a new two dimensional block adaptive fir filtering algorithm and its application to image restoration this paper presents a new two dimensional 2 d optimum block stochastic gradient tdobsg algorithm for 2 d adaptive finite impulse response fir filtering the tdobsg algorithm employs a space varying convergence factor for all the filter coefficients where the convergence factor at each block iteration is optimized in a least squares sense that the squared norm of the a posteriori estimation error vector is minimized it has the same order of computational complexity as another 2 d optimum block adaptive tdoba algorithm computer simulations for image restoration show that the tdobsg algorithm outperforms the tdoba algorithm and other related algorithms in terms of objective and or subjective measures

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

sparse feature fidelity for perceptual image quality assessment the prediction of an image quality metric iqm should be consistent with subjective human evaluation as the human visual system hvs is critical to visual perception modeling of the hvs is regarded as the most suitable way to achieve perceptual quality predictions sparse coding that is equivalent to independent component analysis ica can provide a very good description of the receptive fields of simple cells in the primary visual cortex which is the most important part of the hvs with this inspiration a quality metric called sparse feature fidelity sff is proposed for full reference image quality assessment iqa on the basis of transformation of images into sparse representations in the primary visual cortex the proposed method is based on the sparse features that are acquired by a feature detector which is trained on samples of natural images by an ica algorithm in addition two strategies are designed to simulate the properties of the visual perception 1 visual attention and 2 visual threshold the computation of sff has two stages training and fidelity computation in addition the fidelity computation consists of two components feature similarity and luminance correlation the feature similarity measures the structure differences between the two images whereas the luminance correlation evaluates brightness distortions sff also reflects the chromatic properties of the hvs and it is very effective for color iqa the experimental results on five image databases show that sff has a better performance in matching subjective ratings compared with the leading iqms

on the migrativity of triangular subnorms the a t migrative triangular subnorms where t stands for the three prototype triangular norms namely the product the lukasiewicz t norm and the minimum are investigated in detail the paper gives necessary and sufficient conditions under which a triangular subnorm with a continuous additive generator is a t migrative with respect to any of the three prototype triangular norms

comparing images using the hausdorff distance the hausdorff distance measures the extent to which each point of a model set lies near some point of an image set and vice versa thus this distance can be used to determine the degree of resemblance between two objects that are superimposed on one another efficient algorithms for computing the hausdorff distance between all possible relative positions of a binary image and a model are presented the focus is primarily on the case in which the model is only allowed to translate with respect to the image the techniques are extended to rigid motion the hausdorff distance computation differs from many other shape comparison methods in that no correspondence between the model and the image is derived the method is quite tolerant of small position errors such as those that occur with edge detectors and other feature extraction methods it is shown that the method extends naturally to the problem of comparing a portion of a model against an image

a method for registration of 3 d shapes the authors describe a general purpose representation independent method for the accurate and computationally efficient registration of 3 d shapes including free form curves and surfaces the method handles the full six degrees of freedom and is based on the iterative closest point icp algorithm which requires only a procedure to find the closest point on a geometric entity to a given point the icp algorithm always converges monotonically to the nearest local minimum of a mean square distance metric and the rate of convergence is rapid during the first few iterations therefore given an adequate set of initial rotations and translations for a particular class of objects with a certain level of shape complexity one can globally minimize the mean square distance metric over all six degrees of freedom by testing each initial registration one important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model prior to shape inspection experimental results show the capabilities of the registration algorithm on point sets curves and surfaces

image feature localization by multiple hypothesis testing of gabor features several novel and particularly successful object and object category detection and recognition methods based on image features local descriptions of object appearance have recently been proposed the methods are based on a localization of image features and a spatial constellation search over the localized features the accuracy and reliability of the methods depend on the success of both tasks image feature localization and spatial constellation model search in this paper we present an improved algorithm for image feature localization the method is based on complex valued multiresolution gabor features and their ranking using multiple hypothesis testing the algorithm provides very accurate local image features over arbitrary scale and rotation we discuss in detail issues such as selection of filter parameters confidence measure and the magnitude versus complex representation and show on a large test sample how these influence the performance the versatility and accuracy of the method is demonstrated on two profoundly different challenging problems faces and license plates

similarity of scenic bilevel images this paper presents a study of bilevel image similarity including new objective metrics intended to quantify similarity consistent with human perception and a subjective experiment to obtain ground truth for judging the performance of the objective similarity metrics the focus is on scenic bilevel images which are complex natural or hand drawn images such as landscapes or portraits the ground truth was obtained from ratings by 77 subjects of 44 distorted versions of seven scenic images using a modified version of the sdsce testing methodology based on hypotheses about human perception of bilevel images several new metrics are proposed that outperform existing ones in the sense of attaining significantly higher pearson and spearman rank correlation coefficients with respect to the ground truth from the subjective experiment the new metrics include adjusted percentage error bilevel local direction and connected components comparison combinations of these metrics are also proposed which exploit their complementarity to attain even better performance these metrics and the ground truth are then used to assess the relative severity of various kinds of distortion and the performance of several lossy bilevel compression methods

rotation invariant local frequency descriptors for texture classification this paper presents a novel rotation invariant method for texture classification based on local frequency components the local frequency components are computed by applying 1 d fourier transform on a neighboring function defined on a circle of radius r at each pixel we observed that the low frequency components are the major constituents of the circular functions and can effectively represent textures three sets of features are extracted from the low frequency components two based on the phase and one based on the magnitude the proposed features are invariant to rotation and linear changes of illumination moreover by using low frequency components the proposed features are very robust to noise while the proposed method uses a relatively small number of features it outperforms state of the art methods in three well known datasets brodatz outex and curet in addition the proposed method is very robust to noise and can remarkably improve the classification accuracy especially in the presence of high levels of noise

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

structural similarity quality metrics in a coding context exploring the space of realistic distortions perceptual image quality metrics have explicitly accounted for human visual system hvs sensitivity to subband noise by estimating just noticeable distortion jnd thresholds a recently proposed class of quality metrics known as structural similarity metrics ssim models perception implicitly by taking into account the fact that the hvs is adapted for extracting structural information from images we evaluate ssim metrics and compare their performance to traditional approaches in the context of realistic distortions that arise from compression and error concealment in video compression transmission applications in order to better explore this space of distortions we propose models for simulating typical distortions encountered in such applications we compare specific ssim implementations both in the image space and the wavelet domain these include the complex wavelet ssim cwssim a translation insensitive ssim implementation we also propose a perceptually weighted multiscale variant of cwssim which introduces a viewing distance dependence and provides a natural way to unify the structural similarity approach with the traditional jnd based perceptual approaches

scaled and rotated texture classification using a class of basis functions abstract three classes of basis functions are considered for classifying scaled and rotated textured images the first is the orthonormal compactly supported daubechies and the discrete haar bases the second is the biorthogonal basis and the third is the non orthogonal gabor basis textures are scaled and rotated and the basis functions are used to expand them features are computed on a combination of inter resolution coefficients experimental results show that the daubechies orthonormal basis perform well in recognizing transformed textures followed by the haar basis the concept of multiresolution representation and orthogonality are shown to be useful for invariant texture classificaiton

matching 2 5d face scans to 3d models the performance of face recognition systems that use two dimensional images depends on factors such as lighting and subject s pose we are developing a face recognition system that utilizes three dimensional shape information to make the system more robust to arbitrary pose and lighting for each subject a 3d face model is constructed by integrating several 2 5d face scans which are captured from different views 2 5d is a simplified 3d x y z surface representation that contains at most one depth value z direction for every point in the x y plane two different modalities provided by the facial scan namely shape and texture are utilized and integrated for face matching the recognition engine consists of two components surface matching and appearance based matching the surface matching component is based on a modified iterative closest point icp algorithm the candidate list from the gallery used for appearance matching is dynamically generated based on the output of the surface matching component which reduces the complexity of the appearance based matching stage three dimensional models in the gallery are used to synthesize new appearance samples with pose and illumination variations and the synthesized face images are used in discriminant subspace analysis the weighted sum rule is applied to combine the scores given by the two matching components experimental results are given for matching a database of 200 3d face models with 598 2 5d independent test scans acquired under different pose and some lighting and expression changes these results show the feasibility of the proposed matching scheme

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

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

complex wavelet structural similarity a new image similarity index we introduce a new measure of image similarity called the complex wavelet structural similarity cw ssim index and show its applicability as a general purpose image similarity index the key idea behind cw ssim is that certain image distortions lead to consistent phase changes in the local wavelet coefficients and that a consistent phase shift of the coefficients does not change the structural content of the image by conducting four case studies we have demonstrated the superiority of the cw ssim index against other indices e g dice hausdorff distance commonly used for assessing the similarity of a given pair of images in addition we show that the cw ssim index has a number of advantages it is robust to small rotations and translations it provides useful comparisons even without a preprocessing image registration step which is essential for other indices moreover it is computationally less expensive

copulas diagonals and tail dependence we present some known and novel aspects about bivariate copulas with prescribed diagonal section by highlighting their use in the description of the tail dependence moreover we present the tail concentration function which depends on the diagonal section of a copula as a tool to give a description of tail dependence at finite scale the tail concentration function is hence used to introduce a graphical tool that can help to distinguish different families of copulas in the copula test space moreover it serves as a basis to determine the grouping structure of different financial time series by taking into account their pairwise tail behavior

convex combinations of uninorms and triangular subnorms abstract the problem whether a non trivial convex combination of two continuous triangular norms can be a t norm was extensively studied in the last decades bringing the negative answer in all examined special cases on the other hand in the field of non continuous t norms including left continuous t norms several examples were found including mainly a convex combination of an migrative t norm and the drastic product t norm or a convex combination of ordinal sums of product related t subnorms recently convex combinations of uninorms were discussed in 18 since any uninorm with the neutral element e 0 1 restricted to 0 e 2 is isomorphic to a t norm this investigation extends the original problem of a convex combination of t norms given by alsina frank and schweizer in this work we will study convex combinations of uninorms further covering uninorms with continuous underlying functions uninorms with different neutral elements and representable uninorms we will also show that convex combinations of triangular subnorms play a major role in this investigation

lossy cutset coding of bilevel images based on markov random fields an effective low complexity method for lossy compression of scenic bilevel images called lossy cutset coding is proposed based on a markov random field model it operates by losslessly encoding pixels in a square grid of lines which is a cutset with respect to a markov random field model and preserves key structural information such as borders between black and white regions relying on the markov random field model the decoder takes a map approach to reconstructing the interior of each grid block from the pixels on its boundary thereby creating a piecewise smooth image that is consistent with the encoded grid pixels the map rule which reduces to finding the block interiors with fewest black white transitions is directly implementable for the most commonly occurring block boundaries thereby avoiding the need for brute force or iterative solutions experimental results demonstrate that the new method is computationally simple outperforms the current lossy compression technique most suited to scenic bilevel images and provides substantially lower rates than lossless techniques e g jbig with little loss in perceived image quality

lossless near lossless and refinement coding of bilevel images we present general and unified algorithms for lossy lossless coding of bilevel images the compression is realized by applying arithmetic coding to conditional probabilities as in the current jbig standard the conditioning may be specified by a template for better compression the more general free tree may be used loss may be introduced in a preprocess on the encoding side to increase compression the primary algorithm is a rate distortion controlled greedy flipping of pixels though being general the algorithms are primarily aimed at material containing half toned images as a supplement to the specialized soft pattern matching techniques that work better for text template based refinement coding is applied for lossy to lossless refinement introducing only a small amount of loss in half toned test images compression is increased by up to a factor of four compared with jbig lossy lossless and refinement decoding speed and lossless encoding speed are less than a factor of two slower than jbig the de coding method is proposed as part of jbig2 an emerging international standard for lossless lossy compression of bilevel images

some results on the convex combination of uninorms abstract it is well known that under some conditions a convex combination of the drastic product t d and the strict triangular norm is still a triangular norm uninorms as a special kind of generalization of both triangular norms and triangular conorms have been widely used in fuzzy set theory in this paper the convex combination of uninorms is studied some properties of the class of uninorms with the underlying triangular norm t d are presented firstly then the convex combination of this class of uninorms is discussed and it is shown that only in some trivial cases the convex combination is still a uninorm

weighted composition operators from the besov spaces into the weighted type space hμ abstract let denote a positive continuous function on the open unit disk d in this work we characterize the bounded weighted composition operators from the analytic besov spaces b p 1 p into the weighted type space h consisting of the analytic functions on d such that sup z d z f z and determine their operator norms we also determine the essential norm of the bounded weighted composition operators acting on the dirichlet space and obtain explicit estimates when p 1 in the general case when the operator maps b p into h we derive an approximation of the essential norm that yields a characterization of the compact weighted composition operators finally we derive characterizations of the bounded and the compact weighted composition operators from the spaces of antiderivatives of functions in b p and b m o a into the bloch spaces

generalizing the migrativity of continuous t norms this paper generalizes the migrativity of triangular norms t norms for short precisely we find those continuous t norms t which satisfy t l 1x l 2y email protected 3t x y where l email protected 0 1 i 1 2 3 are given it is worth pointing out that this paper provides a new characterization of the product t norm

segmentation by texture using correlation the correlation coefficients are used for segmentation according to texture they are first evaluated on a set of square regions forming two levels of the quadratic picture tree or pyramid if the coefficients of a square and its four children in the tree are similar then that region is considered to be of uniform texture if not it is replaced by its children in this way the split and merge algorithm is used to achieve a preliminary segmentation it is followed by a grouping algorithm using the correlation coefficients and the region adjacency graph plus a small region elimination step the latter regions are grouped according to their gray level because texture cannot be defined reliably on very small regions examples of implementation on four pictures are included

adaptive partially hidden markov models with application to bilevel image coding partially hidden markov models phmms have previously been introduced the transition and emission output probabilities from hidden states as known from the hmms are conditioned on the past this way the hmm may be applied to images introducing the dependencies of the second dimension by conditioning in this paper the phmm is extended to multiple sequences with a multiple token version and adaptive versions of phmm coding are presented the different versions of the phmm are applied to lossless bilevel image coding to reduce and optimize the model cost and size the contexts are organized in trees and effective quantization of the parameters is introduced the new coding methods achieve results that are better than the jbig standard on selected test images although at the cost of increased complexity by the minimum description length principle the methods presented for optimizing the code length may apply as guidance for training p hmms for e g segmentation or recognition purposes thereby the phmm models provide a new approach to image modeling

some observations about the convex combination of continuous triangular norms abstract the convex combination of continuous triangular norms is discussed let c 1 be the class of all nilpotent t norms c 2 be the class of all strict t norms and c 3 be the class of all continuous non archimedean t norms it is shown that for any t norms t 1 t 2 which are taken from two different classes of c i i 1 2 3 the convex combination t 1 t 1 t 2 is not associative for any 0 1 the case of two continuous non archimedean t norms is also investigated by our results the problem of whether or not two continuous t norms can be combined with each other is reduced to that of whether two strict or two nilpotent t norms can be combined with each other or not

stochastic relaxation gibbs distributions and the bayesian restoration of images we make an analogy between images and statistical mechanics systems pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice like physical system the assignment of an energy function in the physical system determines its gibbs distribution because of the gibbs distribution markov random field mrf equivalence this assignment also determines an mrf image model the energy function is a more convenient and natural mechanism for embodying picture attributes than are the local characteristics of the mrf for a range of degradation mechanisms including blurring nonlinear deformations and multiplicative or additive noise the posterior distribution is an mrf with a structure akin to the image model by the analogy the posterior distribution defines another imaginary physical system gradual temperature reduction in the physical system isolates low energy states annealing or what is the same thing the most probable states under the gibbs distribution the analogous operation under the posterior distribution yields the maximum a posteriori map estimate of the image given the degraded observations the result is a highly parallel relaxation algorithm for map estimation we establish convergence properties of the algorithm and we experiment with some simple pictures for which good restorations are obtained at low signal to noise ratios

person re identification by dual regularized kiss metric learning person re identification aims to match the images of pedestrians across different camera views from different locations this is a challenging intelligent video surveillance problem that remains an active area of research due to the need for performance improvement person re identification involves two main steps feature representation and metric learning although the keep it simple and straightforward kiss metric learning method for discriminative distance metric learning has been shown to be effective for the person re identification the estimation of the inverse of a covariance matrix is unstable and indeed may not exist when the training set is small resulting in poor performance here we present dual regularized kiss dr kiss metric learning by regularizing the two covariance matrices dr kiss improves on kiss by reducing overestimation of large eigenvalues of the two estimated covariance matrices and in doing so guarantees that the covariance matrix is irreversible furthermore we provide theoretical analyses for supporting the motivations specifically we first prove why the regularization is necessary then we prove that the proposed method is robust for generalization we conduct extensive experiments on three challenging person re identification datasets viper grid and cuhk 01 and show that dr kiss achieves new state of the art performance

wavelet based texture retrieval using generalized gaussian density and kullback leibler distance we present a statistical view of the texture retrieval problem by combining the two related tasks namely feature extraction fe and similarity measurement sm into a joint modeling and classification scheme we show that using a consistent estimator of texture model parameters for the fe step followed by computing the kullback leibler distance kld between estimated models for the sm step is asymptotically optimal in term of retrieval error probability the statistical scheme leads to a new wavelet based texture retrieval method that is based on the accurate modeling of the marginal distribution of wavelet coefficients using generalized gaussian density ggd and on the existence a closed form for the kld between ggds the proposed method provides greater accuracy and flexibility in capturing texture information while its simplified form has a close resemblance with the existing methods which uses energy distribution in the frequency domain to identify textures experimental results on a database of 640 texture images indicate that the new method significantly improves retrieval rates e g from 65 to 77 compared with traditional approaches while it retains comparable levels of computational complexity

convex combinations of continuous t norms with the same diagonal function abstract the problem of whether a non trivial convex combination of two continuous t norms with the same diagonal function can be a t norm is studied it is shown that in both cases of two nilpotent and of two strict t norms a non trivial convex combination of t norms with common diagonal function is associative only if the two t norms involved coincide for general continuous t norms a similar result follows an example of a convex class of non continuous t norms is also included

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

a parametric texture model based on joint statistics of complex wavelet coefficients we present a universal statistical model for texture images in the context of an overcomplete complex wavelet transform the model is parameterized by a set of statistics computed on pairs of coefficients corresponding to basis functions at adjacent spatial locations orientations and scales we develop an efficient algorithm for synthesizing random images subject to these constraints by iteratively projecting onto the set of images satisfying each constraint and we use this to test the perceptual validity of the model in particular we demonstrate the necessity of subgroups of the parameter set by showing examples of texture synthesis that fail when those parameters are removed from the set we also demonstrate the power of our model by successfully synthesizing examples drawn from a diverse collection of artificial and natural textures

rotation invariant texture classification using feature distributions abstract a distribution based classification approach and a set of recently developed texture measures are applied to rotation invariant texture classification the performance is compared to that obtained with the well known circular symmetric autoregressive random field csar model approach a difficult classification problem of 15 different brodatz textures and seven rotation angles is used in experiments the results show much better performance for our approach than for the csar features a detailed analysis of the confusion matrices and the rotation angles of misclassified samples produces several interesting observations about the classification problem and the features used in this study

operator norms and essential norms of weighted composition operators between banach spaces of analytic functions abstract in this work we characterize the bounded and compact weighted composition operators from a large class of banach space of analytic functions on the open unit disk into weighted banach spaces determine the operator norm exactly and obtain estimates on the essential norm we obtain an exact formula of the essential norm when such operators map the hardy spaces the weighted bergman spaces the bloch space and the little bloch space into the banach space h where the weight is a continuous function in this general setting we also study such operators when the target space is an bloch space for any 0 and more generally any bloch type space as special cases we derive norm and essential norm approximations for the operators acting on the hardy spaces and the weighted bergman spaces immediate corollaries are results on certain integral operators

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

on the statistical analysis of dirty pictures may 7th 1986 professor a f m smith in the chair summary a continuous two dimensional region is partitioned into a fine rectangular array of sites or pixels each pixel having a particular colour belonging to a prescribed finite set the true colouring of the region is unknown but associated with each pixel there is a possibly multivariate record which conveys imperfect information about its colour according to a known statistical model the aim is to reconstruct the true scene with the additional knowledge that pixels close together tend to have the same or similar colours in this paper it is assumed that the local characteristics of the true scene can be represented by a nondegenerate markov random field such information can be combined with the records by bayes theorem and the true scene can be estimated according to standard criteria however the computational burden is enormous and the reconstruction may reflect undesirable largescale properties of the random field thus a simple iterative method of reconstruction is proposed which does not depend on these large scale characteristics the method is illustrated by computer simulations in which the original scene is not directly related to the assumed random field some complications including parameter estimation are discussed potential applications are mentioned briefly

compact composition operators on besov spaces we give a carleson measure characterization of the compact composition operators on besov spaces we use this characterization to show that every compact composition operator on a besov space is compact on the bloch space finally we give conditions that guarantee that the converse holds

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

reduced reference quality assessment based on the entropy of dwt coefficients of locally weighted gradient magnitudes perceptual image quality assessment iqa attempts to use computational models to estimate the image quality in accordance with subjective evaluations reduced reference iqa rriqa methods make use of partial information or features extracted from the reference image for estimating the quality of distorted images finding a balance between the number of rr features and accuracy of the estimated image quality is essential and important in iqa in this paper we propose a training free low cost rriqa method that requires a very small number of rr features six rr features the proposed rriqa algorithm is based on the discrete wavelet transform dwt of locally weighted gradient magnitudes we apply human visual system s contrast sensitivity and neighborhood gradient information to weight the gradient magnitudes in a locally adaptive manner the rr features are computed by measuring the entropy of each dwt subband for each scale and pooling the subband entropies along all orientations resulting in l rr features one average entropy per scale for an l level dwt extensive experiments performed on seven large scale benchmark databases demonstrate that the proposed rriqa method delivers highly competitive performance as compared with the state of the art rriqa models as well as full reference ones for both natural and texture images the matlab source code of redlog and the evaluation results are publicly available online at https http lab engineering asu edu ivulab software redlog

model driven domain adaptation on product manifolds for unconstrained face recognition many classification algorithms see a reduction in performance when tested on data with properties different from that used for training this problem arises very naturally in face recognition where images corresponding to the source domain gallery training data and the target domain probe testing data are acquired under varying degree of factors such as illumination expression blur and alignment in this paper we account for the domain shift by deriving a latent subspace or domain which jointly characterizes the multifactor variations using appropriate image formation models for each factor we formulate the latent domain as a product of grassmann manifolds based on the underlying geometry of the tensor space and perform recognition across domain shift using statistics consistent with the tensor geometry more specifically given a face image from the source or target domain we first synthesize multiple images of that subject under different illuminations blur conditions and 2d perturbations to form a tensor representation of the face the orthogonal matrices obtained from the decomposition of this tensor where each matrix corresponds to a factor variation are used to characterize the subject as a point on a product of grassmann manifolds for cases with only one image per subject in the source domain the identity of target domain faces is estimated using the geodesic distance on product manifolds when multiple images per subject are available an extension of kernel discriminant analysis is developed using a novel kernel based on the projection metric on product spaces furthermore a probabilistic approach to the problem of classifying image sets on product manifolds is introduced we demonstrate the effectiveness of our approach through comprehensive evaluations on constrained and unconstrained face datasets including still images and videos

weighted composition operators from zygmund spaces into bloch spaces abstract let be an analytic self map and u be a fixed analytic function on the open unit disk d in the complex plane c the weighted composition operator is defined by uc f z u z f z z d f h d this paper studies the boundedness and compactness of weighted composition operators from zygmund spaces into bloch spaces

a similarity metric for edge images the performance of several discrepancy measures for the comparison of edge images is analyzed and a novel similarity metric aimed at overcoming their problems is proposed the algorithm finds an optimal matching of the pixels between the images and estimates the error produced by this matching the resulting pixel correspondence metric pcm can take into account edge strength as well as the displacement of edge pixel positions in the estimation of similarity a series of experimental tests shows the new metric to be a robust and effective tool in the comparison of edge images when a small localization error of the detected edges is allowed

associativity of triangular norms characterized by the geometry of their level sets associativity of triangular norms is an algebraic property which unlike for example their commutativity is usually understood as hardly visually interpretable this problem has been studied intensively in the last decade and as a result geometric symmetries of triangular norms with involutive level sets have been revealed the presented paper intends to introduce a different approach which gives more general results the inspiration is taken from web geometry a branch of differential geometry and its concept of reidemeister closure condition which is known to provide a geometric characterization of associativity of loops the paper shows that this concept can be adopted successfully for triangular norms so that it characterizes their associativity in a similar way moreover the offered adaptation preserves the beneficial transparency and simplicity of the reidemeister closure condition this way a visual characterization of the associativity based on the geometry of the level sets is provided for general continuous and continuous archimedean triangular norms

shiftable multiscale transforms one of the major drawbacks of orthogonal wavelet transforms is their lack of translation invariance the content of wavelet subbands is unstable under translations of the input signal wavelet transforms are also unstable with respect to dilations of the input signal and in two dimensions rotations of the input signal the authors formalize these problems by defining a type of translation invariance called shiftability in the spatial domain shiftability corresponds to a lack of aliasing thus the conditions under which the property holds are specified by the sampling theorem shiftability may also be applied in the context of other domains particularly orientation and scale jointly shiftable transforms that are simultaneously shiftable in more than one domain are explored two examples of jointly shiftable transforms are designed and implemented a 1 d transform that is jointly shiftable in position and scale and a 2 d transform that is jointly shiftable in position and orientation the usefulness of these image representations for scale space analysis stereo disparity measurement and image enhancement is demonstrated

analysis of structural similarity in mammograms for detection of bilateral asymmetry we hypothesize that quantification of structural similarity or dissimilarity between paired mammographic regions can be effective in detecting asymmetric signs of breast cancer bilateral masking procedures are applied for this purpose by using automatically detected anatomical landmarks changes in structural information of the extracted regions are investigated using spherical semivariogram descriptors and correlation based structural similarity indices in the spatial and complex wavelet domains the spatial distribution of grayscale values as well as of the magnitude and phase responses of multidirectional gabor filters are used to represent the structure of mammographic density and of the directional components of breast tissue patterns respectively a total of 188 mammograms from the ddsm and mini mias databases consisting of 47 asymmetric cases and 47 normal cases were analyzed for the combined dataset of mammograms areas under the receiver operating characteristic curves of 0 83 0 77 and 0 87 were obtained respectively with linear discriminant analysis the bayesian classifier and an artificial neural network with radial basis functions using the features selected by stepwise logistic regression and leave one patient out cross validation two view analysis provided accuracy up to 0 94 with sensitivity and specificity of 1 and 0 88 respectively

using zernike moments for the illumination and geometry invariant classification of multispectral texture we develop a method for recognizing color texture independent of rotation scale and illumination color texture is modeled using spatial correlation functions defined within and between sensor bands using a linear model for surface spectral reflectance with the same number of parameters as the number of sensor classes we show that illumination and geometry changes in the scene correspond to a linear transformation of the correlation functions and a linear transformation of their coordinates a several step algorithm that includes scale estimation and correlation moment computation is used to achieve the invariance the key to the method is the new result that illumination rotation and scale changes in the scene correspond to a specific transformation of correlation function zernike moment matrices these matrices can be estimated from a color image this relationship is used to derive an efficient algorithm for recognition the algorithm is substantiated using classification results on over 200 images of color textures obtained under various illumination conditions and geometric configurations

some results of weighted quasi arithmetic mean of continuous triangular norms the weighted quasi arithmetic mean of continuous triangular norms is investigated some results on convex combination of continuous triangular norms in ouyang and fang y ouyang j x fang some observations about the convex combination of continuous triangular norms nonlinear anal tma 68 2008 3382 3387 are generalized several open problems are posed

classification and segmentation of rotated and scaled textured images using texture tuned masks abstract a rotation and scale invariant texture classifier function is described for effective classification and segmentation of images involving textures of unknown rotation and scale changes the classifier used is the texture energy associated with a mask that has been tuned to be both discriminant between different textures and to be invariant to rotation and scale changes the mask tuning scheme utilized is based on task oriented criterion optimization via a guided random search procedure to incorporate the changes both a dynamic texture sample set using a two dimensional 2d linked list and a re ranking procedure are applied for training maximum feature dispersion of inter texture classes and high feature convergence of inner texture class samples associated with other statistical measures are suggested as key criteria in training in a study based on 15 distinct brodatz textures it is found that the tuning process although computationally intensive converges efficiently the mean classifier values of the classifier for a particular texture at different orientation and different scales are tightly clustered an objective measure of classification capability is determined by computing the standard deviation of the classifier over pure texture at definite orientation and scale examples are presented of the classifier function applied to the segmentation of collages of brodatz textures comprising regions of various orientation and scale

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

convex combinations of nilpotent triangular norms abstract in this paper we deal with the open problem of convex combinations of continuous triangular norms stated by alsina frank and schweizer c alsina m j frank b schweizer problems on associative functions aequationes math 66 2003 128 140 problems 5 and 6 they pose a question whether a non trivial convex combination of triangular norms can ever be a triangular norm the main result of this paper gives a negative answer to the question for any pair of continuous archimedean triangular norms with different supports with the help of this result we show that a non trivial convex combination of nilpotent t norms is never a t norm the main result also gives an alternative proof to the result presented by ouyang and fang y ouyang j fang some observations about the convex combination of continuous triangular norms nonlinear anal 68 11 2008 3382 3387 theorem 3 1 in proof of the main theorem we utilize the reidmeister condition known from the web geometry

filters random fields and maximum entropy frame towards a unified theory for texture modeling this article presents a statistical theory for texture modeling this theory combines filtering theory and markov random field modeling through the maximum entropy principle and interprets and clarifies many previous concepts and methods for texture analysis and synthesis from a unified point of view our theory characterizes the ensemble of images i with the same texture appearance by a probability distribution f i on a random field and the objective of texture modeling is to make inference about f i given a set of observed texture examples in our theory texture modeling consists of two steps 1 a set of filters is selected from a general filter bank to capture features of the texture these filters are applied to observed texture images and the histograms of the filtered images are extracted these histograms are estimates of the marginal distributions of f i this step is called feature extraction 2 the maximum entropy principle is employed to derive a distribution p i which is restricted to have the same marginal distributions as those in 1 this p i is considered as an estimate of f i this step is called feature fusion a stepwise algorithm is proposed to choose filters from a general filter bank the resulting model called frame filters random fields and maximum entropy is a markov random field mrf model but with a much enriched vocabulary and hence much stronger descriptive ability than the previous mrf models used for texture modeling gibbs sampler is adopted to synthesize texture images by drawing typical samples from p i thus the model is verified by seeing whether the synthesized texture images have similar visual appearances to the texture images being modeled experiments on a variety of 1d and 2d textures are described to illustrate our theory and to show the performance of our algorithms these experiments demonstrate that many textures which are previously considered as from different categories can be modeled and synthesized in a common framework

stability of phase information this paper concerns the robustness of local phase information for measuring image velocity and binocular disparity it addresses the dependence of phase behavior on the initial filters as well as the image variations that exist between different views of a 3d scene we are particularly interested in the stability of phase with respect to geometric deformations and its linearity as a function of spatial position these properties are important to the use of phase information and are shown to depend on the form of the filters as well as their frequency bandwidths phase instabilities are also discussed using the model of phase singularities described by jepson and fleet in addition to phase based methods these results are directly relevant to differential optical flow methods and zero crossing tracking

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

an efficient algorithm for calculating the exact hausdorff distance the hausdorff distance hd between two point sets is a commonly used dissimilarity measure for comparing point sets and image segmentations especially when very large point sets are compared using the hd for example when evaluating magnetic resonance volume segmentations or when the underlying applications are based on time critical tasks like motion detection then the computational complexity of hd algorithms becomes an important issue in this paper we propose a novel efficient algorithm for computing the exact hausdorff distance in a runtime analysis the proposed algorithm is demonstrated to have nearly linear complexity furthermore it has efficient performance for large point set sizes as well as for large grid size performs equally for sparse and dense point sets and finally it is general without restrictions on the characteristics of the point set the proposed algorithm is tested against the hd algorithm of the widely used national library of medicine insight segmentation and registration toolkit itk using magnetic resonance volumes with extremely large size the proposed algorithm outperforms the itk hd algorithm both in speed and memory required in an experiment using trajectories from a road network the proposed algorithm significantly outperforms an hd algorithm based on r trees

two dimensional approach to full reference image quality assessment based on positional structural information a method for full reference visual quality assessment based on the 2 d combination of two diverse metrics is described the first metric is a measure of structural information loss based on the fisher information about the position of the structures in the observed images the second metric acts as a categorical indicator of the type of distortion that images underwent these two metrics constitute the inner state of a virtual cognitive model viewed as a system whose output is the automatic visual quality estimate the use of a 2 d metric fills the intrinsic incompleteness of methods based on a single metric while providing consistent response across different image impairment factors and blind distortion classification capability with a modest computational overhead the high accuracy and robustness of the method are demonstrated through cross validation experiments

on the convex combination of td and continuous triangular norms the problem of when t l 1 l t d lt l 0 1 is a triangular norm where t d is the drastic product and t is a continuous triangular norm is studied it is shown that t l cannot be a triangular norm when t is nilpotent it is also shown that t l is a triangular norm if t is strict and its additive generator f satisfies f lx f x f l for all x 0 1 the cases that t t m and t is the ordinal sum of continuous archimedean summands are also discussed some left continuous t norms which can be combined with each other are given

structural texture similarity metrics for image analysis and retrieval we develop new metrics for texture similarity that accounts for human visual perception and the stochastic nature of textures the metrics rely entirely on local image statistics and allow substantial point by point deviations between textures that according to human judgment are essentially identical the proposed metrics extend the ideas of structural similarity and are guided by research in texture analysis synthesis they are implemented using a steerable filter decomposition and incorporate a concise set of subband statistics computed globally or in sliding windows we conduct systematic tests to investigate metric performance in the context of known item search the retrieval of textures that are identical to the query texture this eliminates the need for cumbersome subjective tests thus enabling comparisons with human performance on a large database our experimental results indicate that the proposed metrics outperform peak signal to noise ratio psnr structural similarity metric ssim and its variations as well as state of the art texture classification metrics using standard statistical measures

unsupervised texture segmentation using markov random field models the problem of unsupervised segmentation of textured images is considered the only explicit assumption made is that the intensity data can be modeled by a gauss markov random field gmrf the image is divided into a number of nonoverlapping regions and the gmrf parameters are computed from each of these regions a simple clustering method is used to merge these regions the parameters of the model estimated from the clustered segments are then used in two different schemes one being all approximation to the maximum a posterior estimate of the labels and the other minimizing the percentage misclassification error the proposed approach is contrasted with the algorithm of s lakshamanan and h derin 1989 which uses a simultaneous parameter estimation and segmentation scheme the results of the adaptive segmentation algorithm of lakshamanan and derin are compared with a simple nearest neighbor classification scheme to show that if enough information is available simple techniques could be used as alternatives to computationally expensive schemes