a new approach to computing the euler characteristic in this paper we describe a new approach to computing the euler characteristic of a three dimensional digital image our approach is based on computing the change in numbers of black components tunnels and cavities in 3 x 3 x 3 neighborhood of an object black point due to its deletion the existing algorithms to computing the euler characteristic of a 3d digital image are based on counting the numbers of all k dimensional elements 0 k 3 in a polyhedral representation of the image our approach can be modified for 6 26 18 6 6 18 and other connectivity relations of grid points a parallel implementation of the algorithm is described using the concept of sub fields

stein type shrinkage estimators in gamma regression model with application to prostate cancer data gamma regression is applied in several areas such as life testing forecasting cancer incidences genomics rainfall prediction experimental designs and quality control gamma regression models allow for a monotone and no constant hazard in survival models owing to the broad applicability of gamma regression we propose some novel and improved methods to estimate the coefficients of gamma regression model we combine the unrestricted maximum likelihood ml estimators and the estimators that are restricted by linear hypothesis and we present stein type shrinkage estimators ses we then develop an asymptotic theory for ses and obtain their asymptotic quadratic risks in addition we conduct monte carlo simulations to study the performance of the estimators in terms of their simulated relative efficiencies it is evident from our studies that the proposed ses outperform the usual ml estimators furthermore some tabular and graphical representations are given as proofs of our assertions this study is finally ended by appraising the performance of our estimators for a real prostate cancer data

a filter bank for the directional decomposition of images theory and design the authors introduce a directionally oriented 2 d filter bank with the property that the individual channels may be critically sampled without loss of information the passband regions of the component filters are wedge shaped and thus provide directional information it is shown that these filter bank outputs may be maximally decimated to achieve a minimum sample representation in a way that permits the original signal to be exactly reconstructed the authors discuss the theory for directional decomposition and reconstruction in addition implementation issues are addressed where realizations based on both recursive and nonrecursive filters are considered

image modeling and denoising with orientation adapted gaussian scale mixtures we develop a statistical model to describe the spatially varying behavior of local neighborhoods of coefficients in a multiscale image representation neighborhoods are modeled as samples of a multivariate gaussian density that are modulated and rotated according to the values of two hidden random variables thus allowing the model to adapt to the local amplitude and orientation of the signal a third hidden variable selects between this oriented process and a nonoriented scale mixture of gaussians process thus providing adaptability to the local orientedness of the signal based on this model we develop an optimal bayesian least squares estimator for denoising images and show through simulations that the resulting method exhibits significant improvement over previously published results obtained with gaussian scale mixtures

the contourlet transform an efficient directional multiresolution image representation the limitations of commonly used separable extensions of one dimensional transforms such as the fourier and wavelet transforms in capturing the geometry of image edges are well known in this paper we pursue a true two dimensional transform that can capture the intrinsic geometrical structure that is key in visual information the main challenge in exploring geometry in images comes from the discrete nature of the data thus unlike other approaches such as curvelets that first develop a transform in the continuous domain and then discretize for sampled data our approach starts with a discrete domain construction and then studies its convergence to an expansion in the continuous domain specifically we construct a discrete domain multiresolution and multidirection expansion using nonseparable filter banks in much the same way that wavelets were derived from filter banks this construction results in a flexible multiresolution local and directional image expansion using contour segments and thus it is named the contourlet transform the discrete contourlet transform has a fast iterated filter bank algorithm that requires an order n operations for n pixel images furthermore we establish a precise link between the developed filter bank and the associated continuous domain contourlet expansion via a directional multiresolution analysis framework we show that with parabolic scaling and sufficient directional vanishing moments contourlets achieve the optimal approximation rate for piecewise smooth functions with discontinuities along twice continuously differentiable curves finally we show some numerical experiments demonstrating the potential of contourlets in several image processing applications

analysis synthesis techniques for subband image coding analysis synthesis systems designed for low bit rate image coding their impact on overall system quality and their computational complexity are discussed the investigation focuses on the design of analysis synthesis systems for image coding and the perceptual impact of these systems at low bit rates two objectives are emphasized in developing these systems confining the total size of the subband images to be equal to the original image size and designing the filters so that perceptual distortion is not introduced by the analysis synthesis system methods based on circular convolution and symmetric extensions are developed and discussed in detail the theory design and implementation of both recursive and nonrecursive filtering systems are discussed methods are introduced which display advantages over conventional quadrature mirror filter based approaches

classification of binary textures using the 1 d boolean model the one dimensional 1 d boolean model is used to calculate features for the description of binary textures each two dimensional 2 d texture is converted into several 1 d strings by scanning it according to raster vertical horizontal or hilbert sequences several different probability distributions for the segment lengths created this way are used to model their distribution therefore each texture is described by a set of boolean models classification is performed by calculating the overlapping probability between corresponding models the method is evaluated with the help of 32 different binary textures and the pros and cons of the approach are discussed

texture analysis with variational hidden markov trees a variational bayes formulation of the hidden markov tree hmt model is proposed for texture analysis utilizing a multilevel wavelet decomposition of imagery the variational method yields an approximation to the full posterior of the hmt parameters texture classification is based on the posterior predictive distribution or marginalized evidence with example results presented

preservation of subband symmetry in multirate signal coding the article derives necessary and sufficient conditions for a multirate linear phase fir filter bank acting on a symmetrically extended finite length input signal to produce either symmetric or antisymmetric downsampled subbands such techniques have gained popularity for image coding applications and we provide the solution to a key technical problem in terms of the input and filter symmetries and the downsampling ratio

multiresolution feature extraction and selection for texture segmentation an approach is described for unsupervised segmentation of textured images local texture properties are extracted using local linear transforms that have been optimized for maximal texture discrimination local statistics texture energy measures are estimated at the output of an equivalent filter bank by means of a nonlinear transformation absolute value followed by an iterative gaussian smoothing algorithm this procedure generates a multiresolution sequence of feature planes with a half octave scale progression a feature reduction technique is then applied to the data and is determined by simultaneously diagonalizing scatter matrices evaluated at two different spatial resolutions this approach provides a good approximation of r a fisher s 1950 multiple linear discriminants and has the advantage of requiring no a priori knowledge this feature reduction methods appears to be an improvement on the commonly used karhunen loeve transform and allows efficient texture segmentation based on simple thresholding

combining estimators to improve structural model estimation and inference under quadratic loss asymptotically semi parametric estimators of the parameters in linear structural models have the same sampling properties in finite samples the sampling properties of these estimators vary and large biases may result for sample sizes often found in practice with a goal of improving asymptotic risk performance and finite sample efficiency properties we investigate the idea of combining correlated structural equation estimators with different finite and asymptotic sampling characteristics based on a quadratic loss measure we present evidence that the finite sample performance of the resulting combination estimator can be notably superior to that of a leading traditional moment based estimator

detection of 3 d simple points for topology preserving transformations with application to thinning the problems of 3 d digital topology preservation under binary transformations and 3 d object thinning are considered in this correspondence first the authors establish the conditions under which transformation of an object voxel to a non object voxel or its inverse does not affect the image topology an efficient algorithm to detect a simple point has been proposed on the basis of those conditions in this connection some other interesting properties of 3 d digital geometry are also discussed using these properties and the simple point detection algorithm the authors have proposed an algorithm to generate a surface skeleton so that the topology of the original image is preserved the shape of the image is maintained as much as possible and the results are less affected by noise

surfaces in three dimensional digital images this is one of a series of reports on the digital geometry of three dimensional images such as those produced by computed tomography in this report we define simple surface points and simple closed surfaces and show that any connected collection of simple surface points form a simple closed surface thus proving a three dimensional analog of the two dimensional jordan curve theorem we also show that the converse is not a theorem in contrast to the two dimensional case and discuss more complex surface types finally we show that the two dimensional analog of our definition of simple closed surface characterizes simple closed curves but that several other characterizations of 2 d curves when extended to 3 d are not adequate to characterize surfaces

problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak abstract we draw attention to two problems associated with the use of instrumental variables iv the importance of which for empirical work has not been fully appreciated first the use of instruments that explain little of the variation in the endogenous explanatory variables can lead to large inconsistencies in the iv estimates even if only a weak relationship exists between the instruments and the error in the structural equation second in finite samples iv estimates are biased in the same direction as ordinary least squares ols estimates the magnitude of the bias of iv estimates approaches that of ols estimates as the r 2 between the instruments and the endogenous explanatory variable approaches 0 to illustrate these problems we reexamine the results of a recent paper by angrist and krueger who used large samples from the u s census to estimate wage equations in which quarter of birth is used as an instrument for educational attainment we find evidence that despite huge sample sizes th

bayesian texture classification based on contourlet transform and byy harmony learning of poisson mixtures as a newly developed 2 d extension of the wavelet transform using multiscale and directional filter banks the contourlet transform can effectively capture the intrinsic geometric structures and smooth contours of a texture image that are the dominant features for texture classification in this paper we propose a novel bayesian texture classifier based on the adaptive model selection learning of poisson mixtures on the contourlet features of texture images the adaptive model selection learning of poisson mixtures is carried out by the recently established adaptive gradient bayesian ying yang harmony learning algorithm for poisson mixtures it is demonstrated by the experiments that our proposed bayesian classifier significantly improves the texture classification accuracy in comparison with several current state of the art texture classification approaches

comments on svd based modeling for image texture classification using wavelet transform this correspondence points out that in there are some errors in two important formulas and the performance of the proposed singular value decomposition svd feature is severely overestimated it also presents a modified svd feature which competes with the gabor feature

directional multiscale modeling of images using the contourlet transform the contourlet transform is a new two dimensional extension of the wavelet transform using multiscale and directional filter banks the contourlet expansion is composed of basis images oriented at various directions in multiple scales with flexible aspect ratios given this rich set of basis images the contourlet transform effectively captures smooth contours that are the dominant feature in natural images we begin with a detailed study on the statistics of the contourlet coefficients of natural images using histograms to estimate the marginal and joint distributions and mutual information to measure the dependencies between coefficients this study reveals the highly non gaussian marginal statistics and strong interlocation interscale and interdirection dependencies of contourlet coefficients we also find that conditioned on the magnitudes of their generalized neighborhood coefficients contourlet coefficients can be approximately modeled as gaussian random variables based on these findings we model contourlet coefficients using a hidden markov tree hmt model with gaussian mixtures that can capture all interscale interdirection and interlocation dependencies we present experimental results using this model in image denoising and texture retrieval applications in denoising the contourlet hmt outperforms other wavelet methods in terms of visual quality especially around edges in texture retrieval it shows improvements in performance for various oriented textures

image denoising using scale mixtures of gaussians in the wavelet domain we describe a method for removing noise from digital images based on a statistical model of the coefficients of an overcomplete multiscale oriented basis neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables a gaussian vector and a hidden positive scalar multiplier the latter modulates the local variance of the coefficients in the neighborhood and is thus able to account for the empirically observed correlation between the coefficient amplitudes under this model the bayesian least squares estimate of each coefficient reduces to a weighted average of the local linear estimates over all possible values of the hidden multiplier variable we demonstrate through simulations with images contaminated by additive white gaussian noise that the performance of this method substantially surpasses that of previously published methods both visually and in terms of mean squared error

texture classification and segmentation using wavelet packet frame and gaussian mixture model in this paper we propose a scheme for texture classification and segmentation the methodology involves an extraction of texture features using the wavelet packet frame decomposition this is followed by a gaussian mixture based classifier which assigns each pixel to the class each subnet of the classifier is modeled by a gaussian mixture model and each texture image is assigned to the class to which pixels of the image most belong this scheme shows high recognition accuracy in the classification of brodatz texture images it can also be expanded to an unsupervised texture segmentation using a kullback leibler divergence between two gaussian mixtures the proposed method was successfully applied to brodatz mosaic image segmentation and fabric defect detection

dose response modeling in mental health using stein like estimators with instrumental variables a mental health trial is analyzed using a dose response model in which the number of sessions attended by the patients is deemed indicative of the dose of psychotherapeutic treatment here the parameter of interest is the difference in causal treatment effects between the subpopulations that take part in different numbers of therapy sessions for this data set interactions between random treatment allocation and prognostic baseline variables provide the requisite instrumental variables while the corresponding two stage least squares tsls estimator tends to have smaller bias than the ordinary least squares ols estimator the tsls suffers from larger variance it is therefore appealing to combine the desirable properties of the ols and tsls estimators such a trade off is achieved through an affine combination of these two estimators using mean squared error as a criterion this produces the semi parametric stein like spsl estimator as introduced by judge and mittelhammer 2004 the spsl estimator is used in conjunction with multiple imputation with chained equations to provide an estimator that can exploit all available information simulated data are also generated to illustrate the superiority of the spsl estimator over its ols and tsls counterparts a package entitled steiniv implementing these methods has been made available through the r platform 2017 the authors statistics in medicine published by john wiley sons ltd

signal analysis using a multiresolution form of the singular value decomposition this paper proposes a multiresolution form of the singular value decomposition svd and shows how it may be used for signal analysis and approximation it is well known that the svd has optimal decorrelation and subrank approximation properties the multiresolution form of svd proposed here retains those properties and moreover has linear computational complexity by using the multiresolution svd the following important characteristics of a signal may he measured at each of several levels of resolution isotropy sphericity of principal components self similarity under scaling and resolution of mean squared error into meaningful components theoretical calculations are provided for simple statistical models to show what might be expected results are provided with real images to show the usefulness of the svd decomposition

topology preservation in 3d digital space abstract in a 3d two tone digital space the conditions under which sequential transformation of an object point to a non object point or its inverse does not affect the image topology are investigated the concept of border connectivity is introduced and an efficient algorithm for checking a simple point is proposed in this connection some other interesting properties of 3d digital geometry are also discussed this investigation may be used in thinning and topology preserving morphological transformations in 3d digital space

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

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

parameter estimation of poisson mixture with automated model selection through byy harmony learning finite mixture is widely used in the fields of information processing and data analysis however its model selection i e the selection of components in the mixture for a given sample data set has been still a rather difficult task recently the bayesian ying yang byy harmony learning has provided a new approach to the gaussian mixture modeling with a favorite feature that model selection can be made automatically during parameter learning in this paper based on the same byy harmony learning framework for finite mixture we propose an adaptive gradient byy learning algorithm for poisson mixture with automated model selection it is demonstrated well by the simulation experiments that this adaptive gradient byy learning algorithm can automatically determine the number of actual poisson components for a sample data set with a good estimation of the parameters in the original or true mixture where the components are separated in a certain degree moreover the adaptive gradient byy learning algorithm is successfully applied to texture classification

dominant local binary patterns for texture classification this paper proposes a novel approach to extract image features for texture classification the proposed features are robust to image rotation less sensitive to histogram equalization and noise it comprises of two sets of features dominant local binary patterns dlbp in a texture image and the supplementary features extracted by using the circularly symmetric gabor filter responses the dominant local binary pattern method makes use of the most frequently occurred patterns to capture descriptive textural information while the gabor based features aim at supplying additional global textural information to the dlbp features through experiments the proposed approach has been intensively evaluated by applying a large number of classification tests to histogram equalized randomly rotated and noise corrupted images in outex brodatz meastex and curet texture image databases our method has also been compared with six published texture features in the experiments it is experimentally demonstrated that the proposed method achieves the highest classification accuracy in various texture databases and image conditions

the byy annealing learning algorithm for gaussian mixture with automated model selection bayesian ying yang byy learning has provided a new mechanism that makes parameter learning with automated model selection via maximizing a harmony function on a backward architecture of the byy system for the gaussian mixture however since there are a large number of local maxima for the harmony function any local searching algorithm such as the hard cut em algorithm does not work well in order to overcome this difficulty we propose a simulated annealing learning algorithm to search the global maximum of the harmony function being expressed as a kind of deterministic annealing em procedure it is demonstrated by the simulation experiments that this byy annealing learning algorithm can efficiently and automatically determine the number of clusters or gaussians during the learning process moreover the byy annealing learning algorithm is successfully applied to two real life data sets including iris data classification and unsupervised color image segmentation

the singular value decomposition and long and short spaces of noisy matrices using geometrical algebraic and statistical arguments it is clarified why and when the singular value decomposition is successful in so called subspace methods first the concepts of long and short spaces are introduced and a fundamental asymmetry in the consistency properties of the estimates is discussed the model which is associated with the short space can be estimated consistently but the estimates of the original data which follow from the long space are always inconsistent an expression is found for the asymptotic bias in terms of canonical angles which can be estimated from the data this allows all equivalent reconstructions of the original signals to be described as a matrix ball the center of which is the minimum variance estimate remarkably the canonical angles also appear in the optimal weighting that is used in weighted subspace fitting approaches the results are illustrated with a numerical simulation a number of examples are discussed

digital topology and geometry in medical imaging a survey digital topology and geometry refers to the use of topologic and geometric properties and features for images defined in digital grids such methods have been widely used in many medical imaging applications including image segmentation visualization manipulation interpolation registration surface tracking object representation correction quantitative morphometry etc digital topology and geometry play important roles in medical imaging research by enriching the scope of target outcomes and by adding strong theoretical foundations with enhanced stability fidelity and efficiency this paper presents a comprehensive yet compact survey on results principles and insights of methods related to digital topology and geometry with strong emphasis on understanding their roles in various medical imaging applications specifically this paper reviews methods related to distance analysis and path propagation connectivity surface tracking image segmentation boundary and centerline detection topology preservation and local topological properties skeletonization and object representation correction and quantitative morphometry a common thread among the topics reviewed in this paper is that their theory and algorithms use the principle of digital path connectivity path propagation and neighborhood analysis

the em mpm algorithm for segmentation of textured images analysis and further experimental results in this paper we present new results relative to the expectation maximization maximization of the posterior marginals em mpm algorithm for simultaneous parameter estimation and segmentation of textured images the em mpm algorithm uses a markov random field model for the pixel class labels and alternately approximates the mpm estimate of the pixel class labels and estimates parameters of the observed image model the goal of the em mpm algorithm is to minimize the expected value of the number of misclassified pixels we present new theoretical results in this paper which show that the algorithm can be expected to achieve this goal to the extent that the em estimates of the model parameters are close to the true values of the model parameters we also present new experimental results demonstrating the performance of the em mpm algorithm

radon representation based feature descriptor for texture classification in this paper we aim to handle the intraclass variation resulting from the geometric transformation and the illumination change for more robust texture classification to this end we propose a novel feature descriptor called radon representation based feature descriptor rrfd rrfd converts the original pixel represented images into radon pixel images by using the radon transform the new radon pixel representation is more informative in geometry and has a much lower dimension subsequently rrfd efficiently achieves affine invariance by projecting an image or an image patch from the space of radon pixel pairs onto an invariant feature space by using a ratiogram i e the histogram of ratios between the areas of triangle pairs the illumination invariance is also achieved by defining an illumination invariant distance metric on the invariant feature space comparing to the existing radon transform based texture features which only achieve rotation and or scaling invariance rrfd achieves affine invariance the experimental results on curet show that rrfd is a powerful feature descriptor that is suitable for texture classification

new characterizations of simple points in 2d 3d and 4d discrete spaces a point of a discrete object is called simple if it can be deleted from this object without altering topology in this article we present new characterizations of simple points which hold in dimensions 2 3 and 4 and which lead to efficient algorithms for detecting such points in order to prove these characterizations we establish two confluence properties of the collapse operation which hold in the neighborhood of a point in spaces of low dimension this work is settled in the framework of cubical complexes which provides a sound topological basis for image analysis and allows to retrieve the main notions and results of digital topology in particular the notion of simple point

the laplacian pyramid as a compact image code we describe a technique for image encoding in which local operators of many scales but identical shape serve as the basis functions the representation differs from established techniques in that the code elements are localized in spatial frequency as well as in space pixel to pixel correlations are first removed by subtracting a lowpass filtered copy of the image from the image itself the result is a net data compression since the difference or error image has low variance and entropy and the low pass filtered image may represented at reduced sample density further data compression is achieved by quantizing the difference image these steps are then repeated to compress the low pass image iteration of the process at appropriately expanded scales generates a pyramid data structure the encoding process is equivalent to sampling the image with laplacian operators of many scales thus the code tends to enhance salient image features a further advantage of the present code is that it is well suited for many image analysis tasks as well as for image compression fast algorithms are described for coding and decoding

texture classification using refined histogram in this correspondence we propose a novel efficient and effective refined histogram rh for modeling the wavelet subband detail coefficients and present a new image signature based on the rh model for supervised texture classification our rh makes use of a step function with exponentially increasing intervals to model the histogram of detail coefficients and the concatenation of the rh model parameters for all wavelet subbands forms the so called rh signature to justify the usefulness of the rh signature we discuss and investigate some of its statistical properties these properties would clarify the sufficiency of the signature to characterize the wavelet subband information in addition we shall also present an efficient rh signature extraction algorithm based on the coefficient counting technique which helps to speed up the overall classification system performance we apply the rh signature to texture classification using the well known databases experimental results show that our proposed rh signature in conjunction with the use of symmetrized kullback leibler divergence gives a satisfactory classification performance compared with the current state of the art methods

the nonsubsampled contourlet transform theory design and applications in this paper we develop the nonsubsampled contourlet transform nsct and study its applications the construction proposed in this paper is based on a nonsubsampled pyramid structure and nonsubsampled directional filter banks the result is a flexible multiscale multidirection and shift invariant image decomposition that can be efficiently implemented via the a trous algorithm at the core of the proposed scheme is the nonseparable two channel nonsubsampled filter bank nsfb we exploit the less stringent design condition of the nsfb to design filters that lead to a nsct with better frequency selectivity and regularity when compared to the contourlet transform we propose a design framework based on the mapping approach that allows for a fast implementation based on a lifting or ladder structure and only uses one dimensional filtering in some cases in addition our design ensures that the corresponding frame elements are regular symmetric and the frame is close to a tight one we assess the performance of the nsct in image denoising and enhancement applications in both applications the nsct compares favorably to other existing methods in the literature

statistical properties of bit plane probability model and its application in supervised texture classification the modeling of wavelet subband histograms via the product bernoulli distributions pbd has received a lot of interest and the pbd model has been applied successfully in texture image retrieval in order to fully understand the usefulness and effectiveness of the pbd model and its associated signature namely the bit plane probability bp signature on image processing applications we discuss and investigate some of their statistical properties these properties would help to clarify the sufficiency of the bp signature to characterize wavelet subbands which in turn justifies its use in real time applications we apply the bp signature on supervised texture classification problem and experimental results suggest that the weighted l1 norm rather than the standard l1 norm should be used for the bp signature comparative classification experiments show that our method outperforms the current state of the art generalized gaussian density approaches

multiscale image segmentation using wavelet domain hidden markov models we introduce a new image texture segmentation algorithm hmtseg based on wavelets and the hidden markov tree hmt model the hmt is a tree structured probabilistic graph that captures the statistical properties of the coefficients of the wavelet transform since the hmt is particularly well suited to images containing singularities edges and ridges it provides a good classifier for distinguishing between textures utilizing the inherent tree structure of the wavelet hmt and its fast training and likelihood computation algorithms we perform texture classification at a range of different scales we then fuse these multiscale classifications using a bayesian probabilistic graph to obtain reliable final segmentations since hmtseg works on the wavelet transform of the image it can directly segment wavelet compressed images without the need for decompression into the space domain we demonstrate the performance of hmtseg with synthetic aerial photo and document image segmentations

texture classification by modeling joint distributions of local patterns with gaussian mixtures texture classification generally requires the analysis of patterns in local pixel neighborhoods statistically the underlying processes are comprehensively described by their joint probability density functions jpdfs even for small neighborhoods however stable estimation of jpdfs by joint histograms jhsts is often infeasible since the number of entries in the jhst exceeds by far the number of pixels in a typical texture region moreover evaluation of distance functions between jhsts is often computationally prohibitive practically the number of entries in a jhst is therefore reduced by considering only two pixel patterns leading to 2d jhsts known as cooccurrence matrices or by quantization of the gray levels in local patterns to only two gray levels yielding local binary patterns lbps both approaches result in a loss of information we introduce here a framework for supervised texture classification which reduces or avoids this information loss local texture neighborhoods are first filtered by a filter bank without further quantization the jpdf of the filter responses is then described parametrically by gaussian mixture models gmms we show that the parameters of the gmms can be reliably estimated from small image regions moreover distances between the thus modelled jpdfs of different texture patterns can be computed efficiently in closed form from their model parameters we furthermore extend this texture descriptor to achieve full invariance to rotation we evaluate the framework for different filter banks on the brodatz texture set we first show that combining the lbp difference filters with the gmm based density estimator outperforms the classical lbp approach and its codebook extensions when replacing these rather elementary difference filters by the wavelet frame transform wft the performance of the framework on all 111 brodatz textures exceeds the one obtained more recently by spin image and rift descriptors by lazebnik et al

sensitivity analysis for instrumental variables regression with overidentifying restrictions instrumental variables regression iv regression is a method for making causal inferences about the effect of a treatment based on an observational study in which there are unmeasured confounding variables the method requires one or more valid instrumental variables ivs a valid iv is a variable that is associated with the treatment is independent of unmeasured confounding variables and has no direct effect on the outcome often there is uncertainty about the validity of the proposed ivs when a researcher proposes more than one iv the validity of these ivs can be tested through the overidentifying restrictions test although the overidentifying restrictions test does provide some information the test has no power versus certain alternatives and can have low power versus many alternatives due to its omnibus nature to fully address uncertainty about the validity of the proposed ivs we argue that a sensitivity analysis is needed a sensitivity analysis examines the impact of plausible amounts of

filtering for texture classification a comparative study in this paper we review most major filtering approaches to texture feature extraction and perform a comparative study filtering approaches included are laws masks 1980 ring wedge filters dyadic gabor filter banks wavelet transforms wavelet packets and wavelet frames quadrature mirror filters discrete cosine transform eigenfilters optimized gabor filters linear predictors and optimized finite impulse response filters the features are computed as the local energy of the filter responses the effect of the filtering is highlighted keeping the local energy function and the classification algorithm identical for most approaches for reference comparisons with two classical nonfiltering approaches co occurrence statistical and autoregressive model based features are given we present a ranking of the tested approaches based on extensive experiments

mendelian randomization incorporating uncertainty about pleiotropy mendelian randomization mr requires strong assumptions about the genetic instruments of which the most difficult to justify relate to pleiotropy in a two sample mr different methods of analysis are available if we are able to assume m1 no pleiotropy fixed effects meta analysis m2 that there may be pleiotropy but that the average pleiotropic effect is zero random effects meta analysis and m3 that the average pleiotropic effect is nonzero mr egger in the latter 2 cases we also require that the size of the pleiotropy is independent of the size of the effect on the exposure selecting one of these models without good reason would run the risk of misrepresenting the evidence for causality the most conservative strategy would be to use m3 in all analyses as this makes the weakest assumptions but such an analysis gives much less precise estimates and so should be avoided whenever stronger assumptions are credible we consider the situation of a two sample design when we are unsure which of these 3 pleiotropy models is appropriate the analysis is placed within a bayesian framework and bayesian model averaging is used we demonstrate that even large samples of the scale used in genome wide meta analysis may be insufficient to distinguish the pleiotropy models based on the data alone our simulations show that bayesian model averaging provides a reasonable trade off between bias and precision bayesian model averaging is recommended whenever there is uncertainty about the nature of the pleiotropy

two symmetrical thinning algorithms for 3d binary images based on p simple points in this paper we propose two 3d symmetrical thinning algorithms based on the parallel deletion of p simple points the first one permits surface skeletons to be obtained the second one permits curve skeletons to be extracted and as far as we know this is the only symmetrical curve thinning algorithm which preserves topology these algorithms have been conceived in order to obtain precise results on simple specific objects parallelepipeds consequently we can predict the number of deletion iterations the number of deleted points and the skeleton of these objects obtained by the two algorithms which is hardly ever possible to assess with other algorithms even on these simple objects

a semiparametric basis for combining estimation problems under quadratic loss when there is uncertainty concerning the appropriate statistical model estimator to use in representing the data sampling process we consider a basis for optimally combining estimation problems the objective is to produce natural adaptive estimators that are free of subjective choices and tuning parameters in the context of two competing multivariate linear statistical models estimators we demonstrate a semiparametric stein like spsl estimator that under quadratic loss has superior risk performance relative to the conventional least squares estimator the relationship of the spsl estimator to the family of stein estimators is noted and asymptotic and analytic finite sample risk properties of the estimator are developed for some special cases as an application we consider the problem of combining two polar linear models and demonstrate a corresponding spsl estimator an extensive sampling experiment is used to investigate the finite sample performance of the spsl estimator over a wide range of

a new shape preserving parallel thinning algorithm for 3d digital images this paper is concerned with a new parallel thinning algorithm for three dimensional digital images that preserves the topology and maintains their shape we introduce an approach of selecting shape points and outer layer used for erosion during each iteration the approach produces good skeleton for different types of corners the concept of using two image versions in thinning is introduced and its necessity in parallel thinning is justified the robustness of the algorithm under pseudo random noise as well as rotation with respect to shape properties is studied and the results are found to be satisfactory

translation invariant contourlet transform and its application to image denoising most subsampled filter banks lack the feature of translation invariance which is an important characteristic in denoising applications in this paper we study and develop new methods to convert a general multichannel multidimensional filter bank to a corresponding translation invariant ti framework in particular we propose a generalized algorithme agrave trous which is an extension of the algorithme agrave trous introduced for 1 d wavelet transforms using the proposed algorithm as well as incorporating modified versions of directional filter banks we construct the ti contourlet transform tict to reduce the high redundancy and complexity of the tict we also introduce semi translation invariant contourlet transform stict then we employ an adapted bivariate shrinkage scheme to the stict to achieve an efficient image denoising approach our experimental results demonstrate the benefits and potential of the proposed denoising approach complexity analysis and efficient realization of the proposed ti schemes are also presented

segmentation of textured images using a multiresolution gaussian autoregressive model we present a new algorithm for segmentation of textured images using a multiresolution bayesian approach the new algorithm uses a multiresolution gaussian autoregressive mgar model for the pyramid representation of the observed image and assumes a multiscale markov random field model for the class label pyramid the models used in this paper incorporate correlations between different levels of both the observed image pyramid and the class label pyramid the criterion used for segmentation is the minimization of the expected value of the number of misclassified nodes in the multiresolution lattice the estimate which satisfies this criterion is referred to as the multiresolution maximization of the posterior marginals mmpm estimate and is a natural extension of the single resolution maximization of the posterior marginals mpm estimate previous multiresolution segmentation techniques have been based on the maximum a posterior map estimation criterion which has been shown to be less appropriate for segmentation than the mpm criterion it is assumed that the number of distinct textures in the observed image is known the parameters of the mgar model the means prediction coefficients and prediction error variances of the different textures are unknown a modified version of the expectation maximization em algorithm is used to estimate these parameters the parameters of the gibbs distribution for the label pyramid are assumed to be known experimental results demonstrating the performance of the algorithm are presented

svd based modeling for image texture classification using wavelet transformation this paper introduces a new model for image texture classification based on wavelet transformation and singular value decomposition the probability density function of the singular values of wavelet transformation coefficients of image textures is modeled as an exponential function the model parameter of the exponential function is estimated using maximum likelihood estimation technique truncation of lower singular values is employed to classify textures in the presence of noise kullback leibler distance kld between estimated model parameters of image textures is used as a similarity metric to perform the classification using minimum distance classifier the exponential function permits us to have closed form expressions for the estimate of the model parameter and computation of the kld these closed form expressions reduce the computational complexity of the proposed approach experimental results are presented to demonstrate the effectiveness of this approach on the entire 111 textures from brodatz database the experimental results demonstrate that the proposed approach improves recognition rates using a lower number of parameters on large databases the proposed approach achieves higher recognition rates compared to the traditional subband energy based approach the hybrid imm svm approach and the ggd based approach

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