generalizing the hough transform to detect arbitrary shapes abstract the hough transform is a method for detecting curves by exploiting the duality between points on a curve and parameters of that curve the initial work showed how to detect both analytic curves 1 2 and non analytic curves 3 but these methods were restricted to binary edge images this work was generalized to the detection of some analytic curves in grey level images specifically lines 4 circles 5 and parabolas 6 the line detection case is the best known of these and has been ingeniously exploited in several applications 7 8 9 we show how the boundaries of an arbitrary non analytic shape can be used to construct a mapping between image space and hough transform space such a mapping can be exploited to detect instances of that particular shape in an image furthermore variations in the shape such as rotations scale changes or figure ground reversals correspond to straightforward transformations of this mapping however the most remarkable property is that such mappings can be composed to build mappings for complex shapes from the mappings of simpler component shapes this makes the generalized hough transform a kind of universal transform which can be used to find arbitrarily complex shapes

a locking free fem for cavitation computation in nearly incompressible nonlinear elasticity abstract a reduced selective integration finite element method coupled with a gradient flow method is introduced for the cavitation computation in nearly incompressible nonlinear elasticity numerical experiments show that for the iso parametric biquadratic finite element approximation of the displacement field the method is locking free and efficient for the nearly incompressible cavitation computation and has the same optimal convergence rates as in the compressible case for the elastic energy as well as the deformation and stress field if and only if the reduced integration is applied to the bulk energy term using no more than 2 gaussian quadrature nodes in each dimension

bootstrapping boosted random ferns for discriminative and efficient object classification in this paper we show that the performance of binary classifiers based on boosted random ferns can be significantly improved by appropriately bootstrapping the training step this results in a classifier which is both highly discriminative and computationally efficient and is particularly suitable when only small sets of training images are available during the learning process a small set of labeled images is used to train the boosting binary classifier the classifier is then evaluated over the training set and warped versions of the classified and misclassified patches are progressively added into the positive and negative sample sets for a new re training step in this paper we thoroughly study the conditions under which this bootstrapping scheme improves the detection rates in particular we assess the quality of detection both as a function of the number of bootstrapping iterations and the size of the training set we compare our algorithm against state of the art approaches for several databases including faces cars motorbikes and horses and show remarkable improvements in detection rates with just a few bootstrapping steps

applications of field theoretic renormalization group methods to reaction diffusion problems we review the application of field theoretic renormalization group rg methods to the study of fluctuations in reaction diffusion problems we first investigate the physical origin of universality in these systems before comparing rg methods to other available analytic techniques including exact solutions and smoluchowski type approximations starting from the microscopic reaction diffusion master equation we then pedagogically detail the mapping to a field theory for the single species reaction ka a k we employ this particularly simple but non trivial system to introduce the field theoretic rg tools including the diagrammatic perturbation expansion renormalization and callan symanzik rg flow equation we demonstrate how these techniques permit the calculation of universal quantities such as density decay exponents and amplitudes via perturbative dc d expansions with respect to the upper critical dimension dc with these basics established we then provide an overview of more sophisticated applications to multiple species reactions disorder effects l evy flights persistence problems and the influence of spatial boundaries we also analyse field theoretic approaches to non equilibrium phase transitions separating active from absorbing states we focus particularly on the generic directed percolation universality class as well as on the most prominent exception to this class even offspring branching and annihilating random walks finally we summarize the state of the field and present our perspective on outstanding problems for the future

object recognition by sequential figure ground ranking we present an approach to visual object class segmentation and recognition based on a pipeline that combines multiple figure ground hypotheses with large object spatial support generated by bottom up computational processes that do not exploit knowledge of specific categories and sequential categorization based on continuous estimates of the spatial overlap between the image segment hypotheses and each putative class we differ from existing approaches not only in our seemingly unreasonable assumption that good object level segments can be obtained in a feed forward fashion but also in formulating recognition as a regression problem instead of focusing on a one vs all winning margin that may not preserve the ordering of segment qualities inside the non maximum non winning set our learning method produces a globally consistent ranking with close ties to segment quality hence to the extent entire object or part hypotheses are likely to spatially overlap the ground truth we demonstrate results beyond the current state of the art for image classification object detection and semantic segmentation in a number of challenging datasets including caltech 101 ethz shape as well as pascal voc 2009 and 2010

harmony potentials the hierarchical conditional random field hcrf model have been successfully applied to a number of image labeling problems including image segmentation however existing hcrf models of image segmentation do not allow multiple classes to be assigned to a single region which limits their ability to incorporate contextual information across multiple scales at higher scales in the image this representation yields an oversimplified model since multiple classes can be reasonably expected to appear within large regions this simplified model particularly limits the impact of information at higher scales since class label information at these scales is usually more reliable than at lower noisier scales neglecting this information is undesirable to address these issues we propose a new consistency potential for image labeling problems which we call the harmony potential it can encode any possible combination of labels penalizing only unlikely combinations of classes we also propose an effective sampling strategy over this expanded label set that renders tractable the underlying optimization problem our approach obtains state of the art results on two challenging standard benchmark datasets for semantic image segmentation pascal voc 2010 and msrc 21

segmentation of range images as the search for geometric parametric models segmentation of range images has long been considered in computer vision as an important but extremely difficult problem in this paper we present a new paradigm for the segmentation of range images into piecewise continuous surfaces data aggregation is performed via model recovery in terms of variable order bi variate polynomials using iterative regression model recovery is initiated independently in regularly placed seed regions in the image all the recovered models are potential candidates for the final description of the data selection of the models is defined as a quadratic boolean problem and the solution is sought by the wta winner takes all technique which turns out to be a good compromise between the speed of computation and the accuracy of the solution the overall efficiency of the method is achieved by combining model recovery and model selection in an iterative way partial recovery of the models is followed by the selection optimization procedure and only the best models are allowed to develop further

duality in interacting particle systems and boson representation in the context of markov processes we show a new scheme to derive dual processes and a duality function based on a boson representation this scheme is applicable to a case in which a generator is expressed by boson creation and annihilation operators for some stochastic processes duality relations have been known which connect continuous time markov processes with discrete state space and those with continuous state space we clarify that using a generating function approach and the doi peliti method a birth death process or discrete random walk model is naturally connected to a differential equation with continuous variables which would be interpreted as a dual markov process the key point in the derivation is to use bosonic coherent states as a bra state instead of a conventional projection state as examples we apply the scheme to a simple birth coagulation process and a brownian momentum process the generator of the brownian momentum process is written by elements of the su 1 1 algebra and using a boson realization of su 1 1 we show that the same scheme is available

shape based object detection via boundary structure segmentation we address the problem of object detection and segmentation using global holistic properties of object shape global shape representations are highly susceptible to clutter inevitably present in realistic images and thus can be applied robustly only using a precise segmentation of the object to this end we propose a figure ground segmentation method for extraction of image regions that resemble the global properties of a model boundary structure and are perceptually salient our shape representation called the chordiogram is based on geometric relationships of object boundary edges while the perceptual saliency cues we use favor coherent regions distinct from the background we formulate the segmentation problem as an integer quadratic program and use a semidefinite programming relaxation to solve it the obtained solutions provide a segmentation of the object as well as a detection score used for object recognition our single step approach achieves state of the art performance on several object detection and segmentation benchmarks

distances and means of direct similarities the non euclidean nature of direct isometries in a euclidean space i e transformations consisting of a rotation and a translation creates difficulties when computing distances means and distributions over them which have been well studied in the literature direct similarities transformations consisting of a direct isometry and a positive uniform scaling present even more of a challenge one which we demonstrate and address here in this article we investigate divergences a superset of distances without constraints on symmetry and sub additivity for comparing direct similarities and means induced by them via minimizing a sum of squared divergences we analyze several standard divergences the euclidean distance using the matrix representation of direct similarities a divergence from lie group theory and the family of all left invariant distances derived from riemannian geometry we derive their properties and those of their induced means highlighting several shortcomings in addition we introduce a novel family of left invariant divergences called srt divergences which resolve several issues associated with the standard divergences in our evaluation we empirically demonstrate the derived properties of the divergences and means both qualitatively and quantitatively on synthetic data finally we compare the divergences in a real world application vote based scale invariant object recognition our results show that the new divergences presented here and their means are both more effective and faster to compute for this task

duality relations for asymmetric exclusion processes we derive duality relations for a class ofu q su 2 symmetric stochastic processes including among others the asymmetric exclusion process in one dimension like the known duality relations for symmetric hopping processes these relations express certainm point correlation functions inn particle systems n m in terms of sums of correlation functions of the same system but with onlym particles for the totally asymmetric case we obtain exact expressions for some boundary density correlation functions the dynamical exponent for these correlators isz 2 which is different from the dynamical exponent for bulk density correlations which is known to bez 3 2

finite element analysis of anisotropic non linear incompressible elastic solids by a mixed model a mixed finite element method is presented for geometrically and materially non linear analysis of anisotropic incompressible hyperelastic materials an incremental iteractive total lagrangian formulation is adopted the nodal displacements and the hydrostatic pressure are independently interpolated leading to a mixed system of equations with characteristic zero diagonal terms computations are carried out using a three dimensional linear displacement constant pressure element a mixed penalty approximation is then employed to eliminate the pressure variables at the element level the anisotropic material handling capability of the formulation is tested through a number of transversely isotropic problems and the results compared to analytical solutions to demonstrate the applicability of this formulation to model complex anisotropic problems the inflation of a cut toroidal tube with helical fibre orientation is analysed

on detection of multiple object instances using hough transforms hough transform based methods for detecting multiple objects use nonmaxima suppression or mode seeking to locate and distinguish peaks in hough images such postprocessing requires the tuning of many parameters and is often fragile especially when objects are located spatially close to each other in this paper we develop a new probabilistic framework for object detection which is related to the hough transform it shares the simplicity and wide applicability of the hough transform but at the same time bypasses the problem of multiple peak identification in hough images and permits detection of multiple objects without invoking nonmaximum suppression heuristics our experiments demonstrate that this method results in a significant improvement in detection accuracy both for the classical task of straight line detection and for a more modern category level pedestrian detection problem

a novel eye localization method with rotation invariance this paper presents a novel learning method for precise eye localization a challenge to be solved in order to improve the performance of face processing algorithms few existing approaches can directly detect and localize eyes with arbitrary angels in predicted eye regions face images and original portraits at the same time to preserve rotation invariant property throughout the entire eye localization framework a codebook of invariant local features is proposed for the representation of eye patterns a heat map is then generated by integrating a 2 class sparse representation classifier with a pyramid like detecting and locating strategy to fulfill the task of discriminative classification and precise localization furthermore a series of prior information is adopted to improve the localization precision and accuracy experimental results on three different databases show that our method is capable of effectively locating eyes in arbitrary rotation situations 360 in plane

extended duality relations between birth death processes and partial differential equations duality relations between continuous state and discrete state stochastic processes with continuous time have already been studied and used in various research fields we propose extended duality relations which enable us to derive discrete state stochastic processes from arbitrary diffusion type partial differential equations the derivation is based on the doi peliti formalism and it will be clarified that additional states for the discrete state stochastic processes must be considered in some cases

optimal structural design considering flexibility a topology optimization method based on the homogenization method the homogenization design method has been successfully applied to a variety of optimization problems such as the stiffness maximization problem and the eigen frequency maximization problem in this study a methodology to obtain the optimal structure design considering flexibility is developed as a new extension of the homogenization design method first flexibility is formulated using the mutual energy concept second a new multi objective function is proposed to obtain optimal solutions incorporating flexibility and stiffness next the topology optimization procedure is constructed using the homogenization method and sequential linear programming slp finally some examples are presented to confirm that the methodology presented here can provide flexible structures satisfying the problem specifications

cpmc automatic object segmentation using constrained parametric min cuts we present a novel framework to generate and rank plausible hypotheses for the spatial extent of objects in images using bottom up computational processes and mid level selection cues the object hypotheses are represented as figure ground segmentations and are extracted automatically without prior knowledge of the properties of individual object classes by solving a sequence of constrained parametric min cut problems cpmc on a regular image grid in a subsequent step we learn to rank the corresponding segments by training a continuous model to predict how likely they are to exhibit real world regularities expressed as putative overlap with ground truth based on their mid level region properties then diversify the estimated overlap score using maximum marginal relevance measures we show that this algorithm significantly outperforms the state of the art for low level segmentation in the voc 2009 and 2010 data sets in our companion papers 1 2 we show that the algorithm can be used successfully in a segmentation based visual object category recognition pipeline this architecture ranked first in the voc2009 and voc2010 image segmentation and labeling challenges

analysis of mixed finite elements methods for the stokes problem a unified approach we develop a method for the analysis of mixed finite element methods for the stokes problem in the velocity pressure formulation a technical macroelement condition which is sufficient for the classical babuska brezzi inequality to be valid is introduced using this condition we are able to verify the stability and optimal order of convergence of several known mixed finite element methods

robust object detection with interleaved categorization and segmentation this paper presents a novel method for detecting and localizing objects of a visual category in cluttered real world scenes our approach considers object categorization and figure ground segmentation as two interleaved processes that closely collaborate towards a common goal as shown in our work the tight coupling between those two processes allows them to benefit from each other and improve the combined performance the core part of our approach is a highly flexible learned representation for object shape that can combine the information observed on different training examples in a probabilistic extension of the generalized hough transform the resulting approach can detect categorical objects in novel images and automatically infer a probabilistic segmentation from the recognition result this segmentation is then in turn used to again improve recognition by allowing the system to focus its efforts on object pixels and to discard misleading influences from the background moreover the information from where in the image a hypothesis draws its support is employed in an mdl based hypothesis verification stage to resolve ambiguities between overlapping hypotheses and factor out the effects of partial occlusion an extensive evaluation on several large data sets shows that the proposed system is applicable to a range of different object categories including both rigid and articulated objects in addition its flexible representation allows it to achieve competitive object detection performance already from training sets that are between one and two orders of magnitude smaller than those used in comparable systems

shape based detection and top down delineation using image segments we introduce a segmentation based detection and top down figure ground delineation algorithm unlike common methods which use appearance for detection our method relies primarily on the shape of objects as is reflected by their bottom up segmentation our algorithm receives as input an image along with its bottom up hierarchical segmentation the shape of each segment is then described both by its significant boundary sections and by regional dense orientation information derived from the segment s shape using the poisson equation our method then examines multiple overlapping segmentation hypotheses using their shape and color in an attempt to find a coherent whole i e a collection of segments that consistently vote for an object at a single location in the image once an object is detected we propose a novel pixel level top down figure ground segmentation by competitive coverage process to accurately delineate the boundaries of the object in this process given a particular detection hypothesis we let the voting segments compete for interpreting covering each of the semantic parts of an object incorporating competition in the process allows us to resolve ambiguities that arise when two different regions are matched to the same object part and to discard nearby false regions that participated in the voting process we provide quantitative and qualitative experimental results on challenging datasets these experiments demonstrate that our method can accurately detect and segment objects with complex shapes obtaining results comparable to those of existing state of the art methods moreover our method allows us to simultaneously detect multiple instances of class objects in images and to cope with challenging types of occlusions such as occlusions by a bar of varying size or by another object of the same class that are difficult to handle with other existing class specific top down segmentation methods

human detection by quadratic classification on subspace of extended histogram of gradients this paper proposes a quadratic classification approach on the subspace of extended histogram of gradients exhog for human detection by investigating the limitations of histogram of gradients hg and histogram of oriented gradients hog exhog is proposed as a new feature for human detection exhog alleviates the problem of discrimination between a dark object against a bright background and vice versa inherent in hg it also resolves an issue of hog whereby gradients of opposite directions in the same cell are mapped into the same histogram bin we reduce the dimensionality of exhog using asymmetric principal component analysis apca for improved quadratic classification apca also addresses the asymmetry issue in training sets of human detection where there are much fewer human samples than non human samples our proposed approach is tested on three established benchmarking data sets inria caltech and daimler using a modified minimum mahalanobis distance classifier results indicate that the proposed approach outperforms current state of the art human detection methods

equivalence between poisson representation and fock space formalism for birth death processes the equivalence between the poisson representation and the fock space formalism both used to derive coarse grained equations from the master equation for non equilibrium systems is explicitly demonstrated

vibration and stability analyses of cross ply laminated circular cylindrical shells abstract an improved analytical procedure is introduced in the context of free vibration and stability problems of cross ply laminated circular cylindrical shells the donnell shear deformation type theory and donnell s classical theory are used to illustrate the procedure numerical results are presented for shells with a variety of boundary conditions special attention is given to the axisymmetric problems and new results not yet found in the literature for stability and vibration are reported it is recommended that the new technique be used for generating the levy type solutions in vibration and stability problems of laminated shell panels and plates when the number and order of equations are higher than those of the classical theories

deformable prototypes for encoding shape categories in image databases abstract we describe a method for shape based image database search that uses deformable prototypes to represent categories rather than directly comparing a candidate shape with all shape entries in the database shapes are compared in terms of the types of nonrigid deformations differences that relate them to a small subset of representative prototypes to solve the shape correspondence and alignment problem we employ the technique of em modal matching an information preserving shape decomposition for matching describing and comparing shapes despite sensor variations and nonrigid deformations in modal matching shape is decomposed into an ordered basis of orthogonal principal components we demonstrate the utility of this approach for shape comparison in 2 d image databases

fast prism branch and bound hough transform for object class detection this paper addresses the task of efficient object class detection by means of the hough transform this approach has been made popular by the implicit shape model ism and has been adopted many times although ism exhibits robust detection performance its probabilistic formulation is unsatisfactory the principled implicit shape model prism overcomes these problems by interpreting hough voting as a dual implementation of linear sliding window detection it thereby gives a sound justification to the voting procedure and imposes minimal constraints we demonstrate prism s flexibility by two complementary implementations a generatively trained gaussian mixture model as well as a discriminatively trained histogram approach both systems achieve state of the art performance detections are found by gradient based or branch and bound search respectively the latter greatly benefits from prism s feature centric view it thereby avoids the unfavourable memory trade off and any on line pre processing of the original efficient subwindow search ess moreover our approach takes account of the features scale value while ess does not finally we show how to avoid soft matching and spatial pyramid descriptors during detection without losing their positive effect this makes algorithms simpler and faster both are possible if the object model is properly regularised and we discuss a modification of svms which allows for doing so

finite element hourglassing control a new hourglassing control technique for quadrilateral and hexahedral elements used in dynamic finite element codes is presented based on expansion of the strees in a taylor series and retention of additional terms beyond the usual constant stress term this technique has the advantage that actual rather than artificial material properties are used simplifications that render this technique competitive with artificial viscosity and stiffness procedures in terms of additional storage and computation requirements are described

multilayered shell theories accounting for layerwise mixed description part 1 governing equations a reissner mixed variational equation is employed in this paper to derive the differential governing equations of multilayered double curved shells made of orthotropic laminae in linear static cases a layerwise description is referred to by assuming two independent fields in the thickness direction for the transverse stress both shear and normal components and displacement variables in each layer interlaminar values are used as the unknown variables of the introduced expansions the continuity conditions of displacements and transverse shear and normals stresses at the interfaces between two consecutive layers referred to as c 0 z requirements have been a priori fulfilled these have been used to drive the governing equations from a layer to a multilayered level classical displacement formulations and related equivalent single layer equations have been derived for comparison purposes no assumptions have been made concerning the terms of type thickness to radii shell ratio h r donnell s shallow shell type equations are given as particular cases for all of the considered theories indicial notations and arrays have been used extensively to handle the presented developments in a concise manner numerical evaluations and comparisons to exact and other available two dimensional solutions are given in a companion paper e carrera multilayered shell theories accounting for layerwise mixed description part 2 numerical evaluations aiaa journal vol 37 no 9 1999 pp 1117 1124

layerwise theory for dynamic buckling and postbuckling of laminated composite cylindrical shells a general layerwise theory for dynamic buckling and postbuckling of imperfect multilayered composite circular cylindrical shells is introduced in contrast to the conventional displacement based layerwise theories that are more suitable for semianalytical solutions and kinematic boundary conditions the present method includes the displacement and stress components at the same time thus the method provides an efficient formulation where both kinematic and force boundary conditions can be exactly satisfied to use the exact expressions of strain and stress components the full green s strain tensor for large deflections of circular cylinders is employed then almost no simplification is made in the development of the governing equations the applied loads can be composed of various mechanical loads axial compression external pressure external fluid pressure and torsion or a combination of them accuracy and convergence of the present theory in comparison with the three dimensional elasticity approaches proposed so far are higher and more effective finally a few examples of various references that have used different theories are reexamined for comparison purpose

a locking free solver for linear elasticity on quadrilateral and hexahedral meshes based on enrichment of lagrangian elements abstract this paper presents a new finite element solver for linear elasticity on quadrilateral and hexahedral meshes based on enrichment of the classical bilinear or trilinear lagrangian elements it solves the primal variable displacement in the strain div formulation and can handle both displacement and traction boundary conditions it is a locking free solver based on conforming finite elements the solver has second order accuracy in displacement and first order accuracy in stress and dilation divergence of displacement as validated by theoretical analysis and illustrated by numerical experiments on benchmarks deal ii implementation is also discussed

locking spurious mechanisms and pressure divergence in penalty finite element methods for stokes flow problems abstract solution locking spurious mechanisms and pressure divergence exhibited in the stokes flow calculations by the penalty finite element methods are analyzed employing a fourier analysis technique comparison of the approximate discrete operator components with the corresponding continuum ones enables one to explicitly identify the conditions for avoiding locking spurious mechanisms and pressure divergence in terms of discrete operator components which are applicable to either the finite difference or finite element discretizations a synthesis approach is explored to eliminate the solution locking spurious mechanisms and pressure divergence in uniformly reduced integrated four noded elements extensions of the present analysis and synthesis approach to navier stokes equations appear to be straightforward if one deals with a set of consistently linearized operators

general two dimensional theory of laminated cylindrical shells the theory accounts for a desired degree of approximation of the displacements through the thickness thus accounting for any discontinuities in their derivatives at the interface of laminae geometric nonlinearity in the sense of the von karman strains is also included navier type solutions of the linear theory are presented for simply supported boundary conditions

a performance evaluation of local descriptors in this paper we compare the performance of descriptors computed for local interest regions as for example extracted by the harris affine detector mikolajczyk k and schmid c 2004 many different descriptors have been proposed in the literature it is unclear which descriptors are more appropriate and how their performance depends on the interest region detector the descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the detector our evaluation uses as criterion recall with respect to precision and is carried out for different image transformations we compare shape context belongie s et al april 2002 steerable filters freeman w and adelson e setp 1991 pca sift ke y and sukthankar r 2004 differential invariants koenderink j and van doorn a 1987 spin images lazebnik s et al 2003 sift lowe d g 1999 complex filters schaffalitzky f and zisserman a 2002 moment invariants van gool l et al 1996 and cross correlation for different types of interest regions we also propose an extension of the sift descriptor and show that it outperforms the original method furthermore we observe that the ranking of the descriptors is mostly independent of the interest region detector and that the sift based descriptors perform best moments and steerable filters show the best performance among the low dimensional descriptors

multidimensional morphable models a framework for representing and matching object classes we describe a flexible model for representing images of objects of a certain class known a priori such as faces and introduce a new algorithm for matching it to a novel image and thereby perform image analysis the flexible model known as a multidimensional morphable model is learned from example images of objects of a class in this paper we introduce an effective stochastic gradient descent algorithm that automatically matches a model to a novel image several experiments demonstrate the robustness and the broad range of applicability of morphable models our approach can provide novel solutions to several vision tasks including the computation of image correspondence object verification and image compression

large deformation triangular and tetrahedral element formulations for unstructured meshes abstract the increasing use of automatic mesh generators and remeshers has spurred the need for accurate and efficient triangular and tetrahedral elements in unstructured meshes in large deformation analysis of solids accuracy and efficiency require the treatment of volumetric locking and element distortions respectively in this paper a mixed formulations is proposed to treat these issues and applied to triangular and tetrahedral elements the formulations enforces in the weak sense the expression for the volumetric strain in terms of the deformation gradient the mixed formulation is combined with an irreducible formulation to provide a balance between the treatment of volumetric locking and the maintenance of an adequate critical timestep three techniques are proposed to determine such a balance at the local element level example computations in two and three dimensions of an iron cylinder impacting a rigid surface are used to demonstrate the formulations and to compare them to the irreducible displacement based formulation

an integration scheme for prandtl reuss elastoplastic constitutive equations we investigate the generalized midpoint rule for the time integration of elastoplastic constitutive equations for pressure independent yield criteria the incremental equations are divided into one scalar hydrostatic pressure dilation rate equation and a stress deviator strain rate deviator tensorial equation this formulation is applied to two classical problems bulging of a tube under internal pressure and tension test on a notched specimen

treatment of internal constraints by mixed finite element methods unification of concepts a general method to generate assumed stress and strain fields within the context of mixed finite element methods is presented the assumed fields are constructed in such a way that internal constraints are satisfied a priori consequently the locking behaviour commonly observed in finite element solutions of problems with internal constraints is avoided to this end the assumed stress and strain fields are constructed to satisfy a priori the homogeneous part of the equilibrium equations thus avoiding fraeijs de veubeke s limitation principle results obtained using the proposed methodology on a nearly incompressible plane strain problem and thin plate application using a shear deformable theory are indicated

beyond the sum of parts voting with groups of dependent entities the high complexity of multi scale category level object detection in cluttered scenes is efficiently handled by hough voting methods however the main shortcoming of the approach is that mutually dependent local observations are independently casting their votes for intrinsically global object properties such as object scale object hypotheses are then assumed to be a mere sum of their part votes popular representation schemes are however based on a dense sampling of semi local image features which are consequently mutually dependent we take advantage of part dependencies and incorporate them into probabilistic hough voting by deriving an objective function that connects three intimately related problems i grouping mutually dependent parts ii solving the correspondence problem conjointly for dependent parts and iii finding concerted object hypotheses using extended groups rather than based on local observations alone early commitments are avoided by not restricting parts to only a single vote for a locally best correspondence and we learn a weighting of parts during training to reflect their differing relevance for an object experiments successfully demonstrate the benefit of incorporating part dependencies through grouping into hough voting the joint optimization of groupings correspondences and votes not only improves the detection accuracy over standard hough voting and a sliding window baseline but it also reduces the computational complexity by significantly decreasing the number of candidate hypotheses

random geometric prior forest for multiclass object segmentation recent advances in object detection have led to the development of segmentation by detection approaches that integrate top down geometric priors for multiclass object segmentation a key yet under addressed issue in utilizing top down cues for the problem of multiclass object segmentation by detection is efficiently generating robust and accurate geometric priors in this paper we propose a random geometric prior forest scheme to obtain object adaptive geometric priors efficiently and robustly in the scheme a testing object first searches for training neighbors with similar geometries using the random geometric prior forest and then the geometry of the testing object is reconstructed by linearly combining the geometries of its neighbors our scheme enjoys several favorable properties when compared with conventional methods first it is robust and very fast because its inference does not suffer from bad initializations poor local minimums or complex optimization second the figure ground geometries of training samples are utilized in a multitask manner third our scheme is object adaptive but does not require the labeling of parts or poselets and thus it is quite easy to implement to demonstrate the effectiveness of the proposed scheme we integrate the obtained top down geometric priors with conventional bottom up color cues in the frame of graph cut the proposed random geometric prior forest achieves the best segmentation results of all of the methods tested on voc2010 2012 and is 90 times faster than the current state of the art method

a comparison of affine region detectors the paper gives a snapshot of the state of the art in affine covariant region detectors and compares their performance on a set of test images under varying imaging conditions six types of detectors are included detectors based on affine normalization around harris mikolajczyk and schmid 2002 schaffalitzky and zisserman 2002 and hessian points mikolajczyk and schmid 2002 a detector of maximally stable extremal regions proposed by matas et al 2002 an edge based region detector tuytelaars and van gool 1999 and a detector based on intensity extrema tuytelaars and van gool 2000 and a detector of salient regions proposed by kadir zisserman and brady 2004 the performance is measured against changes in viewpoint scale illumination defocus and image compression the objective of this paper is also to establish a reference test set of images and performance software so that future detectors can be evaluated in the same framework

matching widely separated views based on affine invariant regions invariant regions are self adaptive image patches that automatically deform with changing viewpoint as to keep on covering identical physical parts of a scene such regions can be extracted directly from a single image they are then described by a set of invariant features which makes it relatively easy to match them between views even under wide baseline conditions in this contribution two methods to extract invariant regions are presented the first one starts from corners and uses the nearby edges while the second one is purely intensity based as a matter of fact the goal is to build an opportunistic system that exploits several types of invariant regions as it sees fit this yields more correspondences and a system that can deal with a wider range of images to increase the robustness of the system two semi local constraints on combinations of region correspondences are derived one geometric the other photometric they allow to test the consistency of correspondences and hence to reject falsely matched regions experiments on images of real world scenes taken from substantially different viewpoints demonstrate the feasibility of the approach

convergence of mixed finite element approximations for the shallow arch problem the stability and convergence of mixed finite element methods are investigated for an equilibrium problem for thin shallow elastic arches the problem in its standard form contains two terms corresponding to the contributions from the shear and axial strains with a small parameter lagrange multipliers are introduced to formulate the problem in an alternative mixed form questions of existence and uniqueness of solutions to the standard and mixed problems are addressed it is shown that finite element approximations of the mixed problem are stable and convergent reduced integration formulations are equivalent to a mixed formulation which in general is distinct from the formulation shown to be stable and convergent except when the order of polynomial interpolationt of the arch shape satisfies 1 t min 2 r wherer is the order of polynomial approximation of the unknown variables

finite element analysis of linear and non linear planar deformations of elastic initially curved beams reference epfl article 102719view record in web of science record created on 2007 04 23 modified on 2016 08 08

on a systematic development of trilinear three dimensional solid elements based on simo s enhanced strain formulation a systematic development of eight node trilinear three dimensional solid elements based on simo s theoretical framework for the enhanced assumed strain formulation is presented for linear elasticity the starting point is a regular brick followed by parallelepipeds and then arbitrarily distorted elements the advantages and deficits of various enhanced strain formulations are extensively discussed using the regular brick for arbitrarily distorted elements a simple modification of the element stiffness matrix is proposed to achieve a more reliable and very efficient element on some numerical examples the advantages and limits of the family of enhanced strain elements and the improved elements are discussed

accurate object recognition with shape masks in this paper we propose an object recognition approach that is based on shape masks generalizations of segmentation masks as shape masks carry information about the extent outline of objects they provide a convenient tool to exploit the geometry of objects we apply our ideas to two common object class recognition tasks classification and localization for classification we extend the orderless bag of features image representation in the proposed setup shape masks can be seen as weak geometrical constraints over bag of features those constraints can be used to reduce background clutter and help recognition for localization we propose a new recognition scheme based on high dimensional hypothesis clustering shape masks allow to go beyond bounding boxes and determine the outline approximate segmentation of the object during localization furthermore the method easily learns and detects possible object viewpoints and articulations which are often well characterized by the object outline our experiments reveal that shape masks can improve recognition accuracy of state of the art methods while returning richer recognition answers at the same time we evaluate the proposed approach on the challenging natural scene graz 02 object classes dataset

the b bar method and the limitation principles abstract the generalized elastic material provides a reference model to cast in a unitary framework many structural models which are based on nonlinear monotone multivalued relations such as viscoelasticity plasticity and unilateral models the modified forms of the hu washizu and hellinger reissner principles and the displacement based variational formulation are recovered for the generalized elastic material starting from a functional in the complete set of state variables the related limitation principles are derived and their specialization to elasticity and elastoplasticity with mixed hardening are provided it is shown that the interpolating fields for the pressure and the volumetric strain usually adopted in the b bar method lead to a limitation principle accordingly the same elastic and elastoplastic solutions can be obtained by means of an approximate mixed displacement pressure variational principle a second application is concerned with the conditions ensuring the coincidence of the solutions between an approximate two field mixed formulation and the displacement based method numerical examples are provided to show the coincidence of the solutions obtained from different mixed finite element formulations in elasticity or elastoplasticity under the validity of the limitation principles

shape matching and object recognition using shape contexts we present a novel approach to measuring similarity between shapes and exploit it for object recognition in our framework the measurement of similarity is preceded by 1 solving for correspondences between points on the two shapes 2 using the correspondences to estimate an aligning transform in order to solve the correspondence problem we attach a descriptor the shape context to each point the shape context at a reference point captures the distribution of the remaining points relative to it thus offering a globally discriminative characterization corresponding points on two similar shapes will have similar shape contexts enabling us to solve for correspondences as an optimal assignment problem given the point correspondences we estimate the transformation that best aligns the two shapes regularized thin plate splines provide a flexible class of transformation maps for this purpose the dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points together with a term measuring the magnitude of the aligning transform we treat recognition in a nearest neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image results are presented for silhouettes trademarks handwritten digits and the coil data set

enhanced linear triangle for plasticity problems in j 2 solids due to their economy and capability to reproduce high gradient zones low order elements are of great interest in the calculations involving localized failure however the performance of the simplest element the three nodes triangle under incompressible and bending dominated situations is very poor and the computed results unreliable this paper presents an inexpensive three fields u p formulation solving these issues as shown in the examples of application

online learning of a dirichlet process mixture of beta liouville distributions via variational inference a large class of problems can be formulated in terms of the clustering process mixture models are an increasingly important tool in statistical pattern recognition and for analyzing and clustering complex data two challenging aspects that should be addressed when considering mixture models are how to choose between a set of plausible models and how to estimate the model s parameters in this paper we address both problems simultaneously within a unified online nonparametric bayesian framework that we develop to learn a dirichlet process mixture of beta liouville distributions i e an infinite beta liouville mixture model the proposed infinite model is used for the online modeling and clustering of proportional data for which the beta liouville mixture has been shown to be effective we propose a principled approach for approximating the intractable model s posterior distribution by a tractable one which we develop such that all the involved mixture s parameters can be estimated simultaneously and effectively in a closed form this is done through variational inference that enjoys important advantages such as handling of unobserved attributes and preventing under or overfitting we explain that in detail the effectiveness of the proposed work is evaluated on three challenging real applications namely facial expression recognition behavior modeling and recognition and dynamic textures clustering

mining layered grammar rules for action recognition we propose a layered grammar model to represent actions using this model an action is represented by a set of grammar rules the bottom layer of an action instance s parse tree contains action primitives such as spatiotemporal st interest points at each layer above we iteratively mine grammar rules and super rules that account for the high order compositional feature structures the grammar rules are categorized into three classes according to three different st relations of their action components namely the strong relation weak relation and stochastic relation these st relations characterize different action styles degree of stiffness and they are pursued in terms of grammar rules for the purpose of action recognition by adopting the emerging pattern ep mining algorithm for relation pursuit the learned production rules are statistically significant and discriminative using the learned rules the parse tree of an action video is constructed by combining a bottom up rule detection step and a top down ambiguous rule pruning step an action instance is recognized based on the discriminative configurations generated by the production rules of its parse tree experiments confirm that by incorporating the high order feature statistics the proposed method largely improves the recognition performance over the bag of words models

object detection using the statistics of parts in this paper we describe a trainable object detector and its instantiations for detecting faces and cars at any size location and pose to cope with variation in object orientation the detector uses multiple classifiers each spanning a different range of orientation each of these classifiers determines whether the object is present at a specified size within a fixed size image window to find the object at any location and size these classifiers scan the image exhaustively each classifier is based on the statistics of localized parts each part is a transform from a subset of wavelet coefficients to a discrete set of values such parts are designed to capture various combinations of locality in space frequency and orientation in building each classifier we gathered the class conditional statistics of these part values from representative samples of object and non object images we trained each classifier to minimize classification error on the training set by using adaboost with confidence weighted predictions shapire and singer 1999 in detection each classifier computes the part values within the image window and looks up their associated class conditional probabilities the classifier then makes a decision by applying a likelihood ratio test for efficiency the classifier evaluates this likelihood ratio in stages at each stage the classifier compares the partial likelihood ratio to a threshold and makes a decision about whether to cease evaluation labeling the input as non object or to continue further evaluation the detector orders these stages of evaluation from a low resolution to a high resolution search of the image our trainable object detector achieves reliable and efficient detection of human faces and passenger cars with out of plane rotation

optimal spectral approximation of 2n order differential operators by mixed isogeometric analysis abstract we approximate the spectra of a class of 2 n order differential operators using isogeometric analysis in mixed formulations this class includes a wide range of differential operators such as those arising in elliptic biharmonic cahn hilliard swift hohenberg and phase field crystal equations the spectra of the differential operators are approximated by solving differential eigenvalue problems in mixed formulations which require auxiliary parameters the mixed isogeometric formulation when applying classical quadrature rules leads to an eigenvalue error convergence of order 2 p where p is the order of the underlying b spline space we improve this order to be 2 p 2 by applying optimally blended quadrature rules developed in puzyrev et al 2017 caloet al 0000 and this order is an optimum in the view of dispersion error we also compare these results with the mixed finite elements and show numerically that the mixed isogeometric analysis leads to significantly better spectral approximations

nonlinear kalman filter based on duality relations between continuous and discrete state stochastic processes an alternative application of duality relations of stochastic processes is demonstrated although conventional usages of the duality relations need analytical solutions for the dual processes here i employ numerical solutions of the dual processes and investigate the usefulness as a demonstration estimation problems of hidden variables in stochastic differential equations are discussed employing algebraic probability theory a little complicated birth death process is derived from the stochastic differential equations and an estimation method based on the ensemble kalman filter is proposed as a result the possibility for making faster computational algorithms based on the duality concepts is shown

a study of transverse normal stress effect on vibration of multilayered plates and shells this paper evaluates transverse normal stress zzeffect on vibration of multilayered structures to this purpose a mixed plate model initially introduced by toledano and murakami has been extended to dynamics analysis of double curved shells these models allow both continuous interlaminar transverse shear and normal stresses as well as the zigzag form of the displacement distribution in the shell thickness directions to be modelled governing equations have been derived by employing a reissner s mixed theorem classical models on the basis of standard displacement formulations have been considered for comparison purposes the evaluations of transverse stress effects have been conducted by comparing constant linear and higher order distributions of transverse displacement components in the plate thickness directions free vibrational response of layered simply supported plates cylindrical and spherical shells made of isotropic as well as orthotropic layers has been analyzed the numerical investigation carried out and comparison with earlier results has concluded that 1 the possibility of describing interlaminar continuous transverse normal stress makes the mixed theories more attractive with respect to other available modelling 2 any refinements of classical models are meaningless unless the effects of interlaminar continuous transverse shear and normal stresses are both taken into account in a multilayered shell theory

image decomposition with multilabel context algorithms and applications most research on image decomposition e g image segmentation and image parsing has predominantly focused on the low level visual clues within a single image and neglected the contextual information across images in this paper we present a new perspective to image decomposition piloted by the multilabel context associated with each individual image observing that the contextual information i e local label representations of the same label are similar while those from different labels are dissimilar exists across images we propose to perform image decomposition in a collective way and obtain an optimal representation for each label from a set of multilabeled images we formulate the problem as an optimization problem which maximizes inter label difference while minimizing the intra label difference of the target label representations and propose two ways to solve this problem such a contextual image decomposition has a wide variety of applications among which two exemplary ones multilabel image annotation and label ranking are presented and evaluated with different classification techniques extensive experiments on two benchmark datasets demonstrate promising results

contour and texture analysis for image segmentation this paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent brightness and texture natural images contain both textured and untextured regions so the cues of contour and texture differences are exploited simultaneously contours are treated in the intervening contour framework while texture is analyzed using textons each of these cues has a domain of applicability so to facilitate cue combination we introduce a gating operator based on the texturedness of the neighborhood at a pixel having obtained a local measure of how likely two nearby pixels are to belong to the same region we use the spectral graph theoretic framework of normalized cuts to find partitions of the image into regions of coherent texture and brightness experimental results on a wide range of images are shown

accuracy of some finite element models for arch problems abstract this paper presents a theoretical accuracy study of some finite element models for thin arches as is well known the selection of finite elements for curved members is quite a delicate problem we obtain order estimates of errors of finite element solutions by means of the perturbation theory of mixed models and the technique of asymptotic expansion in particular we theoretically show that certain finite element models may suffer from the so called locking phenomenon numerical results are also given to be compared with the theoretical error estimates

efficient subwindow search a branch and bound framework for object localization most successful object recognition systems rely on binary classification deciding only if an object is present or not but not providing information on the actual object location to estimate the object s location one can take a sliding window approach but this strongly increases the computational cost because the classifier or similarity function has to be evaluated over a large set of candidate subwindows in this paper we propose a simple yet powerful branch and bound scheme that allows efficient maximization of a large class of quality functions over all possible subimages it converges to a globally optimal solution typically in linear or even sublinear time in contrast to the quadratic scaling of exhaustive or sliding window search we show how our method is applicable to different object detection and image retrieval scenarios the achieved speedup allows the use of classifiers for localization that formerly were considered too slow for this task such as svms with a spatial pyramid kernel or nearest neighbor classifiers based on the lambda2 distance we demonstrate state of the art localization performance of the resulting systems on the uiuc cars data set the pascal voc 2006 data set and in the pascal voc 2007 competition

stationary states and their stability of the stepping stone model involving mutation and selection on considere un modele de diffusion qui est un processus de diffusion de dimension infinie on etudie la regularite des distributions marginales de dimension finie des etats stationnaires

a penalty model for the analysis of laminated composite shells abstract a theory for laminated composite shells and a finite element model based on this theory are presented due to a flexible layer wise description of the displacement field the theory accounts for an accurate description of the shear deformation and the stretching of transverse normals to the middle surface with respect to other layer wise theories which are available in the literature the present one distinguishes itself by the use of the penalty method to enforce the perfect bonding between adjacent layers this method offers the possibility to easily obtain accurate interlaminar stresses numerical results for simply supported laminated spherical shells are presented to show the features of the proposed theory and its accuracy

reconstruction of first order shear deformation shell theory for laminated and sandwich shells a first order shear deformation shell theory fsdt based on general curvilinear coordinates has been reconstructed through a strain energy transformation based on the definition of the mean displacements the transverse shear strains that are constant through the thickness are improved to vary through the thickness it is assumed that the displacements and in surface strains of three dimensional theory are approximated by those of fsdt relationship between fsdt and three dimensional theory has been systematically established in the averaged least square sense this relationship provides the closed form recovering relations for three dimensional variables expressed in terms of fsdt relations the present approach has been applied to a higher order zigzag shell theory comparisons of displacements and stresses of laminated and sandwich shells using the present theory are made with the original fsdt and three dimensional exact solutions

mixed finite element methods reduced and selective integration techniques a unification of concepts the equivalence of certain classes of mixed finite element methods with displacement methods which employ reduced and selective integration techniques is established this enables one to obtain the accuracy of the mixed formulation without incurring the additional computational expense engendered by the auxiliary field of the mixed method applications and numerical examples are presented for problems with constraints which can be difficult to enforce in finite element approximations and have often dictated the use of mixed principles these include thin beams and plates and linear and nonlinear incompressible and nearly incompressible continuum problems in solid and fluid mechanics

b and f projection methods for nearly incompressible linear and non linear elasticity and plasticity using higher order nurbs elements abstract this paper presents projection methods to treat the incompressibility constraint in small and large deformation elasticity and plasticity within the framework of isogeometric analysis after reviewing some fundamentals of isogeometric analysis we investigate the use of higher order non uniform rational b splines nurbs within the b projection method the higher continuity property of such functions is explored in nearly incompressible applications and shown to produce accurate and robust results a new non linear f projection method based on a modified minimum potential energy principle and inspired by the b method is proposed for the large deformation case it leads to a symmetric formulation for which the consistent linearized operator for fully non linear elasticity is derived and used in a newton raphson iterative procedure the performance of the methods is assessed on several numerical examples and results obtained are shown to compare favorably with other published techniques

demisting the hough transform for 3d shape recognition and registration in applying the hough transform to the problem of 3d shape recognition and registration we develop two new and powerful improvements to this popular inference method the first intrinsic hough solves the problem of exponential memory requirements of the standard hough transform by exploiting the sparsity of the hough space the second minimum entropy hough explains away incorrect votes substantially reducing the number of modes in the posterior distribution of class and pose and improving precision our experiments demonstrate that these contributions make the hough transform not only tractable but also highly accurate for our example application both contributions can be applied to other tasks that already use the standard hough transform

mixed finite element with drilling rotations for plane problems in finite elasticity abstract in this work we apply the mixed finite element method to construct a two dimensional membrane element with drilling rotations for geometrically nonlinear elasticity only the 4 node element interpolation is discussed but the formulation is plausible for higher order elements as well numerical examples for both linear and nonlinear analyses demonstrate that the element yields rather stiff but indeed convergent results

mean shift mode seeking and clustering mean shift a simple interactive procedure that shifts each data point to the average of data points in its neighborhood is generalized and analyzed in the paper this generalization makes some k means like clustering algorithms its special cases it is shown that mean shift is a mode seeking process on the surface constructed with a shadow kernal for gaussian kernels mean shift is a gradient mapping convergence is studied for mean shift iterations cluster analysis if treated as a deterministic problem of finding a fixed point of mean shift that characterizes the data applications in clustering and hough transform are demonstrated mean shift is also considered as an evolutionary strategy that performs multistart global optimization

hough pyramid matching speeded up geometry re ranking for large scale image retrieval exploiting local feature shape has made geometry indexing possible but at a high cost of index space while a sequential spatial verification and re ranking stage is still indispensable for large scale image retrieval in this work we investigate an accelerated approach for the latter problem we develop a simple spatial matching model inspired by hough voting in the transformation space where votes arise from single feature correspondences using a histogram pyramid we effectively compute pair wise affinities of correspondences without ever enumerating all pairs our hough pyramid matching algorithm is linear in the number of correspondences and allows for multiple matching surfaces or non rigid objects under one to one mapping we achieve re ranking one order of magnitude more images at the same query time with superior performance compared to state of the art methods while requiring the same index space we show that soft assignment is compatible with this matching scheme preserving one to one mapping and further increasing performance

efficient higher order shell theory for laminated composites with multiple delaminations a higher order zigzag theory has been developed for laminated composite shells with multiple delaminations general tensor based formulation is developed for arbitrary curved shell with exact geometric description a laminated shell theory with multiple delaminations for general lamination configurations is obtained by superposing a cubic varying displacement on a zigzag linearly varying displacement the von karman nonlinearity is included in the formulation for the potentialities of addressing problems requiring geometric nonlinearity such as large deflection and postbuckling problems when top and bottom surface transverse shear stress free conditions and interface transverse shear continuity conditions including delamination interfaces are imposed the displacement of the minimal degrees of freedom is obtained the proposed displacement field can systematically handle the number shape size and locations of the delaminations through the variational principle equilibrium equations and variationally consistent boundary conditions are obtained to assess the accuracy and efficiency of the present theory the linear buckling problem of cylindrical shell with multiple delaminations has been analyzed the higher order zigzag theory should work as an efficient tool for analyzing the behavior of composite laminated shells with multiple delaminations

hybrid hexahedral element for solids plates shells and beams by selective scaling a robust two field hexahedral element capable of handling plate shell beam and nearly incompressible material analyses without locking are presented starting with the assumed stress element of pian and tong 7 parasitic strain components leading to locking in plate shell and beam analyses are first identified locking can be alleviated by scaling down selectively the parasitic strain components in the leverage matrix unfortunately the element then fails the patch test however patch test correction and reduction in computation can be achieved by the recently proposed admissible matrix formulation the resulting element is lock free and very efficient all matrices involved in constructing the stiffness matrix can be derived explicitly the accuracy of the element is tested by popular bench mark problems