predictive updating methods with application to bayesian classification we propose algorithms based on random draws from predictive distributions of unknown quantities missing values for instance this procedure can either be iterative which is a special variation of the gibbs sampler or be sequential which is a variation of sequential imputation in the latter case one can update the posterior distribution with new observations easily the methods proposed have intuitive statistical implications and can be generalized to accommodate other bayesian like procedures we display some applications of the method in connection with the bayesian bootstrap classification hierarchical models and selection of variables in particular as an application of the method we present a unified treatment of switching regression models driven by a general binary process and we develop a bayesian testing procedure some simulations and a real example are used to illustrate the methods proposed

estimating latent distributions in recurrent choice data this paper introduces a flexible class of stochastic mixture models for the analysis and interpretation of individual differences in recurrent choice and other types of count data these choice models are derived by specifying elements of the choice process at the individual level probability distributions are introduced to describe variations in the choice process among individuals and to obtain a representation of the aggregate choice behavior due to the explicit consideration of random effect sources the choice models are parsimonious and readily interpretable an easy to implement em algorithm is presented for parameter estimation two applications illustrate the proposed approach

a locally adaptive window for signal matching this article presents a signal matching algorithm that can select an appropriate window size adaptively so as to obtain both precise and stable estimation of correspondences matching two signals by calculating the sum of squared differences ssd over a certain window is a basic technique in computer vision given the signals and a window there are two factors that determine the difficulty of obtaining precise matching the first is the variation of the signal within the window which must be large enough relative to noise that the ssd values exhibit a clear and sharp minimum at the correct disparity the second factor is the variation of disparity within the window which must be small enough that signals of corresponding positions are duly compared these two factors present conflicting requirements to the size of the matching window since a larger window tends to increase the signal variation but at the same time tends to include points of different disparity a window size must be adaptively selected depending on local variations of signal and disparity in order to compute a most certain estimate of disparity at each point there has been little work on a systematic method for automatic window size selection the major difficulty is that while the signal variation is measurable from the input the disparity variation is not since disparities are what we wish to calculate we introduce here a statistical model of disparity variation within a window and employ it to establish a link between the window size and the uncertainty of the computed disparity this allows us to choose the window size that minimizes uncertainty in the disparity computed at each point this article presents a theory for the model and the resultant algorithm together with analytical and experimental results that demonstrate their effectiveness

mixture densities maximum likelihood and the em algorithm the problem of estimating the parameters which determine a mixture density has been the subject of a large diverse body of literature spanning nearly ninety years during the last two decades the

semi supervised cross entropy clustering with information bottleneck constraint abstract in this paper we propose a semi supervised clustering method cec ib that models data with a set of gaussian distributions and that retrieves clusters based on a partial labeling provided by the user partition level side information by combining the ideas from cross entropy clustering cec with those from the information bottleneck method ib our method trades between three conflicting goals the accuracy with which the data set is modeled the simplicity of the model and the consistency of the clustering with side information experiments demonstrate that cec ib performs similar as gaussian mixture models in a classical semi supervised scenario but is faster more robust to noisy labels automatically determines the optimal number of clusters and performs well when not all classes are present in the side information moreover in contrast to many other semi supervised models it can be successfully applied in discovering natural subgroups if the partition level side information is derived from the top levels of a hierarchical clustering

robust transient signal detection using the oversampled gabor representation the gabor representation has been investigated previously as an effective and efficient means of detecting transient signals the major problem associated with the most commonly used form of the gabor representation is that it assumes that signals have integer arrival times and modulation frequencies an assumption that if violated causes signal miss match and deterioration in detector performance the present correspondence presents the concept of oversampling the gabor representation which makes the representation more robust to certain types of signal mismatch to show this the performance of an oversampled gabor detector is compared to that of critically sampled gabor detectors gabor coefficients are calculated in the oversampled representation via the least squares method which has been shown to be superior to the more commonly used biorthogonal function method in terms of corresponding detector performance

stereo using monocular cues within the tensor voting framework we address the fundamental problem of matching in two static images the remaining challenges are related to occlusion and lack of texture our approach addresses these difficulties within a perceptual organization framework considering both binocular and monocular cues initially matching candidates for all pixels are generated by a combination of matching techniques the matching candidates are then embedded in disparity space where perceptual organization takes place in 3d neighborhoods and thus does not suffer from problems associated with scanline or image neighborhoods the assumption is that correct matches produce salient coherent surfaces while wrong ones do not matching candidates that are consistent with the surfaces are kept and grouped into smooth layers thus we achieve surface segmentation based on geometric and not photometric properties surface overextensions which are due to occlusion can be corrected by removing matches whose projections are not consistent in color with their neighbors of the same surface in both images finally the projections of the refined surfaces on both images are used to obtain disparity hypotheses for unmatched pixels the final disparities are selected after a second tensor voting stage during which information is propagated from more reliable pixels to less reliable ones we present results on widely used benchmark stereo pairs

maximum likelihood estimation of latent interaction effects with the lms method in the context of structural equation modeling a general interaction model with multiple latent interaction effects is introduced a stochastic analysis represents the nonnormal distribution of the joint indicator vector as a finite mixture of normal distributions the latent moderated structural equations lms approach is a new method developed for the analysis of the general interaction model that utilizes the mixture distribution and provides a ml estimation of model parameters by adapting the em algorithm the finite sample properties and the robustness of lms are discussed finally the applicability of the new method is illustrated by an empirical example

cluster number selection for a small set of samples using the bayesian ying yang model one major problem in cluster analysis is the determination of the number of clusters in this paper we describe both theoretical and experimental results in determining the cluster number for a small set of samples using the bayesian kullback ying yang byy model selection criterion under the second order approximation we derive a new equation for estimating the smoothing parameter in the cost function finally we propose a gradient descent smoothing parameter estimation approach that avoids complicated integration procedure and gives the same optimal result

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

arc transitive abelian regular covers of cubic graphs abstract quite a lot of attention has been paid recently to the construction of edge or arc transitive covers of symmetric graphs in most cases the approach has involved voltage graph techniques which are excellent for finding regular covers in which the group of covering transformations is either cyclic or elementary abelian or more generally homocyclic but are not so easy to use when the covering group has other forms even when it is abelian but not homocyclic in this paper a different approach is introduced that can be used more widely this new approach takes a universal group for the action of the automorphism group of the base graph and uses reidemeister schreier theory to obtain a presentation for a universal covering group and some representation theory and other methods for determining suitable quotients this approach is then used to find all arc transitive abelian regular covers of k 4 k 3 3 the cube q 3 and the petersen graph a sequel will do the same for the heawood graph

transient signal detection using prior information in the likelihood ratio test a uniform approach to the problem of detecting a deterministic transient signal of unknown waveshape in gaussian noise that is applicable for a general class of signals is provided it is assumed that the vector representation consists of a group of zero coefficients plus a group of unknown nonzero coefficients starting at a certain index performance indices for a likelihood ratio test detector of such a generalized transient signal are derived for three cases a both the starting index and the number of unknown nonzero signal coefficients are known b only the number of unknown nonzero signal coefficients is known and c neither of these is known it is shown that the performance of detector a is always better than that of b and c and conditions under which the performance of detector b is better than that of c are derived the theoretical results are demonstrated by considering signals of different assumed structures

feature selection based on the approximation of class densities by finite mixtures of special type abstract a new method of feature selection based on the approximation of class conditional densities by a mixture of parameterized densities of a special type suitable especially for multimodal data is presented no search procedure is needed when using the proposed method its performance is tested both on real simulated data

anderson acceleration for fixed point iterations this paper concerns an acceleration method for fixed point iterations that originated in work of d g anderson j assoc comput mach 12 1965 pp 547 560 which we accordingly call anderson acceleration here this method has enjoyed considerable success and wide usage in electronic structure computations where it is known as anderson mixing however it seems to have been untried or underexploited in many other important applications moreover while other acceleration methods have been extensively studied by the mathematics and numerical analysis communities this method has received relatively little attention from these communities over the years a recent paper by h fang and y saad numer linear algebra appl 16 2009 pp 197 221 has clarified a remarkable relationship of anderson acceleration to quasi newton secant updating methods and extended it to define a broader anderson family of acceleration methods in this paper our goals are to shed additional light on anderson acceleration and to draw further attention to its usefulness as a general tool we first show that on linear problems anderson acceleration without truncation is essentially equivalent in a certain sense to the generalized minimal residual gmres method we also show that the type 1 variant in the fang saad anderson family is similarly essentially equivalent to the arnoldi full orthogonalization method we then discuss practical considerations for implementing anderson acceleration and illustrate its performance through numerical experiments involving a variety of applications

assessing a mixture model for clustering with the integrated completed likelihood we propose an assessing method of mixture model in a cluster analysis setting with integrated completed likelihood for this purpose the observed data are assigned to unknown clusters using a maximum a posteriori operator then the integrated completed likelihood icl is approximated using the bayesian information criterion bic numerical experiments on simulated and real data of the resulting icl criterion show that it performs well both for choosing a mixture model and a relevant number of clusters in particular icl appears to be more robust than bic to violation of some of the mixture model assumptions and it can select a number of dusters leading to a sensible partitioning of the data

finite mixture varying coefficient models for analyzing longitudinal heterogenous data this paper aims to develop a mixture model to study heterogeneous longitudinal data on the treatment effect of heroin use from a california civil addict program each component of the mixture is characterized by a varying coefficient mixed effect model we use the bayesian p splines approach to approximate the varying coefficient functions we develop markov chain monte carlo algorithms to estimate the smooth functions unknown parameters and latent variables in the model we use modified deviance information criterion to determine the number of components in the mixture a simulation study demonstrates that the modified deviance information criterion selects the correct number of components and the estimation of unknown quantities is accurate we apply the proposed model to the heroin treatment study furthermore we identify heterogeneous longitudinal patterns copyright 2011 john wiley sons ltd

linear criteria for lifting automorphisms of elementary abelian regular coverings abstract for a given finite connected graph a group h of automorphisms of and a finite group a a natural question can be raised as follows find all the connected regular coverings of having a as its covering transformation group on which each automorphism in h can be lifted in this paper we investigate the regular coverings with a z p n an elementary abelian group and get some new matrix theoretical characterizations for an automorphism of the base graph to be lifted as one of its applications we classify all the connected regular covering graphs of the petersen graph satisfying the following two properties 1 the covering transformation group is isomorphic to the elementary abelian p group z p n and 2 the group of fibre preserving automorphisms of a covering graph acts arc transitively as a byproduct some new 2 and 3 arc transitive graphs are constructed

detecting a suddenly arriving dynamic profile of finite duration this paper addresses the detection of a suddenly arriving dynamic profile of a finite duration often called a transient change in contrast to the traditional abrupt change detection where the post change period is assumed to be infinitely long the detection of a suddenly arriving transient change should be done before it disappears the detection of transient changes after their disappearance is considered as missed hence the traditional quickest change detection criterion minimizing the average detection delays provided a prescribed false alarm rate is compromised the proposed optimality criterion minimizes the worst case probability of missed detection provided that the worst case probability of false alarm during a certain period is upper bounded a suboptimal cusum type transient change detection algorithm based on a subclass of truncated sequential probability ratio tests is proposed the optimization of the proposed algorithm in this subclass leads to a specially designed finite moving average test the proposed method is analyzed theoretically and by simulation a special attention is paid to the case of gaussian observations with a dynamic profile

dense features for semi dense stereo correspondence we present a new feature based algorithm for stereo correspondence most of the previous feature based methods match sparse features like edge pixels producing only sparse disparity maps our algorithm detects and matches dense features between the left and right images of a stereo pair producing a semi dense disparity map our dense feature is defined with respect to both images of a stereo pair and it is computed during the stereo matching process not a preprocessing step in essence a dense feature is a connected set of pixels in the left image and a corresponding set of pixels in the right image such that the intensity edges on the boundary of these sets are stronger than their matching error which is the difference in intensities between corresponding boundary pixels our algorithm produces accurate semi dense disparity maps leaving featureless regions in the scene unmatched it is robust requires little parameter tuning can handle brightness differences between images nonlinear errors and is fast linear complexity

how to find g admissible abelian regular coverings of a graph abstract given a finite connected simple graph and a subgroup g of its automorphism group a general method for finding all finite abelian regular coverings of that admit a lift of each element of g is developed as an application all connected arc transitive abelian regular coverings of the petersen graph are classified up to isomorphism

cross trees edge and superpixel priors based cost aggregation for stereo matching in this paper we propose a novel cross trees structure to perform the non local cost aggregation strategy and the cross trees structure consists of a horizontal tree and a vertical tree compared to other spanning trees the significant superiorities of the cross trees are that the trees constructions are efficient and the trees are exactly unique since the constructions are independent on any local or global property of the image itself additionally two different priors edge prior and superpixel prior are proposed to tackle the false cost aggregations which cross the depth boundaries hence our method contains two different algorithms in terms of cross trees prior by traversing the two crossed trees successively a fast non local cost aggregation algorithm is performed twice to compute the aggregated cost volume performance evaluation on the 27 middlebury data sets shows that both our algorithms outperform the other two tree based non local methods namely minimum spanning tree mst and segment tree st graphical abstractdisplay omitted highlightswe propose a novel cross trees structure to perform the non local cost aggregation the trees constructions are unique and independent on the image itself we propose and incorporate two priors into the non local framework new edge weights function is designed according to the trees and the priors optimal support regions can be chose by cutting the cost aggregation flow on paths

detection of random transient signals via hyperparameter estimation difficulties arise with the generalized likelihood ratio test glrt in situations where one or more of the unknown signal parameters requires an enumeration that is computationally intractable in the transient signal detection problem the frequency characteristics of the signal are typically unknown therefore even if an aggregate signal bandwidth is assumed the estimation problem intrinsic to the glrt requires an enumeration of all possible sets of signal locations within the monitored band in this paper a prior distribution is imposed over those portions of the signal parameter space that traditionally require enumeration by replacing intractable enumeration over possible signal characteristics with an a priori signal distribution and by estimating the hyperparameters of the prior distribution jointly with other signal parameters it is possible to obtain a new formulation of the glrt that avoids enumeration and is computationally feasible the glrt philosophy is not changed by this approach what is different from the original glrt is the underlying signal model the performance of this new approach appears to be competitive with that of a scheme of emerging acceptance the power law detector

orientation guided geodesic weighting for patchmatch based stereo matching we propose an orientation guided geodesic weighting oggw strategy for local stereo matching we propose a method of cost volume filtering combining a multipoint lpa method with our oggw strategy we propose a patchmatch filter with curved surface fitting pmf cs to obtain a disparity map with sub pixel accuracy recently patchmatch based methods for local stereo matching are experiencing great progress with the use of compact and over segmented regions that have similar intensities or colors using patches as support regions this paper proposes an orientation guided geodesic weighting oggw strategy to search for an approximate shortest path from a support pixel in the patch to a pixel of interest along a guided orientation the oggw is computed by accumulating intensity differences or color dissimilarities between connected pixels along the path after obtaining matching cost updates by model fitting the oggw is used for weighted averaging on the updated costs to obtain a filtered cost volume in addition a new filtering method that combines the patchmatch filter with curved surface fitting pmf cs is presented in this paper curved surface fitting along with outliers removal is carried out to seek for a reliable regression model for estimating the disparities on a patch and to achieve a disparity map with sub pixel accuracy we conduct a number of experiments to evaluate the performances of oggw and pmf cs on cost volume filtering and disparity estimation experimental results show that our algorithm produces accurate stereo matching results and outperforms the current state of the art patchmatch based methods

arc transitive elementary abelian covers of the octahedron graph abstract in this paper we construct the pairwise non congruent elementary abelian covers of the octahedron graph o 6 which admit a lift of an arc transitive group of automorphisms of o 6 it shows that if the covering transformation group is a 2 group then its rank is less than or equal to 7 and there exist exactly 14 non congruent covering projections in total which admit lifts of arc transitive subgroups of the full automorphism group of o 6 if the covering transformation group is an odd prime p group then its rank is 1 3 4 6 or 7 and there exist p 4 such non congruent covering projections in total

improved transient signal detection using a wavepacket based detector with an extended translation invariant wavelet transform this paper presents the theory of m band extended translation invariant eti wavelet transforms the eti generalizes the translation invariant wavelet transform of weiss 1933 it is shown that iteration of the eti in a tree structure provides a signal decomposition into an orthonormal wavepacket basis other properties such as translation invariance and invertibility of the transform are proven the theory is then applied to transient signal detection through development of a family of translation invariant wavepacket based detectors this family of detectors provides improved the performance over previously defined wavepacket based detectors a performance analysis is conducted roc curves generated by monte carlo simulation are presented indicating the detector performance the detector performance is demonstrated to be independent of the signal translation

detection of transient signals by the gabor representation gabor representation is used for the detection of transient signals with unknown arrival times a one sided exponential window function is used which seems to be most appropriate for transient modelling explicit expressions for the gabor coefficients are given for this window function when the given signal is random so are the coefficients the second order moments of the gabor coefficients are computed for a white noise signal these are then used to introduce a detection statistic based on the gabor coefficients the proposed detector is capable of separating transients having different arrival times even in this case where their waveforms partially overlap

a simple stereo algorithm to recover precise object boundaries and smooth surfaces in area based stereo matching there is a problem called boundary overreach i e the recovered object boundary turns out to be wrongly located away from the real one this is especially harmful to segmenting objects using depth information a few approaches have been proposed to solve this problem however these techniques tend to degrade on smooth surfaces that is there seems to be a trade off problem between recovering precise object edges and obtaining smooth surfaces in this paper we propose a new simple method to solve this problem using multiple stereo pairs and multiple windowing our method detects the region where the boundary overreach is likely to occur let us call it bo region and adopts appropriate methods for the bo and non bo regions although the proposed method is quite simple the experimental results have shown that it is very effective at recovering both sharp object edges at their correct locations and smooth object surfaces

all purpose and plug in power law detectors for transient signals a power law statistic operating on discrete fourier transform dft data has emerged as a basis for a remarkably robust detector of transient signals having unknown structure location and strength we offer a number of improvements to nuttall s 1994 original power law detector specifically the power law detector requires that its data be prenormalized and spectrally white a constant false alarm rate cfar and self whitening version is developed and analyzed further it is noted that transient signals tend to be contiguous both in the temporal and frequency sense and consequently new power law detectors in the frequency and the wavelet domains are given the resulting detectors offer exceptional performance and are extremely easy to implement there are no parameters to tune they may be considered plug in solutions to the transient detection problem and are all purpose in that they make minimal assumptions on the structure of the transient signal save of some degree of agglomeration of energy in time and or frequency

semiparametric maximum likelihood for measurement error model regression this paper presents an em algorithm for semiparametric likelihood analysis of linear generalized linear and nonlinear regression models with measurement errors in explanatory variables a structural model is used in which probability distributions are specified for a the response and b the measurement error a distribution is also assumed for the true explanatory variable but is left unspecified and is estimated by nonparametric maximum likelihood for various types of extra information about the measurement error distribution the proposed algorithm makes use of available routines that would be appropriate for likelihood analysis of a and b if the true x were available simulations suggest that the semiparametric maximum likelihood estimator retains a high degree of efficiency relative to the structural maximum likelihood estimator based on correct distributional assumptions and can outperform maximum likelihood based on an incorrect distributional assumption the approach is illustrated on three examples with a variety of structures and types of extra information about the measurement error distribution

sar amplitude probability density function estimation based on a generalized gaussian model in the context of remotely sensed data analysis an important problem is the development of accurate models for the statistics of the pixel intensities focusing on synthetic aperture radar sar data this modeling process turns out to be a crucial task for instance for classification or for denoising purposes in this paper an innovative parametric estimation methodology for sar amplitude data is proposed that adopts a generalized gaussian gg model for the complex sar backscattered signal a closed form expression for the corresponding amplitude probability density function pdf is derived and a specific parameter estimation algorithm is developed in order to deal with the proposed model specifically the recently proposed method of log cumulants molc is applied which stems from the adoption of the mellin transform instead of the usual fourier transform in the computation of characteristic functions and from the corresponding generalization of the concepts of moment and cumulant for the developed gg based amplitude model the resulting molc estimates turn out to be numerically feasible and are also analytically proved to be consistent the proposed parametric approach was validated by using several real ers 1 xsar e sar and nasa jpl airborne sar images and the experimental results prove that the method models the amplitude pdf better than several previously proposed parametric models for backscattering phenomena

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

wigner higher order moment spectra definition properties computation and application to transient signal analysis the wigner higher order moment spectra whos are defined as extensions of the wigner ville distribution wd to higher order moment spectra domains a general class of time frequency higher order moment spectra is also defined in terms of arbitrary higher order moments of the signal as gen eralizations of the cohen s general class of time frequency rep resentations the properties of the general class of time fre quency higher order moment spectra can be related to the properties of whos which are in fact extensions of the prop erties of the wd discrete time and frequency wigner higher order moment spectra dtf whos distributions are intro duced for signal processing applications and are shown to be implemented with two fft based algorithms one application is presented where the wigner bispectrum wb which is a whos in the third order moment domain is utilized for the detection of transient signals embedded in noise the wb is compared with the wd in terms of simulation examples and analysis of real sonar data it is shown that better detection schemes can be derived in low signal to noise ratio when the wb is applied

a hybrid sem algorithm for high dimensional unsupervised learning using a finite generalized dirichlet mixture this paper applies a robust statistical scheme to the problem of unsupervised learning of high dimensional data we develop analyze and apply a new finite mixture model based on a generalization of the dirichlet distribution the generalized dirichlet distribution has a more general covariance structure than the dirichlet distribution and offers high flexibility and ease of use for the approximation of both symmetric and asymmetric distributions we show that the mathematical properties of this distribution allow high dimensional modeling without requiring dimensionality reduction and thus without a loss of information this makes the generalized dirichlet distribution more practical and useful we propose a hybrid stochastic expectation maximization algorithm hsem to estimate the parameters of the generalized dirichlet mixture the algorithm is called stochastic because it contains a step in which the data elements are assigned randomly to components in order to avoid convergence to a saddle point the adjective hybrid is justified by the introduction of a newton raphson step moreover the hsem algorithm autonomously selects the number of components by the introduction of an agglomerative term the performance of our method is tested by the classification of several pattern recognition data sets the generalized dirichlet mixture is also applied to the problems of image restoration image object recognition and texture image database summarization for efficient retrieval for the texture image summarization problem results are reported for the vistex texture image database from the mit media lab

extracting view dependent depth maps from a collection of images stereo correspondence algorithms typically produce a single depth map in addition to the usual problems of occlusions and textureless regions such algorithms cannot model the variation in scene or object appearance with respect to the viewing position in this paper we propose a new representation that overcomes the appearance variation problem associated with an image sequence rather than estimating a single depth map we associate a depth map with each input image or a subset of them our representation is motivated by applications such as view interpolation and depth based segmentation for model building or layer extraction we describe two approaches to extract such a representation from a sequence of images the first approach which is more classical computes the local depth map associated with each chosen reference frame independently the novelty of this approach lies in its combination of shiftable windows temporal selection and graph cut optimization the second approach simultaneously optimizes a set of self consistent depth maps at multiple key frames since multiple depth maps are estimated simultaneously visibility can be modeled explicitly and disparity consistency imposed across the different depth maps results which include a difficult specular scene example show the effectiveness of our approach

the use of the wavelet transform in the detection of an unknown transient signal for the detection of a not perfectly known signal in noise usually no uniformly most powerful test exists and thus a detector performance depends on the signal representation the use of the wavelet representation of signals to perform a new detection scheme is discussed the advantage of using this particular representation is shown it is shown that prior information regarding the relative bandwidth and the time bandwidth product of the signal to be detected is efficiently incorporated into the detection problem formulation thus the proposed detection scheme is most suitable for detection of unknown transient signals when prior information about the signal time bandwidth product and relative bandwidth exists in these cases the wavelet representation based detector performs better than any other the structure of the proposed detectors is discussed and its performance is evaluated using monte carlo simulations

a bayesian approach to finite mixture models in bioassay via data augmentation and gibbs sampling and its application to insecticide resistance after continued treatment with an insecticide within the population of the susceptible insects resistant strains will occur it is important to know whether there are any resistant strains what the proportions are and what the median lethal doses are for the insecticide lwin and martin 1989 biometrics 45 721 732 propose a probit mixture model and use the em algorithm to obtain the maximum likelihood estimates for the parameters this approach has difficulties in estimating the confidence intervals and in testing the number of components we propose a bayesian approach to obtaining the credible intervals for the location and scale of the tolerances in each component and for the mixture proportions by using data augmentation and gibbs sampler we use bayes factor for model selection and determining the number of components we illustrate the method with data published in lwin and martin 1989

on the convergence properties of the em algorithm two convergence aspects of the em algorithm are studied i does the em algorithm find a local maximum or a stationary value of the incomplete data likelihood function ii does the sequence of parameter estimates generated by em converge several convergence results are obtained under conditions that are applicable to many practical situations two useful special cases are a if the unobserved complete data specification can be described by a curved exponential family with compact parameter space all the limit points of any em sequence are stationary points of the likelihood function b if the likelihood function is unimodal and a certain differentiability condition is satisfied then any em sequence converges to the unique maximum likelihood estimate a list of key properties of the algorithm is included

edgeworth expanded gaussian mixture density modeling instead of increasing the order of the edgeworth expansion of a single gaussian kernel we suggest using mixtures of edgeworth expanded gaussian kernels of moderate order we introduce a simple closed form solution for estimating the kernel parameters based on weighted moment matching furthermore we formulate the extension to the multivariate case which is not always feasible with algebraic density approximation procedures

adaptive mixture density estimation abstract a recursive nonparametric method is developed for performing density estimation derived from mixture models kernel estimation and stochastic approximation the asymptotic performance of the method dubbed adaptive mixtures priebe and marchette pattern recognition24 1197 1209 1991 for its data driven development of a mixture model approximation to the true density is investigated using the method of sieves simulations are included indicating convergence properties for some simple examples

a multiple baseline stereo a stereo matching method that uses multiple stereo pairs with various baselines generated by a lateral displacement of a camera to obtain precise distance estimates without suffering from ambiguity is presented matching is performed simply by computing the sum of squared difference ssd values the ssd functions for individual stereo pairs are represented with respect to the inverse distance and are then added to produce the sum of ssds this resulting function is called the sssd in inverse distance it is shown that the sssd in inverse distance function exhibits a unique and clear minimum at the correct matching position even when the underlying intensity patterns of the scene include ambiguities or repetitive patterns the authors first define a stereo algorithm based on the sssd in inverse distance and present a mathematical analysis to show how the algorithm can remove ambiguity and increase precision experimental results with real stereo images are presented to demonstrate the effectiveness of the algorithm

detecting a transient signal by bispectral analysis a method for detecting a transient waveform of unknown shape in additive stationary noise is presented the method uses a statistic computed from the sample bispectrum of a sampled record of the signal plus noise of length t n tau where 1 tau is the sampling rate the key result underlying the method is that the bispectrum of the noise is zero in a triangle that is a proper subset of the principal domain triangle it is shown that the probability of detecting the signal is high if 3 16 sup 1 2 n sup 5 6 is larger than rho sup 1 where rho is the energy signal to noise ratio the result implies that the bispectrum based test may detect a weak signal of unknown form which evades detection by other methods

maximum likelihood topographic map formation we introduce a new unsupervised learning algorithm for kernel based topographic map formation of heteroscedastic gaussian mixtures that allows for a unified account of distortion error vector quantization log likelihood and kullback leibler divergence

performance analysis of a class of transient detection algorithms a unified framework the detection of short duration nonstationary signals which are commonly referred to as transients is often performed in the time frequency transform domain an analytical framework within which the performance of different detectors based on linear transforms can be easily compared for different classes of signals is developed a given class of transients is modeled as a signal existing in a linear subspace plus a mismatch signal closed form expressions are derived for the best and the worst detection performance for all possible transients in a given class using this framework the performance of detectors based on the gabor transform and on the short time fourier transform is evaluated and compared

detection and parameter estimation of a transient signal using order statistics detection and parameter estimation of a transient signal in noise is a problem of many applications it is characterized by the fact that some of the measurements consist of noise only modern statistical signal processing techniques are applied on a discrete version of the received data and are implemented by digital signal processing dsp in this correspondence we show how order statistics os based signal processing which is of a discrete nature can be used for simultaneous detection and estimation of parameters such as time of arrival and signal duration of a sampled transient signal in white noise we show that the resulting processors are more robust than the conventional processors whereas their performance is about the same at the cost of increased computational complexity

surfaces with occlusions from layered stereo we propose a new binocular stereo algorithm that estimates scene structure as a collection of smooth surface patches the disparities within each patch are modeled by a continuous valued spline while the extent of each patch is represented via a pixelwise partitioning of the images disparities and extents are alternately estimated in an iterative energy minimization framework experimental results demonstrate that for scenes consisting of smooth surfaces the proposed algorithm significantly improves upon the state of the art

adaptive window length detection of underwater transients using wavelets this paper describes a detection method that adapts to unknown characteristics of the underlying transient signal such as location length and time frequency content it applies a set of embedded detectors tuned to a number of signal partitions the detectors are based on the wavelet theory whereby two different techniques are examined one using local fourier transform and the other using discrete wavelet transform the detection statistics are computed so as to enable prewhitening of unknown colored noise and to allow for a constant false alarm rate detection an adapted segmentation of the signal is next obtained with a goal of finding the largest detection statistics within each segment of the partition the detectors are tested using several underwater acoustic transient signals buried in ambient sea noise

asymptotic convergence rate of the em algorithm for gaussian mixtures it is well known that the convergence rate of the expectation maximization em algorithm can be faster than those of convention first order iterative algorithms when the overlap in the given mixture is small but this argument has not been mathematically proved yet this article studies this problem asymptotically in the setting of gaussian mixtures under the theoretical framework of xu and jordan 1996 it has been proved that the asymptotic convergence rate of the em algorithm for gaussian mixtures locally around the true solution is o e0 5 e where e 0 is an arbitrarily small number o x means that it is a higher order infinitesimal as x 0 and e is a measure of the average overlap of gaussians in the mixture in other words the large sample local convergence rate for the em algorithm tends to be asymptotically superlinear when e tends to zero

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

the optimal power law for the detection of a gaussian burst in a background of gaussian noise the optimal power law is analyzed using characteristic function techniques for a signal occupying more than about lambda 0 5 of the integration time the standard square law energy detector is close to optimal among the class of power law detectors the optimal power is nu 3 for lambda approximately 0 3 increasing rapidly as lambda decreases below this value the snr advantage of the optimal processor is not significant for lambda 0 5 but exceeds 1 db for lambda

shape and the stereo correspondence problem we examine the implications of shape on the process of finding dense correspondence and half occlusions for a stereo pair of images the desired property of the disparity map is that it should be a piecewise continuous function which is consistent with the images and which has the minimum number of discontinuities to zeroth order piecewise continuity becomes piecewise constancy using this approximation we first discuss an approach for dealing with such a fronto parallel shapeless world and the problems involved therein we then introduce horizontal and vertical slant to create a first order approximation to piecewise continuity in particular we emphasize the following geometric fact a horizontally slanted surface i e having depth variation in the direction of the separation of the two cameras will appear horizontally stretched in one image as compared to the other image thus while corresponding two images n pixels on a scanline in one image may correspond to a different number of pixels m in the other image this leads to three important modifications to existing stereo algorithms a due to unequal sampling existing intensity matching metrics must be modified b unequal numbers of pixels in the two images must be allowed to correspond to each other and c the uniqueness constraint which is often used for detecting occlusions must be changed to an interval uniqueness constraint we also discuss the asymmetry between vertical and horizontal slant and the central role of non horizontal edges in the context of vertical slant using experiments we discuss cases where existing algorithms fail and how the incorporation of these new constraints provides correct results

an incremental nested partition method for data clustering clustering methods are a powerful tool for discovering patterns in a given data set through an organization of data into subsets of objects that share common features motivated by the independent use of some different partitions criteria and the theoretical and empirical analysis of some of its properties in this paper we introduce an incremental nested partition method which combines these partitions criteria for finding the inner structure of static and dynamic datasets for this we proved that there are relationships of nesting between partitions obtained respectively from these partition criteria and besides that the sensitivity when a new object arrives to the dataset is rigorously studied our algorithm exploits all of these mathematical properties for obtaining the hierarchy of clusterings moreover we realize a theoretical and experimental comparative study of our method with classical hierarchical clustering methods such as single link and complete link and other more recently introduced methods the experimental results over databases of uci repository and the afp and tdt2 news collections show the usefulness and capability of our method to reveal different levels of information hidden in datasets