equilibrium paths of mechanical systems with unilateral constraints i theory the paper shows that the behaviour of mechanical systems subject to unilateral constraints differs from that of standard systems in subtle and yet important ways therefore a proper theoretical formulation is required for simulating their behaviour after showing that the equilibrium equations for a multibody system subject to unilateral constraints have the same form as the standard kuhn tucker conditions in optimization theory the first order equilibrium equations are derived and their integration is discussed at a general integration step one has to distinguish between constraints that are strongly active weakly active and inactive whereas strongly active constraints can be treated like bilateral constraints and inactive constraints can be neglected weakly active constraints need to be constantly re analysed to determine if they switch to a different state the outcome is that in addition to the well known limit points and bifurcation points a new type of limit point can exist where the path is non smooth and the first order equilibrium equations after elimination of any strongly active constraints non singular such points are called corner limit points in analogy with common limit points the degree of instability of the system changes by one at a corner limit point

matching pursuits dissimilarity measure for shape based comparison and classification of high dimensional data in this paper a new matching pursuits dissimilarity measure mpdm is presented that compares two signals using the information provided by their matching pursuits mp approximations without requiring any prior domain knowledge mpdm is a flexible and differentiable measure that can be used to perform shape based comparisons and fuzzy clustering of very high dimensional possibly compressed data a novel prototype based classification algorithm which is termed the computer aided minimization procedure camp is also proposed the camp algorithm uses the mpdm with the competitive agglomeration ca fuzzy clustering algorithm to build reliable shape based prototypes for classification mp is a well known sparse signal approximation technique which is commonly used for video and image coding the dictionary and coefficient information produced by mp has previously been used to define features to build discrimination and prototype based classifiers however existing mp based classification applications are quite problem domain specific thus making their generalization to other problems quite difficult the proposed camp algorithm is the first mp based classification system that requires no assumptions about the problem domain and builds a bridge between the mp and fuzzy clustering algorithms experimental results also show that the camp algorithm is more resilient to outliers in test data than the multilayer perceptron mlp and support vector machine svm classifiers as well as prototype based classifiers using the euclidean distance as their dissimilarity measure

a continuation problem for computing solutions of discretised evolution problems with application to plane quasi static contact problems with friction abstract a continuation problem for finding successive solutions of discretised abstract first order evolution problems is proposed and a general piecewise c 1 continuation problem is studied a condition ensuring local existence and uniqueness of its solution curves is given an analogy of the first order system of smooth problems is derived and results of existence and uniqueness of its solutions are stated possibility of continuation of a solution curve along directions solving the first order system is discussed a technique for numerical continuation of the solution curves is developed furthermore an application of the abstract continuation problem is presented for plane quasi static contact problems with friction various formulations of the first order system are derived for this case so that the analysis from the abstract frame can be developed and supplemented finally the proposed numerical continuation is tested on model examples

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

shape optimization in contact problems with coulomb friction the paper deals with a discretized problem of the shape optimization of elastic bodies in unilateral contact the aim is to extend existing results to the case of contact problems following the coulomb friction law mathematical modelling of the coulomb friction problem leads to a quasi variational inequality it is shown that for small coefficients of friction the discretized problem with coulomb friction has a unique solution and that this solution is lipschitzian as a function of a control variable describing the shape of the elastic body the shape optimization problem belongs to a class of so called mathematical programs with equilibrium constraints mpecs the uniqueness of the equilibria for fixed controls enables us to apply the so called implicit programming approach its main idea consists of minimizing a nonsmooth composite function generated by the objective and the single valued control state mapping in this paper the control state mapping is much more complicated than in most mpecs solved in the literature so far and the generalization of the relevant results is by no means straightforward numerical examples illustrate the efficiency and reliability of the suggested approach

gibbs sampling methods for stick breaking priors a rich and flexible class of random probability measures which we call stick breaking priors can be constructed using a sequence of independent beta random variables examples of random measures that have this characterization include the dirichlet process its two parameter extension the two parameter poisson dirichlet process finite dimensional dirichlet priors and beta two parameter processes the rich nature of stick breaking priors offers bayesians a useful class of priors for nonparametric problems while the similar construction used in each prior can be exploited to develop a general computational procedure for fitting them in this article we present two general types of gibbs samplers that can be used to fit posteriors of bayesian hierarchical models based on stick breaking priors the first type of gibbs sampler referred to as a polya urn gibbs sampler is a generalized version of a widely used gibbs sampling method currently employed for dirichlet process computing this method applies t

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

global growth of bandlimited local approximations abstract we obtain explicit estimates showing how the global norm of a band limited function blows up as it locally approximates a function with a jump in a derivative as an application we obtain bounds on how well a function with a jump in a derivative can be essentially time and band limited

hidden markov models with stick breaking priors the number of states in a hidden markov model hmm is an important parameter that has a critical impact on the inferred model bayesian approaches to addressing this issue include the nonparametric hierarchical dirichlet process which does not extend to a variational bayesian vb solution we present a fully conjugate bayesian approach to determining the number of states in a hmm which does have a variational solution the infinite state hmm presented here utilizes a stick breaking construction for each row of the state transition matrix which allows for a sparse utilization of the same subset of observation parameters by all states in addition to our variational solution we discuss retrospective and collapsed gibbs sampling methods for mcmc inference we demonstrate our model on a music recommendation problem containing 2250 pieces of music from the classical jazz and rock genres

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

variational message passing bayesian inference is now widely established as one of the principal foundations for machine learning in practice exact inference is rarely possible and so a variety of approximation techniques have been developed one of the most widely used being a deterministic framework called variational inference in this paper we introduce variational message passing vmp a general purpose algorithm for applying variational inference to bayesian networks like belief propagation vmp proceeds by sending messages between nodes in the network and updating posterior beliefs using local operations at each node each such update increases a lower bound on the log evidence unless already at a local maximum in contrast to belief propagation vmp can be applied to a very general class of conjugate exponential models because it uses a factorised variational approximation furthermore by introducing additional variational parameters vmp can be applied to models containing non conjugate distributions the vmp framework also allows the lower bound to be evaluated and this can be used both for model comparison and for detection of convergence variational message passing has been implemented in the form of a general purpose inference engine called vibes variational inference for bayesian networks which allows models to be specified graphically and then solved variationally without recourse to coding

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

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

efficient mixed spectrum estimation with applications to target feature extraction we present a decoupled parameter estimation dpe algorithm for estimating sinusoidal parameters from both 1 d and 2 d data sequences corrupted by autoregressive ar noise in the first step of the dpe algorithm we use a relaxation relax algorithm that requires simple fast fourier transforms ffts to obtain the estimates of the sinusoidal parameters we describe how the relax algorithm may be used to extract radar target features from both 1 d and 2 d data sequences in the second step of the dpe algorithm a linear least squares approach is used to estimate the ar noise parameters the dpe algorithm is both conceptually and computationally simple the algorithm not only provides excellent estimation performance under the model assumptions in which case the estimates obtained with the dpe algorithm are asymptotically statistically efficient but is also robust to mismodeling errors

globally convergent nonlinear 3d inverse model for smart materials with hessian based optimization abstract a globally convergent and fully coupled 3d inverse model for smart materials is presented in practice stress and field electric magnetic or temperature are applied to smart materials whereas strain and flux density electric magnetic or temperature are measured we refer to constitutive models that follow this scheme as direct models in certain design and control situations however inverse models are necessary in which the field and stress are found from specified flux density and strain this inversion typically involves an iterative procedure which may be prone to convergence issues an inverse model approach is proposed for arbitrary smart materials the inversion requirement is a continuous and second order differentiable direct model for any chosen smart material the approach is globally convergent which makes it ideal for use in finite element frameworks the premise of the proposed iterative system model is to constitute a recursive correction formula based on second order approximations of a novel scalar error function which offers a faster convergence rate a continuation approach is then used to achieve global convergence for arbitrary input parameters magnetostrictive galfenol is chosen to illustrate the effectiveness of the inverse model and compact analytical derivations of the jacobian and hessian matrices are presented the convergence rate of the proposed approach is superior to that of an existing inverse model finally the inverse model s robustness is demonstrated through integration of the model into a finite element framework to simulate a magnetostrictive composite plate actuator in full 3d

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

combining image compression and classification using vector quantization we describe a method of combining classification and compression into a single vector quantizer by incorporating a bayes risk term into the distortion measure used in the quantizer design algorithm once trained the quantizer can operate to minimize the bayes risk weighted distortion measure if there is a model providing the required posterior probabilities or it can operate in a suboptimal fashion by minimizing the squared error only comparisons are made with other vector quantizer based classifiers including the independent design of quantization and minimum bayes risk classification and kohonen s lvq a variety of examples demonstrate that the proposed method can provide classification ability close to or superior to learning vq while simultaneously providing superior compression performance

uncertainty principles for orthonormal sequences abstract the aim of this paper is to provide complementary quantitative extensions of two results of h s shapiro on the time frequency concentration of orthonormal sequences in l 2 r more precisely shapiro proved that if the elements of an orthonormal sequence and their fourier transforms are all pointwise bounded by a fixed function in l 2 r then the sequence is finite in a related result shapiro also proved that if the elements of an orthonormal sequence and their fourier transforms have uniformly bounded means and dispersions then the sequence is finite this paper gives quantitative bounds on the size of the finite orthonormal sequences in shapiro s uncertainty principles the bounds are obtained by using prolate spheroidal wave functions and combinatorial estimates on the number of elements in a spherical code extensions for riesz bases and different measures of time frequency concentration are also given

rate distortion analysis of discrete hmm pose estimation via multiaspect scattering data we consider the problem of estimating the pose of a target based on a sequence of scattered waveforms measured at multiple target sensor orientations using a hidden markov model hmm representation of the scattered waveform sequence pose estimation reduces to estimating the underlying hmm states from a sequence of observations it is assumed that each scattered waveform must be quantized via an encoding procedure a distortion d is defined as the error in estimating the underlying hmm states and the rate r represents the size of the discrete hmm codebook rate distortion theory is applied to define the minimum rate required to achieve a desired distortion denoted as r d after deriving the rate distortion function r d we demonstrate that discrete hmm performance based on lloyd encoding is far from this bound performance is improved via block coding based on bayes vq results are presented for a canonical hmm problem and then for multiaspect acoustic scattering from underwater elastic targets although the examples presented here are for multiaspect scattering and pose estimation the results are of general applicability to discrete hmm state estimation

implementation of a continuation method for normal maps this paper presents an implementation of a nonsmooth continuation method of which the idea was originally put forward by alexander et al we show how the method can be computationally implemented and present numerical results for variational inequality problems in up to 96 variables

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

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

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

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 gtd based parametric model for radar scattering this paper presents a new approach to scattering center extraction based on a scattering model derived from the geometrical theory of diffraction gtd for stepped frequency measurements at high frequencies the model is better matched to the physical scattering process than the damped exponential model and conventional fourier analysis in addition to determining downrange distance energy and polarization the gtd based model extracts frequency dependent scattering information allowing partial identification of scattering center geometry we derive expressions for the cramer rao bound of this model using these expressions we analyze the behavior of the new model as a function of scatterer separation bandwidth number of data points and noise level additionally a maximum likelihood algorithm is developed for estimation of the model parameters we present estimation results using data measured on a compact range to validate the proposed modeling procedure

hierarchical dirichlet processes we consider problems involving groups of data where each observation within a group is a draw from a mixture model and where it is desirable to share mixture components between groups we assume that the number of mixture components is unknown a priori and is to be inferred from the data in this setting it is natural to consider sets of dirichlet processes one for each group where the well known clustering property of the dirichlet process provides a nonparametric prior for the number of mixture components within each group given our desire to tie the mixture models in the various groups we consider a hierarchical model specifically one in which the base measure for the child dirichlet processes is itself distributed according to a dirichlet process such a base measure being discrete the child dirichlet processes necessarily share atoms thus as desired the mixture models in the different groups necessarily share mixture components we discuss representations of hierarchical dirichlet processes

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

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

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

qualitative analysis of solutions to discrete static contact problems with coulomb friction we analyze properties of solutions to discrete contact problems with coulomb friction which are parametrized by the coeffi cient of frictionf using a generalized variant of the implicit function theo rem we establish conditions under which there exists a local lipschitz continuous branch of solutions around a reference point finally a piecewise smooth continuation algorithm which allows to follow such branches of solutions is proposed y have certain knowledge on the qualitative behavior of their solutions which is not available in the continuous case indeed the discrete model has at least one solution for any friction coeffi cientf and this solution is unique iff is small enough on the other hand the bounds onf guaranteeing uniqueness of the solution are mesh dependent in other words the uniqueness property of the discrete problem depends on the size of the prob lem thus it may happen that a small change off leads to a large change of the solution but this phenomenon appears only when suffi ciently fine discretizations are used this catastrophic behavior can be observed in numerical experiments 8 9 the first step towards better understanding of the discrete model is to estab lish the existence of local lipschitz continuous branches of solutions parametrized byf the present paper ex tends results from 10 where f was assumed to be constant to the case when f depends on the spatial variable such results are important for the development of continuation algorithms which is another goal of this paper

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

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

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

the collapsed gibbs sampler in bayesian computations with applications to a gene regulation problem abstract this article describes a method of grouping and collapsing in using the gibbs sampler and proves from an operator theory viewpoint that the method is in general beneficial the norms of the forward operators associated with the corresponding nonreversible markov chains are used to discriminate among different simulation schemes when applied to bayesian missing data problems the idea of collapsing suggests skipping the steps of sampling parameter s values in standard data augmentation by doing this we obtain a predictive update version of the gibbs sampler a procedure of calculating the posterior odds ratio via the collapsed gibbs sampler when incomplete observations are involved is presented as an illustration of possible applications three examples along with a bayesian treatment for identifying common protein binding sites in unaligned dna sequences are provided

multiaspect target detection via the infinite hidden markov model a new multiaspect target detection method is presented based on the infinite hidden markov model ihmm the scattering of waves from a target is modeled as an ihmm with the number of underlying states treated as infinite from which a full posterior distribution on the number of states associated with the targets is inferred and the target dependent states are learned collectively a set of dirichlet processes dps are used to define the rows of the hmm transition matrix and these dps are linked and shared via a hierarchical dirichlet process learning and inference for the ihmm are based on a gibbs sampler the basic framework is applied to a detailed analysis of measured acoustic scattering data

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

target identification with wave based matched pursuits and hidden markov models the method of matched pursuits is an algorithm by which a waveform is parsed into its fundamental constituents here in the context of short pulse electromagnetic scattering wavefronts and resonances constituting what we have called wave based matched pursuits the wave based matched pursuits algorithm is used to develop a codebook of features that are representative of time domain scattering from a target of interest accounting for the variability of such as a function of target sensor orientation this codebook is subsequently used in the context of a hidden markov model hmm in which the probability of measuring a particular codebook element is quantified as a function of target sensor orientation we review the wave based matched pursuits algorithm and its use in the context of an hmm for target identification finally this new wave based signal processing algorithm is demonstrated with simulated scattering data with additive noise

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

bayes risk weighted vector quantization with posterior estimation for image compression and classification classification and compression play important roles in communicating digital information their combination is useful in many applications including the detection of abnormalities in compressed medical images in view of the similarities of compression and low level classification it is not surprising that there are many similar methods for their design because some of these methods are useful for designing vector quantizers it seems natural that vector quantization vq is explored for the combined goal we investigate several vq based algorithms that seek to minimize both the distortion of compressed images and errors in classifying their pixel blocks these algorithms are investigated with both full search and tree structured codes we emphasize a nonparametric technique that minimizes both error measures simultaneously by incorporating a bayes risk component into the distortion measure used for the design and encoding we introduce a tree structured posterior estimator to produce the class posterior probabilities required for the bayes risk computation in this design for two different image sources we demonstrate that this system provides superior classification while maintaining compression close or superior to that of several other vq based designs including kohonen s 1992 learning vector quantizer and a sequential quantizer classifier design

sensitivity analysis of composite piecewise smooth equations this paper is a contribution to the sensitivity analysis of piecewise smooth equations a piecewise smooth function is a lipschitzian homeomorphism near a given point if and only if it is coherently oriented and has an invertible b derivative at this point we emphasise the role of functions of the typef g h whereg is piecewise smooth andh is smooth and present verifiable conditions which ensure that the functionf g tilde h is a lipschitzian homeomorphism near a given point for every tilde h sufficiently close toh with respect to thec1 topology

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 density estimation and inference using mixtures abstract we describe and illustrate bayesian inference in models for density estimation using mixtures of dirichlet processes these models provide natural settings for density estimation and are exemplified by special cases where data are modeled as a sample from mixtures of normal distributions efficient simulation methods are used to approximate various prior posterior and predictive distributions this allows for direct inference on a variety of practical issues including problems of local versus global smoothing uncertainty about density estimates assessment of modality and the inference on the numbers of components also convergence results are established for a general class of normal mixture models

phase space localization theorem for ondelettes it is proven that an orthonormal basis of ondelettes can never have exponential localization in both position space and momentum space

a remark on the uncertainty principle for hilbertian basis on considere le cas l 2 r dx a une dimension soit l 2 r et la transformee de fourier exp ix x dx on demontre que etant donne 1 2 1 2 il y a une base i pour l 2 r qui satisfait x i et i et i pour chaque i

on the convergence of the markov chain simulation method the markov chain simulation method has been successfully used in many problems including some that arise in bayesian statistics we give a self contained proof of the convergence of this method in general state spaces under conditions that are easy to verify

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

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

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

quantization of multiaspect scattering data target classification and pose estimation in many sensing scenarios the observed scattered waveforms must be quantized for subsequent transmission over a communication channel rate distortion theory plays an important role in defining the bit rate required to achieve a desired distortion the distortion is typically defined in the context of signal reconstruction with the goal of achieving high fidelity synthesis of the compressed data for sensing applications however the objective is often not simply signal reconstruction but classification performance as well other related metrics include target pose estimation we consider multiaspect wave scattering in which classification and pose estimation are performed based on the quantized scattering data moreover rate distortion theory is employed to place bounds on pose estimation performance when both the target identity and pose are unknown a priori it is demonstrated that block coding with bayes vq may yield performance approaching the bound example results are presented for measured acoustic waveforms scattered from underwater elastic targets

bayesian nonparametric inference of switching dynamic linear models many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes we consider two such models the switching linear dynamical system slds and the switching vector autoregressive var process our bayesian nonparametric approach utilizes a hierarchical dirichlet process prior to learn an unknown number of persistent smooth dynamical modes we additionally employ automatic relevance determination to infer a sparse set of dynamic dependencies allowing us to learn slds with varying state dimension or switching var processes with varying autoregressive order we develop a sampling algorithm that combines a truncated approximation to the dirichlet process with efficient joint sampling of the mode and state sequences the utility and flexibility of our model are demonstrated on synthetic data sequences of dancing honey bees the ibovespa stock index and a maneuvering target tracking application

clustering by competitive agglomeration we present a new clustering algorithm called competitive agglomeration ca which minimizes an objective function that incorporates the advantages of both hierarchical and partitional clustering the ca algorithm produces a sequence of partitions with a decreasing number of clusters the initial partition has an over specified number of clusters and the final one has the optimal number of clusters the update equation in the ca algorithm creates an environment in which clusters compete for feature points and only clusters with large cardinalities survive the algorithm can incorporate different distance measures in the objective function to f find an unknown number of clusters of various shapes

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

matching pursuit filters applied to face identification we present a face identification algorithm that automatically processes an unknown image by locating and identifying the face the heart of the algorithm is the use of pursuit filters a matching pursuit filter is an adapted wavelet expansion where the expansion is adapted to both the data and the pattern recognition problem being addressed for identification the filters find the features that differentiate among faces whereas for detection the filters encode the similarities among faces the filters are designed though a simultaneous decomposition of a training set into a two dimensional 2 d wavelet expansion this yields a representation that is explicitly 2 d and encodes information locally the algorithm uses coarse to fine processing to locate a small set of key facial features which are restricted to the nose and eye regions of the face the result is an algorithm that is robust to variations in facial expression hair style and the surrounding environment based on the locations of the facial features the identification module searches the data base for the identity of the unknown face using matching pursuit filters to make the identification the algorithm was demonstrated on three sets of images the first set was images from the feret data base the second set was infrared and visible images of the same people this demonstration was done to compare performance on infrared and visible images individually and on fusing the results from both modalities the third set was mugshot data from a law enforcement application

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

retrospective markov chain monte carlo methods for dirichlet process hierarchical models inference for dirichlet process hierarchical models is typically performed using markov chain monte carlo methods which can be roughly categorized into marginal and conditional methods the former integrate out analytically the infinite dimensional component of the hierarchical model and sample from the marginal distribution of the remaining variables using the gibbs sampler conditional methods impute the dirichlet process and update it as a component of the gibbs sampler since this requires imputation of an infinite dimensional process implementation of the conditional method has relied on finite approximations in this paper we show how to avoid such approximations by designing two novel markov chain monte carlo algorithms which sample from the exact posterior distribution of quantities of interest the approximations are avoided by the new technique of retrospective sampling we also show how the algorithms can obtain samples from functionals of the dirichlet process the marginal and the conditional methods are compared and a careful simulation study is included which involves a non conjugate model different datasets and prior specifications

hyperspectral image unmixing using a multiresolution sticky hdp this paper is concerned with joint bayesian endmember extraction and linear unmixing of hyperspectral images using a spatial prior on the abundance vectors we propose a generative model for hyperspectral images in which the abundances are sampled from a dirichlet distribution dd mixture model whose parameters depend on a latent label process the label process is then used to enforces a spatial prior which encourages adjacent pixels to have the same label a gibbs sampling framework is used to generate samples from the posterior distributions of the abundances and the parameters of the dd mixture model the spatial prior that is used is a tree structured sticky hierarchical dirichlet process shdp and when used to determine the posterior endmember and abundance distributions results in a new unmixing algorithm called spatially constrained unmixing scu the directed markov model facilitates the use of scale recursive estimation algorithms and is therefore more computationally efficient as compared to standard markov random field mrf models furthermore the proposed scu algorithm estimates the number of regions in the image in an unsupervised fashion the effectiveness of the proposed scu algorithm is illustrated using synthetic and real data

sparse representation for coarse and fine object recognition this paper offers a sparse multiscale representation of objects it captures the object appearance by selection from a very large dictionary of gaussian differential basis functions the learning procedure results from the matching pursuit algorithm while the recognition is based on polynomial approximation to the bases turning image matching into a problem of polynomial evaluation the method is suited for coarse recognition between objects and by adding more bases also for fine recognition of the object pose the advantages over the common representation using pca include storing sampled points for recognition is not required adding new objects to an existing data set is trivial because retraining other object models is not needed and significantly in the important case where one has to scan an image over multiple locations in search for an object the new representation is readily available as opposed to pca projection at each location the experimental result on the coil 100 data set demonstrates high recognition accuracy with real time performance

generalized dirichlet distribution in bayesian analysis generalized dirichlet distribution has a more general covariance structure than dirichlet distribution this makes the generalized dirichlet distribution to be more practical and useful the concept of complete neutrality will be used to derive the general moment function for the generalized dirichlet distribution and then some properties of the generalized dirichlet distribution will be established similar to the dirichlet distribution the generalized dirichlet distribution will be shown to conjugate to multinominal sampling two experiments are designed for studying the differences between the dirichlet and the generalized dirichlet distributions in bayesian analysis a method for generating samples from a generalized dirichlet in presented when a prior distribution is either a dirichlet or a generalized dirichlet distribution the way for constructing such a prior is discussed