discriminative face alignment this paper proposes a discriminative framework for efficiently aligning images although conventional active appearance models aams based approaches have achieved some success they suffer from the generalization problem i e how to align any image with a generic model we treat the iterative image alignment problem as a process of maximizing the score of a trained two class classifier that is able to distinguish correct alignment positive class from incorrect alignment negative class during the modeling stage given a set of images with ground truth landmarks we train a conventional point distribution model pdm and a boosting based classifier which acts as an appearance model when tested on an image with the initial landmark locations the proposed algorithm iteratively updates the shape parameters of the pdm via the gradient ascent method such that the classification score of the warped image is maximized we use the term boosted appearance models bams to refer to the learned shape and appearance models as well as our specific alignment method the proposed framework is applied to the face alignment problem using extensive experimentation we show that compared to the aam based approach this framework greatly improves the robustness accuracy and efficiency of face alignment by a large margin especially for unseen data

nonrigid registration of 2 d and 3 d dynamic cell nuclei images for improved classification of subcellular particle motion the observed motion of subcellular particles in fluorescence microscopy image sequences of live cells is generally a superposition of the motion and deformation of the cell and the motion of the particles decoupling the two types of movements to enable accurate classification of the particle motion requires the application of registration algorithms we have developed an intensity based approach for nonrigid registration of multichannel microscopy image sequences of cell nuclei first based on 3 d synthetic images we demonstrate that cell nucleus deformations change the observed motion types of particles and that our approach allows to recover the original motion second we have successfully applied our approach to register 2 d and 3 d real microscopy image sequences a quantitative experimental comparison with previous approaches for nonrigid registration of cell microscopy has also been performed

classic and inverse compositional gauss newton in global dic today effective implementations of digital image correlation dic are based on iterative algorithms with constant linear operators a relevant idea of the classic finite element or more generally global dic fe dic solver consists in replacing the gradient of the deformed state image with that of the reference image so as to obtain a constant operator different arguments small strains small deformations equality of the two gradients close to the solution have been given in the literature to justify this approximation but none of them are fully accurate indeed the convergence of the optimization algorithm has to be investigated from its ability to produce descent directions through such a study this paper attempts to explain why this approximation works and what is its domain of validity then an inverse compositional gauss newton icgn implementation of fe dic is proposed as a cost effective and mathematically sound alternative to this approximation

tensor based aam with continuous variation estimation application to variation robust face recognition the active appearance model aam is a well known model that can represent a non rigid object effectively however the fitting result is often unsatisfactory when an input image deviates from the training images due to its fixed shape and appearance model to obtain more robust aam fitting we propose a tensor based aam that can handle a variety of subjects poses expressions and illuminations in the tensor algebra framework which consists of an image tensor and a model tensor the image tensor estimates image variations such as pose expression and illumination of the input image using two different variation estimation techniques discrete and continuous variation estimation the model tensor generates variation specific aam basis vectors from the estimated image variations which leads to more accurate fitting results to validate the usefulness of the tensor based aam we performed variation robust face recognition using the tensor based aam fitting results to do we propose indirect aam feature transformation experimental results show that tensor based aam with continuous variation estimation outperforms that with discrete variation estimation and conventional aam in terms of the average fitting error and the face recognition rate

absolutely continuous spectrum of dirac operators with square integrable potentials we show that the absolutely continuous part of the spectral function of the one dimensional dirac operator on a half line with a constant mass term and a real square integrable potential is strictly increasing throughout the essential spectrum 1 1 the proof is based on estimates for the transmission coefficient for the full line scattering problem with a truncated potential and a subsequent limiting procedure for the spectral function furthermore we show that the absolutely continuous spectrum persists when an angular momentum term is added thus also establishing the result for spherically symmetric dirac operators in higher dimensions

a domain decomposition approach for full field measurements based identification of local elastic parameters abstract we propose a domain decomposition formalism specifically designed for the identification of local elastic parameters based on full field measurements this technique is made possible by a multi scale implementation of the constitutive compatibility method contrary to classical approaches the constitutive compatibility method resolves first some eigenmodes of the stress field over the structure rather than directly trying to recover the material properties a two steps micro macro reconstruction of the stress field is performed a dirichlet identification problem is solved first over every subdomain the macroscopic equilibrium is then ensured between the subdomains in a second step we apply the method to large linear elastic 2d identification problems to efficiently produce estimates of the material properties at a much lower computational cost than classical approaches

a lattice based mrf model for dynamic near regular texture tracking a near regular texture nrt is a geometric and photometric deformation from its regular origin a congruent wallpaper pattern formed by 2d translations of a single tile a dynamic nrt is an nrt under motion although nrts are pervasive in man made and natural environments effective computational algorithms for nrts are few this paper addresses specific computational challenges in modeling and tracking dynamic nrts including ambiguous correspondences occlusions and drastic illumination and appearance variations we propose a lattice based markov random field mrf model for dynamic nrts in a 3d spatiotemporal space our model consists of a global lattice structure that characterizes the topological constraint among multiple textons and an image observation model that handles local geometry and appearance variations based on the proposed mrf model we develop a tracking algorithm that utilizes belief propagation and particle filtering to effectively handle the special challenges of the dynamic nrt tracking without any assumption on the motion types or lighting conditions we provide quantitative evaluations of the proposed method against existing tracking algorithms and demonstrate its applications in video editing

non parametric tests of returns to scale this paper discusses various statistics for testing hypotheses regarding returns to scale in the context of non parametric models of technical efficiency in addition the paper presents bootstrap estimation procedures which yield appropriate critical values for the test statistics evidence on the true sizes and power of the various proposed tests is obtained from monte carlo experiments this paper is an extension of earlier work in manage sci 44 1998 49 j appl statist 27 2000b 779 c 2002 elsevier science b v all rights reserved

on estimation of monotone and concave frontier functions abstract when analyzing the productivity of firms one may want to compare how the firms transform a set of inputs x typically labor energy or capital into an output y typically a quantity of goods produced the economic efficiency of a firm is then defined in terms of its ability to operate close to or on the production frontier the boundary of the production set the frontier function gives the maximal level of output attainable by a firm for a given combination of its inputs the efficiency of a firm may then be estimated via the distance between the attained production level and the optimal level given by the frontier function from a statistical viewpoint the frontier function may be viewed as the upper boundary of the support of the population of firms density in the input and output space it is often reasonable to assume that the production frontier is a concave monotone function then a famous estimator in the univariate input and output case is the data envelopment analysis dea estimato

dynamic affine invariant shape appearance handshape features and classification in sign language videos we propose the novel approach of dynamic affine invariant shape appearance model aff sam and employ it for handshape classification and sign recognition in sign language sl videos aff sam offers a compact and descriptive representation of hand configurations as well as regularized model fitting assisting hand tracking and extracting handshape features we construct sa images representing the hand s shape and appearance without landmark points we model the variation of the images by linear combinations of eigenimages followed by affine transformations accounting for 3d hand pose changes and improving model s compactness we also incorporate static and dynamic handshape priors offering robustness in occlusions which occur often in signing the approach includes an affine signer adaptation component at the visual level without requiring training from scratch a new singer specific model we rather employ a short development data set to adapt the models for a new signer experiments on the boston university 400 continuous sl corpus demonstrate improvements on handshape classification when compared to other feature extraction approaches supplementary evaluations of sign recognition experiments are conducted on a multi signer 100 sign data set from the greek sign language lemmas corpus these explore the fusion with movement cues as well as signer adaptation of aff sam to multiple signers providing promising results

the absolutely continuous spectrum of one dimensional schrodinger operators with decaying potentials we investigate one dimensional schrodinger operators with asymptotically small potentials it will follow from our results that if with then is an essential support of the absolutely continuous part of the spectral measure we also prove that if then the spectrum is purely absolutely continuous on these results are optimal

on subordinacy and analysis of the spectrum of schrodinger operators with two singular endpoints the theory of subordinacy is extended to all one dimensional schrodinger operatorsfor which the corresponding differential expression l d 2 dr 2 v r is in the limit point case at both ends of an interval a b with v r locally integrable this enables a detailed classification of the absolutely continuous and singular spectra to be established in terms of the relative asymptotic behaviour of solutions of lu xu x e as r a and r b the result provides a rigorous but straightforward method of direct spectral analysis which has very general application and somefurther properties of the spectrum are deduced from the underlying theory

global propagation of affine invariant features for robust matching local invariant features have been successfully used in image matching to cope with viewpoint change partial occlusion and clutters however when these factors become too strong there will be a lot of mismatches due to the limited repeatability and discriminative power of features in this paper we present an efficient approach to remove the false matches and propagate the correct ones for the affine invariant features which represent the state of the art local invariance first a pair wise affine consistency measure is proposed to evaluate the consensus of the matches of affine invariant regions the measure takes into account both the keypoint location and the region shape size and orientation based on this measure a geometric filter is then presented which can efficiently remove the outliers from the initial matches and is robust to severe clutters and non rigid deformation to increase the correct matches we propose a global match refinement and propagation method that simultaneously finds a optimal group of local affine transforms to relate the features in two images the global method is capable of producing a quasi dense set of matches even for the weakly textured surfaces that suffer strong rigid transformation or non rigid deformation the strong capability of the proposed method in dealing with significant viewpoint change non rigid deformation and low texture objects is demonstrated in experiments of image matching object recognition and image based rendering

discriminative deep face shape model for facial point detection facial point detection is an active area in computer vision due to its relevance to many applications it is a nontrivial task since facial shapes vary significantly with facial expressions poses or occlusion in this paper we address this problem by proposing a discriminative deep face shape model that is constructed based on an augmented factorized three way restricted boltzmann machines model specifically the discriminative deep model combines the top down information from the embedded face shape patterns and the bottom up measurements from local point detectors in a unified framework in addition along with the model effective algorithms are proposed to perform model learning and to infer the true facial point locations from their measurements based on the discriminative deep face shape model 68 facial points are detected on facial images in both controlled and in the wild conditions experiments on benchmark data sets show the effectiveness of the proposed facial point detection algorithm against state of the art methods

direct estimation of nonrigid registrations with image based self occlusion reasoning the registration problem for images of a deforming surface has been well studied external occlusions are usually well handled in 2d image based registration self occlusions are more challenging consequently the surface is usually assumed to be only slightly self occluding this paper is about image based nonrigid registration with self occlusion reasoning a specific framework explicitly modeling self occlusions is proposed it is combined with an intensity based ldquodirectrdquo data term for registration self occlusions are detected as shrinkage areas in the 2d warp experimental results on several challenging data sets show that our approach successfully registers images with self occlusions while effectively detecting the self occluded regions

graph based construction of textured large field of view mosaics for bladder cancer diagnosis large field of view panoramic images greatly facilitate bladder cancer diagnosis and follow up such 2d mosaics can be obtained by registering the images of a video sequence acquired during cystoscopic examinations the scientific challenge in the registration process lies in the strong inter and intra patient texture variability of the images from which primitives cannot be robustly extracted state of the art registration methods are not at the same time robust and accurate especially for image pairs with a small amount of overlap less than 90 or strong perspective transformations moreover no previous contribution to cystoscopy mosaicing presents panoramic images created from multiple overlapping sequences e g zigzags or loop trajectories we show how such overlapping sections can be automatically detected and present a novel registration algorithm that robustly superimposes non consecutive image pairs which are related by stronger perspective transformations and share less overlap than consecutive images less than 50 globally coherent panoramic images are constructed using a non linear optimization and a novel contrast enhancing stitching method results on both phantom and patient data are obtained using constant algorithm parameters which demonstrate the robustness of the proposed method while the methods presented in this contribution are specifically designed for cystoscopy mosaicing they can also be applied to more general mosaicing problems we demonstrate this on a traditional stitching application where a set of pictures of a building are stitched into a seamless globally coherent panoramic image

estimation and inference in two stage semi parametric models of production processes many papers have regressed non parametric estimates of productive efficiency on environmental variables in two stage procedures to account for exogenous factors that might affect firms performance none of these have described a coherent data generating process dgp moreover conventional approaches to inference employed in these papers are invalid due to complicated unknown serial correlation among the estimated efficiencies we first describe a sensible dgp for such models we propose single and double bootstrap procedures both permit valid inference and the double bootstrap procedure improves statistical efficiency in the second stage regression we examine the statistical performance of our estimators using monte carlo experiments

consistent image registration presents a new method for image registration based on jointly estimating the forward and reverse transformations between two images while constraining these transforms to be inverses of one another this approach produces a consistent set of transformations that have less pairwise registration error i e better correspondence than traditional methods that estimate the forward and reverse transformations independently the transformations are estimated iteratively and are restricted to preserve topology by constraining them to obey the laws of continuum mechanics the transformations are parameterized by a fourier series to diagonalize the covariance structure imposed by the continuum mechanics constraints and to provide a computationally efficient numerical implementation results using a linear elastic material constraint are presented using both magnetic resonance and x ray computed tomography image data the results show that the joint estimation of a consistent set of forward and reverse transformations constrained by linear elasticity give better registration results than using either constraint alone or none at all

statistical shape analysis clustering learning and testing using a differential geometric treatment of planar shapes we present tools for 1 hierarchical clustering of imaged objects according to the shapes of their boundaries 2 learning of probability models for clusters of shapes and 3 testing of newly observed shapes under competing probability models clustering at any level of hierarchy is performed using a minimum variance type criterion and a markov process statistical means of clusters provide shapes to be clustered at the next higher level thus building a hierarchy of shapes using finite dimensional approximations of spaces tangent to the shape space at sample means we implicitly impose probability models on the shape space and results are illustrated via random sampling and classification hypothesis testing together hierarchical clustering and hypothesis testing provide an efficient framework for shape retrieval examples are presented using shapes and images from eth surrey and amcom databases

dense point spectrum for the one dimensional dirac operator with an electrostatic potential for the one dimensional dirac operator examples of electrostatic potentials with decay behaviour arbitrarily close to coulomb decay are constructed for which the operator has a prescribed set of eigenvalues dense in the whole or part of its essential spectrum a simple proof that the essential spectrum of one dimensional dirac operators with electrostatic potentials is never empty is given in the appendix

anisotropic motion estimation on edge preserving riesz wavelets for robust video mosaicing image mosaicing is a technique widely used for extending the field of view of industrial medical outdoor or indoor scenes however image registration can be very challenging e g due to large texture variability illumination changes image blur and camera perspective changes in this paper a total variational optical flow approach is investigated to estimate dense point correspondences between image pairs an edge preserving riesz wavelet scale space combined with a novel tv regularizer is proposed for preserving motion discontinuities along the edges of weak textures and for handling strong in plane rotations present in image sequences an anisotropic weighted median filtering is implemented for minimizing outliers quantitative evaluation of the method on the middlebury image database and simulated sequences with known ground truth demonstrates high accuracy of the proposed method in comparison with other state of the art methods including a robust graph cut method and a patch matching approach qualitative results on video sequences of difficult real scenes demonstrate the robustness of the proposed method highlightsmosaicing of images with strong texture and illumination variability fast robust and accurate optical flow computation tv l1 method on a second order riesz wavelet basis for preserving texture discontinuities novel anisotropic tv regularizer accurate results for both the middlebury data set and various complicated scenes

global 2d digital image correlation for motion estimation in a finite element framework a variational formulation and a regularized pyramidal multi grid implementation in this study the inverse problem of reconstructing the in plane 2d displacements of a monitored surface through a sequence of two dimensional digital images severely ill posed in hadamard s sense is deeply investigated a novel variational formulation is presented for the continuum 2d digital image correlation problem and critical issues such as semi coercivity and solution multiplicity are discussed by functional analysis tools in the framework of a galerkin finite element discretization of the displacement field a robust implementation for 2d digital image correlation is outlined aiming to attenuate the spurious oscillations which corrupt the deformation scenario especially when very fine meshes are adopted recourse is made to a hierarchical family of grids linked by suitable restriction and prolongation operators and defined over an image pyramid multi grid cycles are performed ascending and descending along the pyramid with only one newton iteration per level irrespective of the tolerance satisfaction as if the problem were linear at each level the conventional least square matching functional is herein enriched by a tychonoff regularization provision preserving the solution against an unstable response the algorithm is assessed on the basis of both synthetic and truly experimental image pairs copyright 2013 john wiley sons ltd

transformation invariant subspace clustering subspace clustering has achieved great success in many computer vision applications however most subspace clustering algorithms require well aligned data samples which is often not straightforward to achieve this paper proposes a transformation invariant subspace clustering framework by jointly aligning data samples and learning subspace representation by alignment the transformed data samples become highly correlated and a better affinity matrix can be obtained the joint problem can be reduced to a sequence of least squares regression problems which can be efficiently solved we verify the effectiveness of the proposed method with extensive experiments on unaligned real data demonstrating its higher clustering accuracy than the state of the art subspace clustering and transformation invariant clustering algorithms highlightsa transformation invariant subspace clustering algorithm is proposed alignment and subspace clustering are mutually dependent state of the art clustering results on the unconstrained face dataset

metric learning for image alignment image alignment has been a long standing problem in computer vision parameterized appearance models pams such as the lucas kanade method eigentracking and active appearance models are commonly used to align images with respect to a template or to a previously learned model while pams have numerous advantages relative to alternate approaches they have at least two drawbacks first they are especially prone to local minima in the registration process second often few if any of the local minima of the cost function correspond to acceptable solutions to overcome these problems this paper proposes a method to learn a metric for pams that explicitly optimizes that local minima occur at and only at the places corresponding to the correct fitting parameters to the best of our knowledge this is the first paper to address the problem of learning a metric to explicitly model local properties of the pams error surface synthetic and real examples show improvement in alignment performance in comparison with traditional approaches in addition we show how the proposed criteria for a good metric can be used to select good features to track

active appearance models revisited active appearance models aams and the closely related concepts of morphable models and active blobs are generative models of a certain visual phenomenon although linear in both shape and appearance overall aams are nonlinear parametric models in terms of the pixel intensities fitting an aam to an image consists of minimising the error between the input image and the closest model instances i e solving a nonlinear optimisation problem we propose an efficient fitting algorithm for aams based on the inverse compositional image alignment algorithm we show that the effects of appearance variation during fitting can be precomputed projected out using this algorithm and how it can be extended to include a global shape normalising warp typically a 2d similarity transformation we evaluate our algorithm to determine which of its novel aspects improve aam fitting performance

on subordinacy and analysis of the spectrum of one dimensional schrodinger operators abstract by considering the behaviour as n of the ratio of l2 0 n norms of solutions of d 2 dr 2 v r u xu 0 r x r a characterisation of the absolutely continuous and singular spectra of one dimensional schrodinger operators is deduced the analysis is applicable to all operators for which l d 2 dr 2 v r is regular at 0 and in the limit point case at infinity with v r locally integrable

parametric image alignment using enhanced correlation coefficient maximization in this work we propose the use of a modified version of the correlation coefficient as a performance criterion for the image alignment problem the proposed modification has the desirable characteristic of being invariant with respect to photometric distortions since the resulting similarity measure is a nonlinear function of the warp parameters we develop two iterative schemes for its maximization one based on the forward additive approach and the second on the inverse compositional method as it is customary in iterative optimization in each iteration the nonlinear objective function is approximated by an alternative expression for which the corresponding optimization is simple in our case we propose an efficient approximation that leads to a closed form solution per iteration which is of low computational complexity the latter property being particularly strong in our inverse version the proposed schemes are tested against the forward additive lucas kanade and the simultaneous inverse compositional algorithm through simulations under noisy conditions and photometric distortions our forward version achieves more accurate alignments and exhibits faster convergence whereas our inverse version has similar performance as the simultaneous inverse compositional algorithm but at a lower computational complexity

simultaneous registration of multiple images similarity metrics and efficient optimization we address the alignment of a group of images with simultaneous registration therefore we provide further insights into a recently introduced framework for multivariate similarity measures referred to as accumulated pair wise estimates ape and derive efficient optimization methods for it more specifically we show a strict mathematical deduction of ape from a maximum likelihood framework and establish a connection to the congealing framework this is only possible after an extension of the congealing framework with neighborhood information moreover we address the increased computational complexity of simultaneous registration by deriving efficient gradient based optimization strategies for ape gauss newton and the efficient second order minimization esm we present next to ssd the usage of intrinsically nonsquared similarity measures in this least squares optimization framework the fundamental assumption of esm the approximation of the perfectly aligned moving image through the fixed image limits its application to monomodal registration we therefore incorporate recently proposed structural representations of images which allow us to perform multimodal registration with esm finally we evaluate the performance of the optimization strategies with respect to the similarity measures leading to very good results for esm the extension to multimodal registration is in this context very interesting because it offers further possibilities for evaluations due to publicly available datasets with ground truth alignment

one dimensional mapping for estimating projective transformations in this paper the 1 d mapping is studied for estimating a general projective transformation between images first a ray model of image patches is proposed to convert a complex projective deformation of a patch in the cartesian space into two simple rigid shifts of radial lines in the log polar inverse polar space to capture these shifts via direct search two types of radial image line matching viz 1 d log polar mapping and 1 d inverse polar mapping are formally defined also the unique features of these shifts are analyzed then a novel two step mathematical framework is devised for transformation estimation in the first step the 1 d log polar mapping is applied to estimating four affine parameters via two alternative methods for simulated real images in the second step the 1 d inverse polar mapping is applied to estimating two projective parameters by a direct search combined with the two step line matching two translational parameters are also determined finally the performance of the framework is evaluated first in estimating a variety of projective transformations by comparing the estimates with their ground truths and between the two proposed methods and second in registering a wide range of challenging image pairs by comparing with a state of the art feature based method

nonparametric frontier estimation a robust approach most nonparametric methods for estimating production frontiers data envelopment analysis and free disposal hall fdh are based on envelopment techniques statistical inference based on these estimators is available but by construction they are very sensitive to extreme values or outliers we propose a nonparametric estimator which is more robust to these extreme values it is based on a concept of expected minimum input function or expected maximal output function we show how this function is related to the efficient frontier itself the resulting estimator is related to the fdh estimator but it will not envelop all the data the asymptotic theory is provided our approach includes the multiple input and multiple output cases c 2002 elsevier science s a all rights reserved

feature based lucas kanade and active appearance models lucas kanade and active appearance models are among the most commonly used methods for image alignment and facial fitting respectively they both utilize nonlinear gradient descent which is usually applied on intensity values in this paper we propose the employment of highly descriptive densely sampled image features for both problems we show that the strategy of warping the multichannel dense feature image at each iteration is more beneficial than extracting features after warping the intensity image at each iteration motivated by this observation we demonstrate robust and accurate alignment and fitting performance using a variety of powerful feature descriptors especially with the employment of histograms of oriented gradient and scale invariant feature transform features our method significantly outperforms the current state of the art results on in the wild databases

comparing efficiency across markets an extension and critique of the zhang and bartels 1998 methodology abstract the use of non parametric frontier methods for the evaluation of product market efficiency in heterogeneous markets seems to have gained some popularity recently however the statistical properties of these frontier estimators have been largely ignored the main point is that non parametric frontier estimators are biased and that the degree of bias depends on specific sample properties most importantly sample size and number of dimensions of the model to investigate the effect of this bias on comparing market efficiency this contribution estimates the efficiency for several datasets for two main product categories following zhang y bartels r 1998 the effect of sample size on the mean efficiency in dea with an application to electricity distribution in australia sweden and new zealand journal of productivity analysis 9 3 187 204 these results comprise re estimates for the larger samples limiting their size to that of the smaller samples when the model dimensions for different samples are identical furthermore sample sizes are adjusted to mitigate the eventual differences in dimensions in specification this allows comparing market efficiency for different markets on a more equal footing since it reduces the bias effect to a minimum making the comparison of market efficiency possible however the article also points out the critical limitations of this zhang y bartels r 1998 the effect of sample size on the mean efficiency in dea with an application to electricity distribution in australia sweden and new zealand journal of productivity analysis 9 3 187 204 approach in certain respects apart from reporting these negative results we also offer some suggestions for future work

value distribution and spectral analysis of differential operators the theory of value distribution is applied through a study of boundary value distribution for the weyl titchmarsh m function to the spectral analysis of differential operators on the half line particular consequences are the theoretical underpinning of a numerical approach to spectral analysis of differential operators exhibiting absolutely continuous or singular spectra and new identities for the spectral measure in terms of solutions of the associated differential equation

a unified gradient based approach for combining asm into aam active appearance model aam framework is a very useful method that can fit the shape and appearance model to the input image for various image analysis and synthesis problems however since the goal of the aam fitting algorithm is to minimize the residual error between the model appearance and the input image it often fails to accurately converge to the landmark points of the input image to alleviate this weakness we have combined active shape models asm into aams in which asms try to find correct landmark points using the local profile model since the original objective function of the asm search is not appropriate for combining these methods we derive a gradient based iterative method by modifying the objective function of the asm search then we propose a new fitting method that combines the objective functions of both asm and aam into a single objective function in a gradient based optimization framework experimental results show that the proposed fitting method reduces the average fitting error when compared with existing fitting methods such as asm aam and texture constrained asm tc asm and improves the performance of facial expression recognition significantly

a smooth nonparametric conditional quantile frontier estimator abstract traditional estimators for nonparametric frontier models dea fdh are very sensitive to extreme values outliers recently aragon et al 2005 nonparametric frontier estimation a conditional quantile based approach econometric theory 21 358 389 proposed a nonparametric frontier model and estimator based on a suitably defined conditional quantile which is more robust to extreme values outliers their estimator is based on a nonsmooth empirical conditional distribution in this paper we propose a new smooth nonparametric conditional quantile estimator for the frontier model our estimator is a kernel based conditional quantile estimator that builds on early work of azzalini 1981 a note on the estimation of a distribution function and quantiles by a kernel method biometrika 68 326 328 it is computationally simple resistant to outliers and extreme values and smooth in addition the estimator is shown to be consistent and n asymptotically normal under mild regularity conditions we also show that our estimator s variance is smaller than that of the estimator proposed by aragon et al a simulation study confirms the asymptotic theory predictions and contrasts our estimator with that of aragon et al

eigentracking robust matching and tracking of articulated objects using a view based representation this paper describes an approach for tracking rigid and articulated objects using a view based representation the approach builds on and extends work on eigenspace representations robust estimation techniques and parameterized optical flow estimation first we note that the least squares image reconstruction of standard eigenspace techniques has a number of problems and we reformulate the reconstruction problem as one of robust estimation second we define a subspace constancy assumption that allows us to exploit techniques for parameterized optical flow estimation to simultaneously solve for the view of an object and the affine transformation between the eigenspace and the image to account for large affine transformations between the eigenspace and the image we define a multi scale eigenspace representation and a coarse to fine matching strategy finally we use these techniques to track objects over long image sequences in which the objects simultaneously undergo both affine image motions and changes of view in particular we use this eigentracking technique to track and recognize the gestures of a moving hand

dynamically removing false features in pyramidal lucas kanade registration pyramidal lucas kanade lk optical flow is a real time registration technique widely employed by a variety of cutting edge consumer applications traditionally the lk algorithm is applied selectively to image feature points that have strong spatial variation which include outliers in textured areas to detect and discard the falsely selected features previous methods generally assess the goodness of each feature after the flow computation is completed such a screening process incurs additional cost this paper provides a handy but not obvious tool for the users of the lk algorithm to remove false features without degrading the algorithm s efficiency we propose a confidence predictor which evaluates the ill posedness of an lk system directly from the underlying data at a cost lower than solving the system we then incorporate our confidence predictor into the course to fine lk registration to dynamically detect false features and terminate their flow computation at an early stage this improves the registration accuracy by preventing the error propagation and maintains or increases the computation speed by saving the runtime on false features experimental results on state of the art benchmarks validate that our method is more accurate and efficient than related works

natural facial expression recognition using differential aam and manifold learning this paper proposes a novel natural facial expression recognition method that recognizes a sequence of dynamic facial expression images using the differential active appearance model aam and manifold learning as follows first the differential aam features dafs are computed by the difference of the aam parameters between an input face image and a reference neutral expression face image second manifold learning embeds the dafs on the smooth and continuous feature space third the input facial expression is recognized through two steps 1 computing the distances between the input image sequence and gallery image sequences using directed hausdorff distance dhd and 2 selecting the expression by a majority voting of k nearest neighbors k nn sequences in the gallery the dafs are robust and efficient for the facial expression analysis due to the elimination of the inter person camera and illumination variations since the dafs treat the neutral expression image as the reference image the neutral expression image must be found effectively this is done via the differential facial expression probability density model dfepdm using the kernel density approximation of the positively directional dafs changing from neutral to angry happy surprised and negatively directional dafs changing from angry happy surprised to neutral then a face image is considered to be the neutral expression if it has the maximum dfepdm in the input sequences experimental results show that 1 the dafs improve the facial expression recognition performance over conventional aam features by 20 and 2 the sequence based k nn classifier provides a 95 facial expression recognition performance on the facial expression database fed06

modified prufer and efgp transforms and the spectral analysis of one dimensional schrodinger operators using control of the growth of the transfer matrices wediscuss the spectral analysis of continuum and discrete half line schrodinger operators with slowly decaying potentials among our results we show if where w has compact support and then h has purely a c resp purely s c spectrum on 0 if for n 1 2 a n potentials where a n are independent identically distributed random variables with e a n 0 e a 2 n 1 and 2 we find singular continuous spectrum with explicitly computable fractional hausdorff dimension

lucas kanade 20 years on a unifying framework since the lucas kanade algorithm was proposed in 1981 image alignment has become one of the most widely used techniques in computer vision applications range from optical flow and tracking to layered motion mosaic construction and face coding numerous algorithms have been proposed and a wide variety of extensions have been made to the original formulation we present an overview of image alignment describing most of the algorithms and their extensions in a consistent framework we concentrate on the inverse compositional algorithm an efficient algorithm that we recently proposed we examine which of the extensions to lucas kanade can be used with the inverse compositional algorithm without any significant loss of efficiency and which cannot in this paper part 1 in a series of papers we cover the quantity approximated the warp update rule and the gradient descent approximation in future papers we will cover the choice of the error function how to allow linear appearance variation and how to impose priors on the parameters

shape appearance correlated active appearance model among the challenges faced by current active shape or appearance models facial feature localization in the wild with occlusion in a novel face image i e in a generic environment is regarded as one of the most difficult computer vision tasks in this paper we propose an active appearance model aam to tackle the problem of generic environment firstly a fast face model initialization scheme is proposed based on the idea that the local appearance of feature points can be accurately approximated with locality constraints nearest neighbors which have similar poses and textures to a test face are retrieved from a training set for constructing the initial face model to further improve the fitting of the initial model to the test face an orthogonal cca occa is employed to increase the correlation between shape features and appearance features represented by principal component analysis pca with these two contributions we propose a novel aam namely the shape appearance correlated aam sac aam and the optimization is solved by using the recently proposed fast simultaneous inverse compositional fast sic algorithm experiment results demonstrate a 5 10 improvement on controlled and semi controlled datasets and with around 10 improvement on wild face datasets in terms of fitting accuracy compared to other state of the art aam models a fast face model initialization scheme is proposed for aams orthogonal cca is employed to increase the correlation between shape and appearance a shape appearance correlated aam is proposed to tackle the generic problem of localizing feature points on unseen faces

on the absolutely continuous spectrum of one dimensional schrodinger operators with square summable potentials for continuous and discrete one dimensional schrodinger operators with square summable potentials the absolutely continuous part of the spectrum is essentially supported by 0 and 2 2 respectively this fact is proved by considering a priori estimates for the transmission coefficient

the impact of outliers and leverage points for technical efficiency measurement using high breakdown procedures given that most data used for production studies have not been accumulated for such purposes it is important that the quantitative tools for messy data which can affect the accuracy of computed technical efficiency measures be found in this study high breakdown robust methods are used in conjunction with a robust distance measure defined relative to the minimum volume ellipsoid estimator the standardized robust residuals from the high breakdown estimators and the robust distance measures are used to statistically and graphically depict both multivariate outliers and leverage points once these points are found their relationship to those observations that exhibit strong technically efficient or inefficient behavior scale inefficiency and or unusual production characteristics is analyzed for three linerboard manufacturing facilities additionally the impact of the outliers and leverage points on the estimated least squares coefficients which are used by the corrected ordinary least squares methodology to compute the full frontier technical efficiency measures is explored finally a sensitivity analysis of the impact of outliers and leverage points on the computed linear programming based technical efficiency measures is presented