rotation and gray scale transform invariant texture identification using wavelet decomposition and hidden markov model in this correspondence we have presented a rotation and gray scale transform invariant texture recognition scheme using the combination of quadrature mirror filter qmf bank and hidden markov model hmm in the first stage the qmf bank is used as the wavelet transform to decompose the texture image into subbands the gray scale transform invariant features derived from the statistics based on first order distribution of gray levels are then extracted from each subband image in the second stage the sequence of subbands is modeled as a hidden markov model hmm and one hmm is designed for each class of textures the hmm is used to exploit the dependence among these subbands and is able to capture the trend of changes caused by rotation during recognition the unknown texture is matched against all the models the best matched model identifies the texture class up to 93 33 classification accuracy is reported

two dimensional fourier series based model for nonminimum phase linear shift invariant systems and texture image classification in this paper chi s 1997 1999 real one dimensional 1 d parametric nonminimum phase fourier series based model fsbm is extended to two dimensional 2 d fsbm for a 2 d nonminimum phase linear shift invariant system by using finite 2 d fourier series approximations to its amplitude response and phase response respectively the proposed 2 d fsbm is guaranteed stable and its complex cepstrum can be obtained from its amplitude and phase parameters through a closed form formula without involving complicated 2 d phase unwrapping and polynomial rooting a consistent estimator is proposed for the amplitude estimation of the 2 d fsbm using a 2 d half plane causal minimum phase linear prediction error filter modeled by a 2 d minimum phase fsbm and then two consistent estimators are proposed for the phase estimation of the 2 d fsbm using the chien et al 1997 2 d phase equalizer modeled by a 2 d all pass fsbm the estimated 2 d fsbm can be applied to modeling of 2 d non gaussian random signals and 2 d signal classification using complex cepstra some simulation results are presented to support the efficacy of the three proposed estimators furthermore classification of texture images 2 d non gaussian signals using the estimated fsbm second and higher order statistics is presented together with some experimental results finally we draw some conclusions

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

robust rotation invariant texture classification using a model based approach in this paper a model based texture classification procedure is presented the texture is modeled as the output of a linear system driven by a binary image this latter retains the morphological characteristics of the texture and it is specified by its spatial autocorrelation function acf we show that features extracted from the acf of the binary excitation suffice to represent the texture for classification purposes specifically we employ a moment invariants based technique to classify the acf the resulting proposed classification procedure is thus inherently rotation invariant moreover it is robust with respect to additive noise experimental results show that this approach allows obtaining high correct rotation invariant classification rates while containing the size of the feature space

the incidental parameter problem since 1948 this paper was written to mark the 50th anniversary of neyman and scott s econometrica paper defining the incidental parameter problem it surveys the history both of the paper and of the problem in the statistics and econometrics literature

2 d shape classification using hidden markov model the authors present a planar shape recognition approach based on the hidden markov model and autoregressive parameters this approach segments closed shapes to make classifications at a finer level the algorithm can tolerate a lot of shape contour perturbation and a moderate amount of occlusion an orientation scheme is described to make the overall classification insensitive to shape orientation excellent recognition results have been reported a distinct advantage of the approach is that the classifier does not have to be trained again when a new class of shapes is added

reduced complexity rotation invariant texture classification using a blind deconvolution approach in this paper we present a texture classification procedure that makes use of a blind deconvolution approach specifically the texture is modeled as the output of a linear system driven by a binary excitation we show that features computed from one dimensional slices extracted from the two dimensional autocorrelation function acf of the binary excitation allows representing the texture for rotation invariant classification purposes the two dimensional classification problem is thus reconduced to a more simple one dimensional one which leads to a significant reduction of the classification procedure computational complexity

the vocabulary and grammar of color patterns we determine the basic categories and the hierarchy of rules used by humans in judging similarity and matching of color patterns the categories are 1 overall color 2 directionality and orientation 3 regularity and placement 4 color purity 5 complexity and heaviness these categories form the pattern vocabulary which is governed by the grammar rules both the vocabulary and the grammar were obtained as a result of a subjective experiment experimental data were interpreted using multidimensional scaling techniques yielding the vocabulary and the hierarchical clustering analysis yielding the grammar rules finally we give a short overview of the existing techniques that can be used to extract and measure the elements of the vocabulary

texture classification using the support vector machines in recent years support vector machines svms have demonstrated excellent performance in a variety of pattern recognition problems in this paper we apply svms for texture classification using translation invariant features generated from the discrete wavelet frame transform to alleviate the problem of selecting the right kernel parameter in the svm we use a fusion scheme based on multiple svms each with a different setting of the kernel parameter compared to the traditional bayes classifier and the learning vector quantization algorithm svms and in particular the fused output from multiple svms produce more accurate classification results on the brodatz texture album

object and texture classification using higher order statistics the problem of the detection and classification of deterministic objects and random textures in a noisy scene is discussed an energy detector is developed in the cumulant domain by exploiting the noise insensitivity of higher order statistics an efficient implementation of this detector is described using matched filtering its performance is analyzed using asymptotic distributions in a binary hypothesis testing framework the object and texture discriminant functions are minimum distance classifiers in the cumulant domain and can be efficiently implemented using a bank of matched filters they are immune to additive gaussian noise and insensitive to object shifts important extensions which can handle object rotation and scaling are also discussed an alternative texture classifier is derived from a ml viewpoint and is statistically efficient at the expense of complexity the application of these algorithms to the texture modeling problem is indicated and consistent parameter estimates are obtained

noisy texture classification a higher order statistics approach abstract in this paper a novel texture classification scheme using higher order statistics hos functions as discriminating features is proposed it is well known that such statistical parameters are insensitive to additive gaussian noise in particular third order statistical parameters i e third order cumulants and bispectrum are insensitive to any symmetrically distributed noise and also exhibit the capability of better characterizing non gaussian signals by exploiting these hos properties it is possible to devise a robust method for classifying textures affected by noise with different distributions and even with very low signal to noise ratios

thermodynamics and statistical analysis of gaussian random fields gaussian fields are considered as gibbsian fields thermodynamic functions are calculated for them and the variational principle is proved as an application we get an approximation of log likelihood and an information theoretic interpretation of the asymptotic behaviour of the maximum likelihood estimator for gaussian markov fields

forecasting using principal components from a large number of predictors this article considers forecasting a single time series when there are many predictors n and time series observations t when the data follow an approximate factor model the predictors can be summarized by a small number of indexes which we estimate using principal components feasible forecasts are shown to be asymptotically efficient in the sense that the difference between the feasible forecasts and the infeasible forecasts constructed using the actual values of the factors converges in probability to 0 as both n and t grow large the estimated factors are shown to be consistent even in the presence of time variation in the factor model

a multivariate bayesian model for embryonic growth most longitudinal growth curve models evaluate the evolution of each of the anthropometric measurements separately when applied to a reference population this exercise leads to univariate reference curves against which new individuals can be evaluated however growth should be evaluated in totality that is by evaluating all body characteristics jointly recently cole et al suggested the superimposition by translation and rotation sitar model which expresses individual growth curves by three subject specific parameters indicating their deviation from a flexible overall growth curve this model allows the characterization of normal growth in a flexible though compact manner in this paper we generalize the sitar model in a bayesian way to multiple dimensions the multivariate sitar model allows us to create multivariate reference regions which is advantageous for prediction the usefulness of the model is illustrated on longitudinal measurements of embryonic growth obtained in the first semester of pregnancy collected in the ongoing rotterdam predict study further we demonstrate how the model can be used to find determinants of embryonic growth copyright 2015 john wiley sons ltd

rotation invariant texture classification using a complete space frequency model a method of rotation invariant texture classification based on a complete space frequency model is introduced a polar analytic form of a two dimensional 2 d gabor wavelet is developed and a multiresolution family of these wavelets is used to compute information conserving microfeatures from these microfeatures a micromodel which characterizes spatially localized amplitude frequency and directional behavior of the texture is formed the essential characteristics of a texture sample its macrofeatures are derived from the estimated selected parameters of the micromodel classification of texture samples is based on the macromodel derived from a rotation invariant subset of macrofeatures in experiments comparatively high correct classification rates were obtained using large sample sets

rotation invariant texture classification using lbp variance lbpv with global matching local or global rotation invariant feature extraction has been widely used in texture classification local invariant features e g local binary pattern lbp have the drawback of losing global spatial information while global features preserve little local texture information this paper proposes an alternative hybrid scheme globally rotation invariant matching with locally variant lbp texture features using lbp distribution we first estimate the principal orientations of the texture image and then use them to align lbp histograms the aligned histograms are then in turn used to measure the dissimilarity between images a new texture descriptor lbp variance lbpv is proposed to characterize the local contrast information into the one dimensional lbp histogram lbpv does not need any quantization and it is totally training free to further speed up the proposed matching scheme we propose a method to reduce feature dimensions using distance measurement the experimental results on representative databases show that the proposed lbpv operator and global matching scheme can achieve significant improvement sometimes more than 10 in terms of classification accuracy over traditional locally rotation invariant lbp method

derivative estimation for longitudinal data analysis examining features of blood pressure measured repeatedly during pregnancy estimating velocity and acceleration trajectories allows novel inferences in the field of longitudinal data analysis such as estimating change regions rather than change points and testing group effects on nonlinear change in an outcome ie a nonlinear interaction in this article we develop derivative estimation for 2 standard approaches polynomial mixed models and spline mixed models we compare their performance with an established method principal component analysis through conditional expectation through a simulation study we then apply the methods to repeated blood pressure bp measurements in a uk cohort of pregnant women where the goals of analysis are to i identify and estimate regions of bp change for each individual and ii investigate the association between parity and bp change at the population level the penalized spline mixed model had the lowest bias in our simulation study and we identified evidence for bp change regions in over 75 of pregnant women using mean velocity difference revealed differences in bp change between women in their first pregnancy compared with those who had at least 1 previous pregnancy we recommend the use of penalized spline mixed models for derivative estimation in longitudinal data analysis

multi level repeated measures growth modelling using extended spline functions this paper explores the fitting of multi level models to growth data over a wide age range using a new class of extended spline models these extend conventional splines based on a function representation by allowing variable order functions and by including fractional polynomial terms the work focuses on modelling human growth in height and head circumference with example data sets the procedures can be used with covariates and for comparing population parameters

varying complexity in tree structured image distribution models probabilistic models of image statistics underlie many approaches in image analysis and processing an important class of such models have variables whose dependency graph is a tree if the hidden variables take values on a finite set most computations with the model can be performed exactly including the likelihood calculation training with the em algorithm etc crouse et al developed one such model the hidden markov tree hmt they took particular care to limit the complexity of their model we argue that it is beneficial to allow more complex tree structured models describe the use of information theoretic penalties to choose the model complexity and present experimental results to support these proposals for these experiments we use what we call the hierarchical image probability hip model the differences between the hip and the hmt models include the use of multivariate gaussians to model the distributions of local vectors of wavelet coefficients and the use of different numbers of hidden states at each resolution we demonstrate the broad utility of image distributions by applying the hip model to classification synthesis and compression across a variety of image types namely electrooptical synthetic aperture radar and mammograms digitized x rays in all cases we compare with the hmt

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

bayesian texture classification from indirect observations using fast sampling a bayesian method for texture model choice from blurred and noisy i e indirect observations is presented the textures are modeled by stationary random fields with various distribution laws either gaussian or scale mixtures of gaussians the power spectral densities of the fields are modeled by parametric functions and the aim is to select the most appropriate model among a set of candidates this is achieved by computing the a posteriori model probabilities through parameter marginalization the marginalization is done by sampling and harmonic mean approach considering separately each model in a within model sampling strategy the highly nonlinear dependency with respect to the parameters imposes the use of the metropolis hastings sampler moreover to achieve efficient sampling the paper proposes a new fast algorithm based on the fisher information matrix the fisher metropolis hastings

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

morphologically constrained grfs applications to texture synthesis and analysis a new class of gibbs random fields grfs is proposed capable of modeling geometrical constraints in images by means of mathematical morphology the proposed approach known as morphologically constrained grfs models images by means of their size density since the size density is a multiresolution statistical summary morphologically constrained grfs explicitly incorporate multiresolution information into image modeling important properties are studied and their implication to texture synthesis and analysis is discussed statistical inference can be easily implemented by means of mathematical morphology this allows the design of a computationally simple morphological bayes classifier which produces excellent results in classifying natural textures

log polar wavelet energy signatures for rotation and scale invariant texture classification classification of texture images is important in image analysis and classification this paper proposes an effective scheme for rotation and scale invariant texture classification using log polar wavelet signatures the rotation and scale invariant feature extraction for a given image involves applying a log polar transform to eliminate the rotation and scale effects but at same time produce a row shifted log polar image which is then passed to an adaptive row shift invariant wavelet packet transform to eliminate the row shift effects so the output wavelet coefficients are rotation and scale invariant the adaptive row shift invariant wavelet packet transform is quite efficient with only o n spl middot log n complexity a feature vector of the most dominant log polar wavelet energy signatures extracted from each subband of wavelet coefficients is constructed for rotation and scale invariant texture classification in the experiments we employed a mahalanobis classifier to classify a set of 25 distinct natural textures selected from the brodatz album the experimental results based on different testing data sets for images with different orientations and scales show that the proposed classification scheme using log polar wavelet signatures outperforms two other texture classification methods its overall accuracy rate for joint rotation and scale invariance being 90 8 percent demonstrating that the extracted energy signatures are effective rotation and scale invariant features concerning its robustness to noise the classification scheme also performs better than the other methods

optimal gabor filters for texture segmentation texture segmentation involves subdividing an image into differently textured regions many texture segmentation schemes are based on a filter bank model where the filters called gabor filters are derived from gabor elementary functions the goal is to transform texture differences into detectable filter output discontinuities at texture boundaries by locating these discontinuities one can segment the image into differently textured regions distinct discontinuities occur however only if the gabor filter parameters are suitably chosen some previous analysis has shown how to design filters for discriminating simple textures designing filters for more general natural textures though has largely been done ad hoc we have devised a more rigorously based method for designing gabor filters it assumes that an image contains two different textures and that prototype samples of the textures are given a priori we argue that gabor filter outputs can be modeled as rician random variables often approximated well as gaussian rv s and develop a decision theoretic algorithm for selecting optimal filter parameters to improve segmentations for difficult texture pairs we also propose a multiple filter segmentation scheme motivated by the rician model experimental results indicate that our method is superior to previous methods in providing useful gabor filters for a wide range of texture pairs

maximum likelihood estimation and inference for high dimensional generalized factor models with application to factor augmented regressions abstract this paper reestablishes the main results in bai 2003 and bai and ng 2006 for generalized factor models with slightly stronger conditions on the relative magnitude of n number of subjects and t number of time periods convergence rates of the estimated factor space and loading space and asymptotic normality of the estimated factors and loadings are established under mild conditions that allow for linear logit probit tobit poisson and some other single index nonlinear models the probability density mass function is allowed to vary across subjects and time thus mixed models are also allowed for for factor augmented regressions this paper establishes the limit distributions of the parameter estimates the conditional mean and the forecast when factors estimated from nonlinear mixed data are used as proxies for the true factors

a spatial correlation based method for neighbor set selection in random field image models random field rf models have widespread application in image modeling and analysis the effectiveness of these models is largely dependent on the choice of neighbor sets which determine the spatial interactions that are representable by the model we consider the problem of selecting these neighbor sets for simultaneous autoregressive and gauss markov random field models based on the correlation structure of the image to be modeled a procedure for identifying appropriate neighbor sets is proposed and experimental results which demonstrate the viability of this method are presented

extraction of shift invariant wavelet features for classification of images with different sizes an effective shift invariant wavelet feature extraction method for classification of images with different sizes is proposed the feature extraction process involves a normalization followed by an adaptive shift invariant wavelet packet transform an energy signature is computed for each subband of these invariant wavelet coefficients a reduced subset of energy signatures is selected as the feature vector for classification of images with different sizes experimental results show that the proposed method can achieve high classification accuracy of 98 5 percent and outperforms the other two image classification methods

an adaptive hybrid pattern for noise robust texture analysis local binary patterns lbp achieve great success in texture analysis however they are not robust to noise the two reasons for such disadvantage of lbp schemes are 1 they encode the texture spatial structure based only on local information which is sensitive to noise and 2 they use exact values as the quantization thresholds which make the extracted features sensitive to small changes in the input image in this paper we propose a noise robust adaptive hybrid pattern ahp for noised texture analysis in our scheme two solutions from the perspective of texture description model and quantization algorithm have been developed to reduce the feature s noise sensitiveness first a hybrid texture description model is proposed in this model the global texture spatial structure which is depicted by a global description model is encoded with the primitive microfeature for texture description second we develop an adaptive quantization algorithm in which equal probability quantization is utilized to achieve the maximum partition entropy higher noise tolerance can be obtained with the minimum lost information in the quantization process the experimental results of texture classification on two texture databases with three different types of noise show that our approach leads significant improvement in noised texture analysis furthermore our scheme achieves state of the art performance in noisy face recognition highlightsa hybrid texture description model is proposed for noise robust texture modeling an adaptive quantization algorithm is designed for robust angular space quantization based on the new description model and quantization algorithm we develop the ahp experimental results demonstrate the significant improvement achieved by our scheme

determining the number of factors in approximate factor models in this paper we develop some econometric theory for factor models of large dimensions the focus is the determination of the number of factors r which is an unresolved issue in the rapidly growing literature on multifactor models we first establish the convergence rate for the factor estimates that will allow for consistent estimation of r we then propose some panel criteria and show that the number of factors can be consistently estimated using the criteria the theory is developed under the framework of large cross sections n and large time dimensions t no restriction is imposed on the relation between n and t simulations show that the proposed criteria have good finite sample properties in many configurations of the panel data encountered in practice

a completed modeling of local binary pattern operator for texture classification in this correspondence a completed modeling of the local binary pattern lbp operator is proposed and an associated completed lbp clbp scheme is developed for texture classification a local region is represented by its center pixel and a local difference sign magnitude transform ldsmt the center pixels represent the image gray level and they are converted into a binary code namely clbp center clbp c by global thresholding ldsmt decomposes the image local differences into two complementary components the signs and the magnitudes and two operators namely clbp sign clbp s and clbp magnitude clbp m are proposed to code them the traditional lbp is equivalent to the clbp s part of clbp and we show that clbp s preserves more information of the local structure than clbp m which explains why the simple lbp operator can extract the texture features reasonably well by combining clbp s clbp m and clbp c features into joint or hybrid distributions significant improvement can be made for rotation invariant texture classification

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

individual and time effects in nonlinear panel models with large n t fixed eects estimators of nonlinear panel data models can be severely biased because of the well known incidental parameter problem we develop analytical and jackknife bias corrections for nonlinear models with both individual and time eects under asymptotic sequences where the time dimension t grows with the cross sectional dimension n the time eects introduce additional incidental parameter bias as the existing bias corrections apply to models with only individual eects we derive the appropriate corrections the basis for the corrections are general asymptotic expansions of xed eects estimators with incidental parameters in multiple dimensions we apply the expansions to m estimators with concave objective functions in parameters for panel models with additive individual and time eects these estimators cover xed eects estimators of the most popular limited dependent variable models such as logit probit ordered probit tobit and poisson models our analysis therefore extends the use of large t bias adjustments to an important class of models we also develop bias corrections for functions of the data parameters and individual and time eects including average partial eects in this case the incidental parameter bias is asymptotically of second order in general but the corrections improve nite sample properties

classification of textures using gaussian markov random fields the problem of texture classification arises in several disciplines such as remote sensing computer vision and image analysis in this paper we present two feature extraction methods for the classification of textures using two dimensional 2 d markov random field mrf models it is assumed that the given m m texture is generated by a gaussian mrf model in the first method the least square ls estimates of model parameters are used as features in the second method using the notion of sufficient statistics it is shown that the sample correlations over a symmetric window including the origin are optimal features for classification simple minimum distance classifiers using these two feature sets yield good classification accuracies for a seven class problem

unsupervised texture segmentation using gabor filters abstract this paper presents a texture segmentation algorithm inspired by the multi channel filtering theory for visual information processing in the early stages of human visual system the channels are characterized by a bank of gabor filters that nearly uniformly covers the spatial frequency domain and a systematic filter selection scheme is proposed which is based on reconstruction of the input image from the filtered images texture features are obtained by subjecting each selected filtered image to a nonlinear transformation and computing a measure of energy in a window around each pixel a square error clustering algorithm is then used to integrate the feature images and produce a segmentation a simple procedure to incorporate spatial information in the clustering process is proposed a relative index is used to estimate the true number of texture categories

a new approach for subset 2 d ar model identification for describing textures this paper addresses the problem of identification of appropriate autoregressive ar components to describe textural regions of digital images by a general class of two dimensional 2 d ar models in analogy with univariate time series the proposed technique first selects a neighborhood set of 2 d lag variables corresponding to the significant multiple partial auto correlation coefficients a matrix is then suitably formed from these 2 d lag variables using singular value decomposition svd and orthonormal with column pivoting factorization qrcp techniques the prime information of this matrix corresponding to different pseudoranks is obtained schwarz s 1978 information criterion sig is then used to obtain the optimum set of 2 d lag variables which are the appropriate autoregressive components of the model for a given textural image a four class texture classification scheme is illustrated with such models and a comparison of the technique with the work of chellappa and chatterjee 1985 is provided

classification of rotated and scaled textured images using gaussian markov random field models consideration is given to the problem of classifying a test textured image that is obtained from one of c possible parent texture classes after possibly applying unknown rotation and scale changes to the parent texture the training texture images parent classes are modeled by gaussian markov random fields gmrfs to classify a rotated and scaled test texture the rotation and scale changes are incorporated in the texture model through an appropriate transformation of the power spectral density of the gmrf for the rotated and scaled image a bona fide likelihood function that shows the explicit dependence of the likelihood function on the gmrf parameters as well as on the rotation and scale parameters is derived although in general the scaled and or rotated texture does not correspond to a finite order gmrf it is possible nonetheless to write down a likelihood function for the image data the likelihood function of the discrete fourier transform of the image data corresponds to that of a white nonstationary gaussian random field with the variance at each pixel i j being a known function of the rotation the scale the gmrf model parameters and i j the variance is an explicit function of the appropriately sampled power spectral density of the gmrf the estimation of the rotation and scale parameters is performed in the frequency domain by maximizing the likelihood function associated with the discrete fourier transform of the image data cramer rao error bounds on the scale and rotation estimates are easily computed a modified bayes decision rule is used to classify a given test image into one of c possible texture classes the classification power of the method is demonstrated through experimental results on natural textures from the brodatz album

radon transform orientation estimation for rotation invariant texture analysis this paper presents a new approach to rotation invariant texture classification the proposed approach benefits from the fact that most of the texture patterns either have directionality anisotropic textures or are not with a specific direction isotropic textures the wavelet energy features of the directional textures change significantly when the image is rotated however for the isotropic images the wavelet features are not sensitive to rotation therefore for the directional textures it is essential to calculate the wavelet features along a specific direction in the proposed approach the radon transform is first employed to detect the principal direction of the texture then the texture is rotated to place its principal direction at 0 degrees a wavelet transform is applied to the rotated image to extract texture features this approach provides a features space with small intraclass variability and therefore good separation between different classes the performance of the method is evaluated using three texture sets experimental results show the superiority of the proposed approach compared with some existing methods

texture classification using dominant neighborhood structure this paper proposes a new approach to extract global image features for the purpose of texture classification the proposed texture features are obtained by generating an estimated global map representing the measured intensity similarity between any given image pixel and its surrounding neighbors within a certain window the intensity similarity map is an average representation of the texture image dominant neighborhood similarity the estimated dominant neighborhood similarity is robust to noise and referred to as image dominant neighborhood structure the global rotation invariant features are then extracted from the generated image dominant neighborhood structure features obtained from the local binary patterns lbps are then extracted in order to supply additional local texture features to the generated features from the dominant neighborhood structure both features complement each other the experimental results on representative texture databases show that the proposed method is robust to noise and can achieve significant improvement in terms of the obtained classification accuracy in comparison to the lbp method in addition the method classification accuracy is comparable to the two recent lbp extensions dominant lbp and completed lbp

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

exploring space frequency co occurrences via local quantized patterns for texture representation local binary pattern lbp has shown its power in texture classification and face recognition however the lbp operator is performed in the original image space and it lacks deeper pixel interactions to capture a richer description in this paper we propose to explore space frequency co occurrences via local quantized patterns for texture representation the proposed method proceeds in two channels in each channel the multi resolution spatial maps are first obtained by specific spatial filtering and local frequency features spectral maps are subsequently extracted by applying the local fourier transform to the spatial map two types of quantization via global thresholding are employed to quantize the spatial and spectral maps into three and two levels respectively the quantized patterns are then jointly encoded to construct a space frequency co occurrence histogram finally the two channel histograms are combined to characterize the texture without resort to the texton based representation our method directly encodes the joint information in the space and frequency domains while preserving the robustness to image rotation illumination scale and viewpoint changes extensive experiments have been conducted on three well known texture databases and our method achieves the best classification results compared with state of the art approaches investigated highlightstwo channel space frequency feature spaces are designed to obtain invariant features two types of quantization via global thresholding are developed the joint space frequency coding is explored to capture a richer description our method outperforms the state of the art on three well known texture datasets

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

subband coding of images subband coding has become quite popular for the source encoding of speech this paper presents a simple yet efficient extension of this concept to the source coding of images we specify the constraints for a set of two dimensional quadrature mirror filters qmf s for a particular frequency domain partition and show that these constraints are satisfied by a separable combination of one dimensional qmf s bits are then optimally allocated among the subbands to minimize the mean squared error for dpcm coding of the subbands also an adaptive technique is developed to allocate the bits within each subband by means of a local variance mask optimum quantization is employed with quantizers matched to the laplacian distribution subband coded images are presented along with their signal to noise ratios snr s the snr performance of the subband coder is compared to that of the adaptive discrete cosine transform dct vector quantization and differential vector quantization for bit rates of 0 67 1 0 and 2 0 bits per pixel for 256 256 monochrome images the adaptive subband coder has the best snr performance

the multi state latent factor intensity model for credit rating transitions a new empirical reduced form model for credit rating transitions is introduced it is a parametric intensity based duration model with multiple states and driven by exogenous covariates and latent dynamic factors the model has a generalized semi markov structure designed to accommodate many of the stylized facts of credit rating migrations parameter estimation is based on monte carlo maximum likelihood methods for which the details are discussed in this paper a simulation experiment is carried out to show the effectiveness of the estimation procedure an empirical application is presented for transitions in a 7 grade rating system the model includes a common dynamic component that can be interpreted as the credit cycle asymmetric effects of this cycle across rating grades and additional semi markov dynamics are found to be statistically significant finally we investigate whether the common factor model suffices to capture systematic risk in rating transition data by introducing multiple factors in the model

jackknife and analytical bias reduction for nonlinear panel models fixed effects estimators of panel models can be severely biased because of the well known incidental parameters problem we show that this bias can be reduced by using a panel jackknife or an analytical bias correction motivated by large t we give bias corrections for averages over the fixed effects as well as model parameters we find large bias reductions from using these approaches in examples we consider asymptotics where t grows with n as an approximation to the properties of the estimators in econometric applications we show that if t grows at the same rate as n the fixed effects estimator is asymptotically biased so that asymptotic confidence intervals are incorrect but that they are correct for the panel jackknife we show t growing faster than n 1 3 suffices for correctness of the analytic correction a property we also conjecture for the jackknife

rotation invariant multiresolution texture analysis using radon and wavelet transforms a new rotation invariant texture analysis technique using radon and wavelet transforms is proposed this technique utilizes the radon transform to convert the rotation to translation and then applies a translation invariant wavelet transform to the result to extract texture features a k nearest neighbors classifier is employed to classify texture patterns a method to find the optimal number of projections for the radon transform is proposed it is shown that the extracted features generate an efficient orthogonal feature space it is also shown that the proposed features extract both of the local and directional information of the texture patterns the proposed method is robust to additive white noise as a result of summing pixel values to generate projections in the radon transform step to test and evaluate the method we employed several sets of textures along with different wavelet bases experimental results show the superiority of the proposed method and its robustness to additive white noise in comparison with some recent texture analysis methods

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

multiresolution gray scale and rotation invariant texture classification with local binary patterns presents a theoretically very simple yet efficient multiresolution approach to gray scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions the method is based on recognizing that certain local binary patterns termed uniform are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature we derive a generalized gray scale and rotation invariant operator presentation that allows for detecting the uniform patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis the proposed approach is very robust in terms of gray scale variations since the operator is by definition invariant against any monotonic transformation of the gray scale another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns

multiresolution statistical analysis of high resolution digital mammograms a multiresolution statistical method for identifying clinically normal tissue in digitized mammograms is used to construct an algorithm for separating normal regions from potentially abnormal regions that is small regions that may contain isolated calcifications this is the initial phase of the development of a general method for the automatic recognition of normal mammograms the first step is to decompose the image with a wavelet expansion that yields a sum of independent images each containing different levels of image detail when calcifications are present there is strong empirical evidence that only some of the image components are necessary for the purpose of detecting a deviation from normal the underlying statistic for each of the selected expansion components can be modeled with a simple parametric probability distribution function this function serves as an instrument for the development of a statistical test that allows for the recognition of normal tissue regions the distribution function depends on only one parameter and this parameter itself has an underlying statistical distribution the values of this parameter define a summary statistic that can be used to set detection error rates once the summary statistic is determined spatial filters that are matched to resolution are applied independently to each selected expansion image regions of the image that correlate with the normal statistical model are discarded and regions in disagreement suspicious areas are flagged these results are combined to produce a detection output image consisting only of suspicious areas this type of detection output is amenable to further processing that may ultimately lead to a fully automated algorithm for the identification of normal mammograms

variable duration hidden markov model and morphological segmentation for handwritten word recognition this paper describes a complete system for the recognition of unconstrained handwritten words using a continuous density variable duration hidden markov model cdvdhmm first a new segmentation algorithm based on mathematical morphology is developed to translate the 2 d image into a 1 d sequence of subcharacter symbols this sequence of symbols is modeled by the cdvdhmm thirty five features are selected to represent the character symbols in the feature space generally there are two information sources associated with written text the shape information and the linguistic knowledge while the shape information of each character symbol is modeled as a mixture gaussian distribution the linguistic knowledge i e constraint is modeled as a markov chain the variable duration state is used to take care of the segmentation ambiguity among the consecutive characters a modified viterbi algorithm which provides 2 globally best paths is adapted to vdhmm by incorporating the duration probabilities for the variable duration state sequence the general string editing method is used at the postprocessing stage the detailed experiments are carried out for two postal applications and successful recognition results are reported

local convex and concave pattern aiming at the defect of local binary pattern lbp this paper presents a novel and efficient local second order derivative descriptor for image texture feature extraction based on local convex and concave pattern lccp lccp encodes local directional second order derivative feature of texture based on local convex and concave characteristic which can really capture the derivative information of texture than the center symmetric local derivative pattern cs ldp does the proposed multi resolution lccp not only encodes directional derivative information but also constructs spatial relationships in a given local region by bilinear interpolation extensions of multi resolution lccp based on uniform pattern and weighted pattern have also been discussed the performance of the proposed lccp and its extensions are assessed on the biometric recognition and texture classification under different challenges each image is divided into several regions from which histogram features are extracted and concatenate into an enhanced feature vector for match extensive experiments have validated the effectiveness of lccp and its extensions

learning completed discriminative local features for texture classification we propose a novel feature representation method namely completed discriminative local features cdlf for texture classification the cdlf learn transformation matrices for texture images that maximize the mutual information between the local features and their category labels we propose an adaptive histogram accumulation aha algorithm which leverages the local contrast characteristic in the process of histogram accumulation the cdlf achieves higher accuracy than state of the art methods on three databases local binary patterns lbp and its variants have shown great potentials in texture classification tasks lbp like texture classification methods usually follow a two step feature extraction process in the first pattern encoding step the local structure information around each pixel is encoded into a binary string in the second histogram accumulation step the binary strings are accumulated into a histogram as the feature vector of a texture image the performances of these classification methods are closely related to the distinctiveness of the feature vectors in this paper we propose a novel feature representation method namely completed discriminative local features cdlf for texture classification the proposed cdlf improves the distinctiveness of lbp like feature vectors in two aspects in the pattern encoding stage we learn a transformation matrix using labeled data which significantly increases the discrimination power of the encoded binary strings in the histogram accumulation step we use an adaptive weight strategy to consider the contributions of pixels in different regions the experimental results on three challenging texture databases demonstrate that the proposed cdlf achieves significantly better results than previous lbp like feature representation methods for texture classification tasks

generalized high dimensional trace regression via nuclear norm regularization abstract we study the generalized trace regression with a near low rank regression coefficient matrix which extends notion of sparsity for regression coefficient vectors specifically given a matrix covariate x the probability density function of the response y is f y x c y exp 1 y b where tr t x this model accommodates various types of responses and embraces many important problem setups such as reduced rank regression matrix regression that accommodates a panel of regressors matrix completion among others we estimate through minimizing empirical negative log likelihood plus nuclear norm penalty we first establish a general theory and then for each specific problem we derive explicitly the statistical rate of the proposed estimator they all match the minimax rates in the linear trace regression up to logarithmic factors numerical studies confirm the rates we established and demonstrate the advantage of generalized trace regression over linear trace regression when the response is dichotomous we also show the benefit of incorporating nuclear norm regularization in dynamic stock return prediction and in image classification

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

bayesian segmentation via asymptotic partition functions asymptotic approximations to the partition function of gaussian random fields are derived textures are characterized via gaussian random fields induced by stochastic difference equations determined by finitely supported stationary linear difference operators adjusted to be nonstationary at the boundaries it is shown that as the scale of the underlying shape increases the log normalizer converges to the integral of the log spectrum of the operator inducing the random field fitting the covariance of the fields amounts to fitting the parameters of the spectrum of the differential operator induced random field model matrix analysis techniques are proposed for handling textures with variable orientation examples of texture parameters estimated from training data via asymptotic maximum likelihood are shown isotropic models involving powers of the laplacian and directional models involving partial derivative mixtures are explored parameters are estimated for mitochondria and actin myocin complexes in electron micrographs and clutter in forward looking infrared images deformable template models are used to infer the shape of mitochondria in electron micrographs with the asymptotic approximation allowing easy recomputation of the partition function as inference proceeds

confidence intervals for diffusion index forecasts and inference for factor augmented regressions we consider the situation when there is a large number of series n e ach witht observations and each series has some predictive ability for some variable of interest a methodology of growing interest is first to estimate common factors from the panel of data by the method of principal components and then to augment an otherwise standard regression with the estimated factors in this paper we show that the least squares estimates obtained from these factor augmented regressions are t consistent and asymptotically normal if t n 0 the conditional mean predicted by the estimated factors is min t n consistent and asymptotically normal except when t n goes to zero inference should take into account the effect of estimated regressors on the estimated conditional mean we present analytical formulas for prediction intervals that are valid regardless of the magnitude of n t and that can also be used when the factors are nonstationary

texture classification using windowed fourier filters we define a distance between textures for texture classification from texture features based on windowed fourier filters the definition of the distance relies on an interpretation of our texture attributes in terms of spectral density when the texture can be considered as a gaussian random field the distance between textures is then defined as a symmetrized kullback distance which is a simple function of the attributes and does not require any normalization an experimental analysis using gabor filters and in particular a comparison to quadratic distances shows the efficiency and robustness of the method

multiply rooted multiscale models for large scale estimation divide and conquer or multiscale techniques have become popular for solving large statistical estimation problems the methods rely on defining a state which conditionally decorrelates the large problem into multiple subproblems each more straightforward than the original however this step cannot be carried out for asymptotically large problems since the dimension of the state grows without bound leading to problems of computational complexity and numerical stability in this paper we propose a new approach to hierarchical estimation in which the conditional decorrelation of arbitrarily large regions is avoided and the problem is instead addressed piece by piece the approach possesses promising attributes it is not a local method the estimate at every point is based on all measurements it is numerically stable for problems of arbitrary size and the approach retains the benefits of the multiscale framework on which it is based a broad class of statistical models a stochastic realization theory an algorithm to calculate statistical likelihoods and the ability to fuse local and nonlocal measurements

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

multivariate multilevel spline models for parallel growth processes application to weight and mean arterial pressure in pregnancy growth models are commonly used in life course epidemiology to describe growth trajectories and their determinants or to relate particular patterns of change to later health outcomes however methods to analyse relationships between two or more change processes occurring in parallel in particular to assess evidence for causal influences of change in one variable on subsequent changes in another are less developed we discuss linear spline multilevel models with a multivariate response and show how these can be used to relate rates of change in a particular time period in one variable to later rates of change in another variable by using the variances and covariances of individual level random effects for each of the splines we describe how regression coefficients can be calculated for these associations and how these can be adjusted for other parameters such as random effect variables relating to baseline values or rates of change in earlier time periods and compare different methods for calculating the standard errors of these regression coefficients we also show that these models can equivalently be fitted in the structural equation modelling framework and apply each method to weight and mean arterial pressure changes during pregnancy obtaining similar results for multilevel and structural equation models this method improves on the multivariate linear growth models which have been used previously to model parallel processes because it enables nonlinear patterns of change to be modelled and the temporal sequence of multivariate changes to be determined with adjustment for change in earlier time periods

parameter orthogonality and approximate conditional inference on propose une statistique du rapport de vraisemblance construite a partir de la distribution conditionnelle des observations etant donne les estimateurs du maximum de vraisemblance pour les parametres de nuisance

generalized latent trait models in this paper we discuss a general model framework within which manifest variables with different distributions in the exponential family can be analyzed with a latent trait model a unified maximum likelihood method for estimating the parameters of the generalized latent trait model will be presented we discuss in addition the scoring of individuals on the latent dimensions the general framework presented allows not only the analysis of manifest variables all of one type but also the simultaneous analysis of a collection of variables with different distributions the approach used analyzes the data as they are by making assumptions about the distribution of the manifest variables directly

shape based invariant texture indexing this paper introduces a new texture analysis scheme which is invariant to local geometric and radiometric changes the proposed methodology relies on the topographic map of images obtained from the connected components of level sets this morphological tool providing a multi scale and contrast invariant representation of images is shown to be well suited to texture analysis we first make use of invariant moments to extract geometrical information from the topographic map this yields features that are invariant to local similarities or local affine transformations these features are invariant to any local contrast change we then relax this invariance by computing additional features that are invariant to local affine contrast changes and investigate the resulting analysis scheme by performing classification and retrieval experiments on three texture databases the obtained experimental results outperform the current state of the art in locally invariant texture analysis

brief review of invariant texture analysis methods this paper considers invariant texture analysis texture analysis approaches whose performances are not affected by translation rotation affine and perspective transform are addressed existing invariant texture analysis algorithms are carefully studied and classified into three categories statistical methods model based methods and structural methods the importance of invariant texture analysis is presented first each approach is reviewed according to its classification and its merits and drawbacks are outlined the focus of possible future work is also suggested

frame representations for texture segmentation we introduce a novel method of feature extraction for texture segmentation that relies on multichannel wavelet frames and 2 d envelope detection we describe and compare two algorithms for envelope detection based on 1 the hilbert transform and 2 zero crossings we present criteria for filter selection and discuss quantitatively their effect on feature extraction the performance of our method is demonstrated experimentally on samples of both natural and synthetic textures

texture classification with combined rotation and scale invariant wavelet features in this work a new rotational and scale invariant feature set for textural image classification combined invariant feature cif set has been introduced it is an integration of the crude wavelets like gaussian mexican hat and orthogonal wavelets like daubechies to achieve a high quality rotational and scale invariant feature set also it is added with features obtained using the newly proposed weighted smoothening gaussian filter masks to improve the classification results to reduce the effect of overlapping features the variations among the feature set are analyzed and the eigenfeatures are extracted to produce good classification result the rotational invariance is achieved by using these two wavelets with their directional properties and the scale invariance is achieved by a method which is an extension to fractal dimension fd features the first and second order statistical parameter and entropy characterize the quality of the features extracted furthermore a comparison that shows the higher recognition rate achieved with the newly proposed method for the set of 6720 samples collected from 105 different textures of brodatz vistek indezine databases and some additional images collected from other resources of indexed and true color images is shown

inferential theory for factor models of large dimensions this paper develops an inferential theory for factor models of large dimensions the principal components estimator is considered because it is easy to compute and is asymptotically equivalent to the maximum likelihood estimator if normality is assumed we derive the rate of convergence and the limiting distributions of the estimated factors factor loadings and common components the theory is developed within the framework of large cross sections n and a large time dimension t to which classical factor analysis does not apply we show that the estimated common components are asymptotically normal with a convergence rate equal to the minimum of the square roots of n and t the estimated factors and their loadings are generally normal although not always so the convergence rate of the estimated factors and factor loadings can be faster than that of the estimated common components these results are obtained under general conditions that allow for correlations and heteroskedasticities in both dimensions stronger results are obtained when the idiosyncratic errors are serially uncorrelated and homoskedastic a necessary and sufficient condition for consistency is derived for large n but fixed t

multiaspect target identification with wave based matched pursuits and continuous hidden markov models multiaspect target identification is effected by fusing the features extracted from multiple scattered waveforms these waveforms are characteristic of viewing the target from a sequence of distinct orientations classification is performed in the maximum likelihood sense which we show under reasonable assumptions can be implemented via a hidden markov model hmm we utilize a continuous hmm paradigm and compare its performance to its discrete counterpart the feature parsing is performed via wave based matched pursuits algorithm performance is assessed by considering measured acoustic scattering data from five similar submerged elastic targets

two dimensional linear prediction model based decorrelation method a unified feature extraction scheme the two dimensional 2 d linear prediction model based decorrelation method is presented by applying 2 d causal linear prediction model to decorrelate a textured image the very heavy computation load required when using a whitening operator to decorrelate the image or the significant information loss when using the gradient operator to approximately whiten the image is avoided the texture model based decorrelation provides three sets of features to perform texture classification the coefficients of the 2 d linear prediction the moments of error residuals and the autocorrelation values an optimum feature selection scheme using modified branch and bound method was introduced to reduce information redundancy after feature selection 100 classification accuracy was achieved for a 20 class texture problem experiments show that this feature extraction scheme is truly information lossless effective and fast

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

spatial size distributions applications to shape and texture analysis this paper proposes new descriptors for binary and gray scale images based on newly defined spatial size distributions ssd the main idea consists of combining a granulometric analysis of the image with a comparison between the geometric covariograms for binary images or the auto correlation function for gray scale images of the original image and its granulometric transformation the usual granulometric size distribution then arises as a particular case of this formulation examples are given to show that in those cases in which a finer description of the image is required the more complex descriptors generated from the ssd could be advantageously used it is also shown that the new descriptors are probability distributions so their intuitive interpretation and properties can be appropriately studied from the probabilistic point of view the usefulness of these descriptors in shape analysis is illustrated by some synthetic examples and their use in texture analysis is studied various cases of ssd and several former methods for texture classification are compared

correction to rotation and gray scale transform invariant texture classification using spiral resampling subband decomposition and hidden markov model this paper proposes a new texture classification algorithm that is invariant to rotation and gray scale transformation first we convert two dimensional 2 d texture images to one dimensional 1 d signals by spiral resampling then we use a quadrature mirror filter qmf bank to decompose sampled signals into subbands in each band we take high order autocorrelation functions as features features in different bands which form a vector sequence are then modeled as a hidden markov model bmm during classification the unknown texture is matched against all the models and the best match is taken as the classification result simulations showed that the highest correct classification rate for 16 kinds of texture was 95 14

sufficient forecasting using factor models we consider forecasting a single time series when there is a large number of predictors and a possible nonlinear effect the dimensionality was first reduced via a high dimensional approximate factor model implemented by the principal component analysis using the extracted factors we develop a novel forecasting method called the sufficient forecasting which provides a set of sufficient predictive indices inferred from high dimensional predictors to deliver additional predictive power the projected principal component analysis will be employed to enhance the accuracy of inferred factors when a semi parametric approximate factor model is assumed our method is also applicable to cross sectional sufficient regression using extracted factors the connection between the sufficient forecasting and the deep learning architecture is explicitly stated the sufficient forecasting correctly estimates projection indices of the underlying factors even in the presence of a nonparametric forecasting function the proposed method extends the sufficient dimension reduction to high dimensional regimes by condensing the cross sectional information through factor models we derive asymptotic properties for the estimate of the central subspace spanned by these projection directions as well as the estimates of the sufficient predictive indices we further show that the natural method of running multiple regression of target on estimated factors yields a linear estimate that actually falls into this central subspace our method and theory allow the number of predictors to be larger than the number of observations we finally demonstrate that the sufficient forecasting improves upon the linear forecasting in both simulation studies and an empirical study of forecasting macroeconomic variables

image modeling using inverse filtering criteria with application to textures statistical approaches to image modeling have largely relied upon random models that characterize the 2 d process in terms of its first and second order statistics and therefore cannot completely capture phase properties of random fields that are non gaussian this constrains the parameters of noncausal image models to be symmetric and therefore the underlying random field to be spatially reversible research indicates that this assumption may not be always valid for texture images in this paper higher than second order statistics are used to derive and implement two classes of inverse filtering criteria for parameter estimation of asymmetric noncausal autoregressive moving average arma image models with known orders contrary to existing approaches fir inverse filters are employed and image models with zeros on the unit bicircle can be handled one of the criteria defines the smallest set of cumulant lags necessary for identifiability of these models to date consistency of these estimators is established and their performance is evaluated with monte carlo simulations as well as texture classification and synthesis experiments

two dimensional frequency domain blind system identification using higher order statistics with application to texture synthesis in this paper shalvi and weinstein s 1993 super exponential se algorithm using higher order statistics for blind deconvolution of one dimensional 1 d linear time invariant systems is extended to a two dimensional 2 d se algorithm then a 2 d frequency domain blind system identification bsi algorithm for 2 d linear shift invariant lsi systems using the computationally efficient 2 d se algorithm and the 2 d linear prediction error filter is proposed in addition to the lsi system estimate the proposed bsi algorithm also provides a minimum mean square error mmse equalizer estimate and an mmse signal enhancement filter estimate then a texture synthesis method tsm using the proposed bsi algorithm is presented some simulation results to support the efficacy of the proposed bsi algorithm and some experimental results to support the efficacy of the proposed tsm are presented finally some conclusions are drawn

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