decomposition 1 trws 2 and reduction 3 4 5 the framework represents a significant advance in handling higher order problems making optimal inference practical for medium sized cliques

object based estimation of dense motion fields motion estimation belongs to key techniques in image sequence processing segmentation of the motion fields such that ideally each independently moving object uniquely corresponds to one region is one of the essential elements in object based image processing this paper is concerned with unsupervised simultaneous estimation of dense motion fields and their segmentations it is based on a stochastic model relating image intensities to motion information based on the analysis of natural images a region based model of motion compensated prediction error is proposed in each region the error is modeled by a white stationary generalized gaussian random process the motion field and its segmentation are themselves modeled by a compound gibbs markov random field accounting for statistical bindings in spatial direction and along the direction of motion trajectories the a posteriori distribution of the motion field for a given image sequence is formulated as an objective function such that its maximization results in the map estimate a deterministic multiscale relaxation technique with regular structure is employed for optimization of the objective function simulation results are in a good agreement with human perception for both the motion fields and their segmentations

multiple time correlation functions for non markovian interaction beyond the quantum regression theorem we derive the dynamical equation of the reduced propagator an object that evolves state vectors of the system conditioned to the dynamics of its environment which is not necessarily in the vacuum state at the initial time such a reduced propagator is essential to obtain multiple time correlation functions mtcfs we also study the evolution of mtcfs within the weak coupling limit and show that the quantum regression theorem is in general not satisfied we illustrate the theory in two different cases first solving an exact model and second presenting the results of the numerical integration for a system coupled with a thermal environment through a nondiagonal interaction

segmentation based on fusion of range and intensity images using robust trimmed methods abstract this paper proposes a segmentation algorithm based on fusion of range and intensity images using robust trimmed methods based on the bayesian theory a priori knowledge is represented using the markov random field mrf a maximum a posteriori map estimator is constructed using the edge features extracted from both range and intensity images objects are represented by a number of local planar surfaces in range images and the parametric space for surface representation is constructed with the surface parameters estimated pixel by pixel based on the least trimmed squares lts method whereas in intensity images the trimmed variance is adopted as the feature for edge extraction a final edge map is obtained by the map estimator that is constructed using the likelihood functions based on the edge information obtained from range and intensity images finally an image is segmented using the fused edge map computer simulation results show that our new segmentation algorithm effectively segments test images independent of shadow noise and lighting environment

convergence analysis of reweighted sum product algorithms markov random fields are designed to represent structured dependencies among large collections of random variables and are well suited to capture the structure of real world signals many fundamental tasks in signal processing e g smoothing denoising segmentation etc require efficient methods for computing approximate marginal probabilities over subsets of nodes in the graph the marginalization problem though solvable in linear time for graphs without cycles is computationally intractable for general graphs with cycles this intractability motivates the use of approximate ldquomessage passingrdquo algorithms this paper studies the convergence and stability properties of the family of reweighted sum product algorithms a generalization of the widely used sum product or belief propagation algorithm in which messages are adjusted with graph dependent weights for pairwise markov random fields we derive various conditions that are sufficient to ensure convergence and also provide bounds on the geometric convergence rates when specialized to the ordinary sum product algorithm these results provide strengthening of previous analyses we prove that some of our conditions are necessary and sufficient for subclasses of homogeneous models but not for general models the experimental simulations on various classes of graphs validate our theoretical results

non markovian quantum dynamics extended correlated projection superoperators the correlated projection superoperator techniques provide a better understanding about how correlations lead to strong non markovian effects in open quantum systems their superoperators are independent of initial state which may not be suitable for some cases to improve this we develop another approach that is extending the composite system before use the correlated projection superoperator techniques such an approach allows the choice of different superoperators for different initial states we apply these techniques to a simple model to illustrate the general approach the numerical simulations of the full schrodinger equation of the model reveal the power and efficiency of the method

voxel based bayesian lesion symptom mapping most existing voxel based lesion symptom mapping methods are based on the same statistical foundation null hypothesis significance testing nhst the two major limitations of these methods are the inability to infer that there is no difference in lesion proportions and a requirement for multiple comparison correction we propose a bayesian approach that directly models the posterior distribution of lesion proportion difference and makes decisions based on inference on this posterior distribution compared to nhst based approaches our bayesian approach yields inference results with clearer semantics and does not require multiple comparison correction we evaluated our bayesian method using simulated data and data from a study of acute ischemic left hemispheric stroke results of both experiments indicate that the bayesian approach is sensitive in detecting regions that characterize group differences

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

non markovian master equations from entanglement with stationary unobserved degrees of freedom we deduce a class of non markovian completely positive master equations which describe a system in a composite bipartite environment consisting of a markovian reservoir and additional stationary unobserved degrees of freedom that modulate the dissipative coupling the entanglement induced memory effects can persist for arbitrary long times and affect the relaxation to equilibrium as well as induce corrections to the quantum regression theorem by considering the extra degrees of freedom as a discrete manifold of energy levels strong non exponential behaviour can arise such as for example power law and stretched exponential decays

graph spectral image smoothing using the heat kernel a new method for smoothing both gray scale and color images is presented that relies on the heat diffusion equation on a graph we represent the image pixel lattice using a weighted undirected graph the edge weights of the graph are determined by the gaussian weighted distances between local neighboring windows we then compute the associated laplacian matrix the degree matrix minus the adjacency matrix anisotropic diffusion across this weighted graph structure with time is captured by the heat equation and the solution i e the heat kernel is found by exponentiating the laplacian eigensystem with time image smoothing is accomplished by convolving the heat kernel with the image and its numerical implementation is realized by using the krylov subspace technique the method has the effect of smoothing within regions but does not blur region boundaries we also demonstrate the relationship between our method standard diffusion based pdes fourier domain signal processing and spectral clustering experiments and comparisons on standard images illustrate the effectiveness of the method