a three invariant hardening plasticity for numerical simulation of powder forming processes via the arbitrary lagrangian eulerian fe model in this paper a three invariant cap plasticity model with an isotropic hardening rule is presented for numerical simulation of powder compaction processes a general form is developed for single cap plasticity which can be compared with some common double surface plasticity models proposed for powders in literature the constitutive elasto plastic matrix and its components are derived based on the definition of yield surface hardening parameter and non linear elastic behaviour as function of relative density of powder different aspects of the new single plasticity are illustrated by generating the classical plasticity models as special cases of the proposed model the procedure for determination of powder parameters is described by fitting the model to reproduce data from triaxial compression and confining pressure experiments the three invariant cap plasticity is performed within the framework of an arbitrary lagrangian eulerian formulation in order to predict the non uniform relative density distribution during large deformation of powder die pressing in ale formulation the reference configuration is used for describing the motion instead of material configuration in lagrangian and spatial configuration in eulerian formulation this formulation introduces some convective terms in the finite element equations and consists of two phases each time step is analysed according to lagrangian phase until required convergence is attained then the eulerian phase is applied to keep mesh configuration regular because of relative displacement between mesh and material all dependent variables such as stress and strain are converted through the eulerian phase finally the numerical schemes are examined for efficiency and accuracy in the modelling of a rotational flanged component an automotive component a conical shaped charge liner and a connecting rod copyright 2005 john wiley sons ltd

principal warps thin plate splines and the decomposition of deformations the decomposition of deformations by principal warps is demonstrated the method is extended to deal with curving edges between landmarks this formulation is related to other applications of splines current in computer vision how they might aid in the extraction of features for analysis comparison and diagnosis of biological and medical images in indicated

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

structured prediction dual extragradient and bregman projections we present a simple and scalable algorithm for maximum margin estimation of structured output models including an important class of markov networks and combinatorial models we formulate the estimation problem as a convex concave saddle point problem that allows us to use simple projection methods based on the dual extragradient algorithm nesterov 2003 the projection step can be solved using dynamic programming or combinatorial algorithms for min cost convex flow depending on the structure of the problem we show that this approach provides a memory efficient alternative to formulations based on reductions to a quadratic program qp we analyze the convergence of the method and present experiments on two very different structured prediction tasks 3d image segmentation and word alignment illustrating the favorable scaling properties of our algorithm

regularization theory and neural networks architectures we had previously shown that regularization principles lead to approximation schemes that are equivalent to networks with one layer of hidden units called regularization networks in particular s

robustness of shape descriptors to incomplete contour representations with inspiration from psychophysical researches of the human visual system we propose a novel aspect and a method for performance evaluation of contour based shape recognition algorithms regarding their robustness to incompleteness of contours we use complete contour representations of objects as a reference training set incomplete contour representations of the same objects are used as a test set the performance of an algorithm is reported using the recognition rate as a function of the percentage of contour retained we call this evaluation procedure the icr test we consider three types of contour incompleteness viz segment wise contour deletion occlusion and random pixel depletion as an illustration the robustness of two shape recognition algorithms to contour incompleteness is evaluated these algorithms use a shape context and a distance multiset as local shape descriptors qualitatively both algorithms mimic human visual perception in the sense that recognition performance monotonously increases with the degree of completeness and that they perform best in the case of random depletion and worst in the case of occluded contours the distance multiset method performs better than the shape context method in this test framework

the two machine flowshop scheduling problem with sequence independent setup times new lower bounding strategies abstract the two machine flowshop environment with sequence independent setup times has been intensely investigated both from theoretical and practical perspectives in the scheduling literature nevertheless very scant attention has been devoted to deriving effective lower bounding strategies in this paper we propose new lower bounds for the total completion time minimization criterion these bounds are based on three relaxation schemes namely the waiting time based relaxation scheme the single machine based relaxation scheme and the lagrangian relaxation scheme extensive computational study carried on instances with up to 500 jobs reveals that embedding the waiting time based bounding strategy within the lagrangian relaxation framework yields the best performance while requiring negligible cpu time

new algorithms for 2d and 3d point matching pose estimation and correspondence abstract a fundamental open problem in computer vision determining pose and correspondence between two sets of points in space is solved with a novel fast robust and easily implementable algorithm the technique works on noisy 2d or 3d point sets that may be of unequal sizes and may differ by non rigid transformations using a combination of optimization techniques such as deterministic annealing and the softassign which have recently emerged out of the recurrent neural network statistical physics framework analog objective functions describing the problems are minimized over thirty thousand experiments on randomly generated points sets with varying amounts of noise and missing and spurious points and on hand written character sets demonstrate the robustness of the algorithm

modal matching for correspondence and recognition modal matching is a new method for establishing correspondences and computing canonical descriptions the method is based on the idea of describing objects in terms of generalized symmetries as defined by each object s eigenmodes the resulting modal description is used for object recognition and categorization where shape similarities are expressed as the amounts of modal deformation energy needed to align the two objects in general modes provide a global to local ordering of shape deformation and thus allow for selecting which types of deformations are used in object alignment and comparison in contrast to previous techniques which required correspondence to be computed with an initial or prototype shape modal matching utilizes a new type of finite element formulation that allows for an object s eigenmodes to be computed directly from available image information this improved formulation provides greater generality and accuracy and is applicable to data of any dimensionality correspondence results with 2d contour and point feature data are shown and recognition experiments with 2d images of hand tools and airplanes are described

improved algorithms for bipartite network flow in this paper network flow algorithms for bipartite networks are studied a network g v e is called bipartite if its vertex set v can be partitioned into two subsets v 1 and v 2 such that all edges have one endpoint in v 1 and the other in v 2 let n v n 1 v 1 n 2 v 2 m e and assume without loss of generality that n 1 leq n 2 a bipartite network is called unbalanced if n 1 ll n 2 and balanced otherwise this notion is necessarily imprecise it is shown that several maximum flow algorithms can be substantially sped up when applied to unbalanced networks the basic idea in these improvements is a two edge push rule that allows one to charge most computation to vertices in v 1 and hence develop algorithms whose running times depend on n 1 rather than n for example it is shown that the two edge push version of goldberg and tarjan s fifo preflow push algorithm runs in o n 1 m n 1 3 time and that the analogous version of ahuja and orlin s excess scaling algorithm runs in o n 1 m n 1 2 log u time where u is the largest edge capacity these ideas are also extended to dynamic tree implementations parametric maximum flows and minimum cost flows

on the impact of anisotropic diffusion on edge detection content aware edge preserving smoothing techniques have gained visibility in recent years however they have had a rather limited impact on the edge detection literature compared to content unaware linear techniques often based on gaussian filters in this work we focus on anisotropic diffusion covering its initial definition by perona and malik and subsequent extensions a visual case study is used to illustrate their features we perform a quantitative evaluation of the performance of the canny method for edge detection when substituting linear gaussian smoothing filters by anisotropic diffusion highlightsreviewing the main trends in anisotropic diffusion identifying the practical scenarios on which anisotropic diffusion fails quantifying the improvement of edge detection when anisitropic diffusion is applied

numerical modelling of large deformation in metal powder forming abstract in this paper the transient dynamic analysis of metal powder during the cold compaction process is simulated by the finite element method based on a total and updated lagrangian formulation since the compaction process involves a very large reduction in volume the behaviour of the powders is assumed to be that of a rate independent elasto plastic material the process is therefore described by a large displacement finite element formulation for the spatial discretization a generalized newmark scheme is used for the time domain discretization and then the final nonlinear equations are solved by a newton raphson procedure a combination of a mohr coulomb and elliptical cap yield model is utilised as a constitutive model to describe the nonlinear behaviour of powder materials an incremental elasto plastic material model is used to simulate the compaction process and a plasticity theory for friction is employed in the treatment of the powder tooling interface the interfacial behaviour between the die and powder is modelled by using an interface element mesh finally the powder behaviour during the compaction of a plane bush a cutting tool and a rotational flanged component is analysed numerically the predictive compaction forces at different displacements the variation with time of the displacement relative density and stress contours are obtained it is shown that the proposed large displacement elasto plastic finite element approach is capable of simulating the metal powder during compaction

the complexity of class polynomial computation via floating point approximations we analyse the complexity of computing class polynomials that are an important ingredient for cm constructions of elliptic curves via complex floating point approximations of their roots the heart of the algorithm is the evaluation of modular functions in several arguments the fastest one of the presented approaches uses a technique devised by dupont to evaluate modular functions by newton iterations on an expression involving the arithmetic geometric mean it runs in time o d log 5 d log log d o d 1 epsilon o h 2 epsilon for any epsilon 0 where d is the cm discriminant and h is the degree of the class polynomial another fast algorithm uses multipoint evaluation techniques known from symbolic computation its asymptotic complexity is worse by a factor of log d up to logarithmic factors this running time matches the size of the constructed polynomials the estimate also relies on a new result concerning the complexity of enumerating the class group of an imaginary quadratic order and on a rigorously proven upper bound for the height of class polynomials

snapshots a novel local surface descriptor and matching algorithm for robust 3d surface alignment in this paper a novel local surface descriptor is proposed and applied to the problem of aligning partial views of a 3d object the descriptor is based on taking snapshots of the surface over each point using a virtual camera oriented perpendicularly to the surface this representation has the advantage of imposing minimal loss of information be robust to self occlusions and also be very efficient to compute then we describe an efficient search technique to deal with the rotation ambiguity of our representation and experimentally demonstrate the benefits of our approaches which are pronounced especially when we align views with small overlap

the difference between consecutive primes ii with enough effort the value of x0 could be determined effectively the paper has much in common with 1 in particular we use the sieve method of harman 4 5 we no longer use zero density estimates however but rather mean value results on dirichlet polynomials similar to those that give rise to such estimates compare for example work of iwaniec and pintz 9 and baker harman and pintz 2 much of the improvement over 1 arises from the use of watt s theorem 11 on a particular kind of mean value more accurate estimates for six dimensional integrals are also used to good effect there is in addition a device which uses a two dimensional sieve to get an asymptotic formula for a one dimensionally sieved set see lemmas 16 17 unfortunately these lemmas which would be of great signi cance for v 0 53 are not very numerically signi cant when v drops to 0 525 the same applies to the roale reversals discussed below let us introduce enough notation to permit an outline of the proof when e is a nite sequence of positive integers counted with multiplicity we write jej for the number of terms of e and ed fm dm 2 eg let p z y

fast descriptors and correspondence propagation for robust global point cloud registration in this paper we present a robust global approach for point cloud registration from uniformly sampled points based on eigenvalues and normals computed from multiple scales we design fast descriptors to extract local structures of these points the eigenvalue based descriptor is effective at finding seed matches with low precision using nearest neighbor search generally recovering the transformation from matches with low precision is rather challenging therefore we introduce a mechanism named correspondence propagation to aggregate each seed match into a set of numerous matches with these sets of matches multiple transformations between point clouds are computed a quality function formulated from distance errors is used to identify the best transformation and fulfill a coarse alignment of the point clouds finally we refine the alignment result with the trimmed iterative closest point algorithm the proposed approach can be applied to register point clouds with significant or limited overlaps and small or large transformations more encouragingly it is rather efficient and very robust to noise a comparison to traditional descriptor based methods and other global algorithms demonstrates the fine performance of the proposed approach we also show its promising application in large scale reconstruction with the scans of two real scenes in addition the proposed approach can be used to register low resolution point clouds captured by kinect as well

minimum distortion isometric shape correspondence using em algorithm we present a purely isometric method that establishes 3d correspondence between two nearly isometric shapes our method evenly samples high curvature vertices from the given mesh representations and then seeks an injective mapping from one vertex set to the other that minimizes the isometric distortion we formulate the problem of shape correspondence as combinatorial optimization over the domain of all possible mappings which then reduces in a probabilistic setting to a log likelihood maximization problem that we solve via the expectation maximization em algorithm the em algorithm is initialized in the spectral domain by transforming the sampled vertices via classical multidimensional scaling mds minimization of the isometric distortion and hence maximization of the log likelihood function is then achieved in the original 3d euclidean space for each iteration of the em algorithm in two steps by first using bipartite perfect matching and then a greedy optimization algorithm the optimal mapping obtained at convergence can be one to one or many to one upon choice we demonstrate the performance of our method on various isometric or nearly isometric pairs of shapes for some of which the ground truth correspondence is available

dense 3d face correspondence we present an algorithm that automatically establishes dense correspondences between a large number of 3d faces starting from automatically detected sparse correspondences on the outer boundary of 3d faces the algorithm triangulates existing correspondences and expands them iteratively by matching points of distinctive surface curvature along the triangle edges after exhausting keypoint matches further correspondences are established by generating evenly distributed points within triangles by evolving level set geodesic curves from the centroids of large triangles a deformable model k3dm is constructed from the dense corresponded faces and an algorithm is proposed for morphing the k3dm to fit unseen faces this algorithm iterates between rigid alignment of an unseen face followed by regularized morphing of the deformable model we have extensively evaluated the proposed algorithms on synthetic data and real 3d faces from the frgcv2 bosphorus bu3dfe and und ear databases using quantitative and qualitative benchmarks our algorithm achieved dense correspondences with a mean localisation error of 1 28 mm on synthetic faces and detected 14 anthropometric landmarks on unseen real faces from the frgcv2 database with 3 mm precision furthermore our deformable model fitting algorithm achieved 98 5 percent face recognition accuracy on the frgcv2 and 98 6 percent on bosphorus database our dense model is also able to generalize to unseen datasets

a rigorous time bound for factoring integers in this paper a probabilistic algorithm is exhibited that factors any positive integer n into prime factors in expected time at most ln 2 1 o for n oo where l a b exp b logx a loglogx l a many practical factoring algorithms including the quadratic sieve and the elliptic curve method are conjectured to have an expected running time that satisfies the same bound but this is the first algorithm for which the bound can be rigorously proved nevertheless this does not close the gap between rigorously established time bounds and merely conjectural ones for factoring algorithms this is due to the advent of a new factoring algorithm the number field sieve which is conjectured to factor any positive integer n in time ln i 0 1 the algorithm analyzed in this paper is a variant of the class group relations method which makes use of class groups of binary quadratic forms of negative discriminant this algorithm was first suggested by seysen and later improved by a k lenstra who showed that the algorithm runs in expected time at most ln 12 1 o 1 if one assumes the generalized riemann hypothesis the main device for removing the use of the generalized riemann hypothesis from the proof is the use of multipliers in addition a character sum estimate for algebraic number fields is used with an explicit dependence on possible exceptional zeros of the corresponding l functions another factoring algorithm using class groups that has been proposed is the random class groups method it is shown that there is a fairly large set of numbers that this algorithm cannot be expected to factor as efficiently as had previously been thought department of mathematics university of california berkeley california 94720 e mail address hwl math berkeley edu department of mathematics university of georgia athens georgia 30602 e mail address carl ada math uga edu this content downloaded from 157 55 39 186 on tue 12 apr 2016 08 54 17 utc all use subject to http about jstor org terms

an eigendecomposition approach to weighted graph matching problems an approximate solution to the weighted graph matching problem is discussed for both undirected and directed graphs the weighted graph matching problem is that of finding the optimum matching between two weighted graphs which are graphs with weights at each arc the proposed method uses an analytic instead of a combinatorial or iterative approach to the optimum matching problem using the eigendecompositions of the adjacency matrices in the case of the undirected graph matching problem or hermitian matrices derived from the adjacency matrices in the case of the directed graph matching problem a matching close to the optimum can be found efficiently when the graphs are sufficiently close to each other simulation results are given to evaluate the performance of the proposed method

reducing rank maximal to maximum weight matching given a bipartite graph g v e email protected email protected b where v n e m and a partition of the edge set into email protected m disjoint subsets e e email protected email protected e email protected email protected email protected email protected e r which are called ranks the rank maximal matching problem is to find a matching m of g such that email protected e 1 is maximized and given that email protected e 1 is maximized email protected e 2 is also maximized and so on such a problem arises as an optimization criteria over a possible assignment of a set of applicants to a set of posts the matching represents the assignment and the ranks on the edges correspond to a ranking of the posts submitted by the applicants the rank maximal matching problem and several other optimization variants e g fair matching and maximum cardinality rank maximal matching can be solved by a reduction to the weight matching problem in time o rnmlogn recently irving et al developed a combinatorial approach which improves the running time for the rank maximal matching problem to o min n r rn m they raised the open questions on a whether such a running time can be achieved by the weight matching reduction and b whether such a running time can be achieved for the other variants of the problem in this work we show how the reduction to the weight matching problem can also be used to achieve the same running time our algorithm is simpler and more intuitive

faster scaling algorithms for network problems this paper presents algorithms for the assignment problem the transportation problem and the minimum cost flow problem of operations research the algorithms find a minimum cost solution yet run in time close to the best known bounds for the corresponding problems without costs for example the assignment problem equivalently minimum cost matching in a bipartite graph can be solved in o sqrt nm log nn time where n m and n denote the number of vertices number of edges and largest magnitude of a cost costs are assumed to be integral the algorithms work by scaling as in the work of goldberg and tarjan in each scaled problem an approximate optimum solution is found rather than an exact optimum

separability criteria for the evaluation of boundary detection benchmarks there exist a significant number of benchmarks for evaluating the performance of boundary detection algorithms most of them relying on some sort of comparison of the automatically generated boundaries with human labeled ones such benchmarks are composed of a representative image data set as well as a comparison measure on the universe of boundary images despite many such data sets and measures have been proposed there is no clear way of knowing which combinations of them are the most suitable for the task in this paper we introduce four criteria that allow for a sensible evaluation of the performance of a comparison measure on a given data set the criteria mimic the way in which humans understand boundary images as well as their ability to recognize the underlying scenes these criteria can as a final goal quantify the ability of the boundary detection benchmarks to evaluate the performance of boundary detection methods either edge based or segmentation based

pre organizing shape instances for landmark based shape correspondence the major challenge in constructing a statistical shape model for a structure is shape correspondence which identifies a set of corresponded landmarks across a population of shape instances to accurately estimate the underlying shape variation both global or pairwise shape correspondence methods have been developed to automatically identify the corresponded landmarks for global methods landmarks are found by optimizing a comprehensive objective function that considers the entire population of shape instances while global methods can produce very accurate shape correspondence they tend to be very inefficient when the population size is large for pairwise methods all shape instances are corresponded to a given template independently therefore pairwise methods are usually very efficient however if the population exhibits a large amount of shape variation pairwise methods may produce very poor shape correspondence in this paper we develop a new method that attempts to address the limitations of global and pairwise methods in particular we first construct a shape tree to globally organize the population of shape instances by identifying similar shape instance pairs we then perform pairwise shape correspondence between such similar shape instances with high accuracy finally we combine these pairwise correspondences to achieve a unified correspondence for the entire population of shape instances we evaluate the proposed method by comparing its performance to five available shape correspondence methods and show that the proposed method achieves the accuracy of a global method with the efficiency of a pairwise method

quantitative error measures for edge detection how to measure the performance of an edge detection method is an open problem many performance measures have been presented in the literature however there seems to be no agreement on the best option and there exist no practical studies of the features of some of the most recent measures in this work we make a comprehensive overview of the different proposals followed by a practical comparison of the most representative measures on synthetic as well as natural edge images

efficient cm constructions of elliptic curves over finite fields we present an algorithm that on input of an integer n 1 together with its prime factorization constructs a finite field f and an elliptic curve e over f for which e f has order n although it is unproved that this can be done for all n a heuristic analysis shows that the algorithm has an expected run time that is polynomial in 2 n log n where n is the number of distinct prime factors of n in the cryptographically relevant case where n is prime an expected run time o logn 4 e can be achieved we illustrate the efficiency of the algorithm by constructing elliptic curves with point groups of order n 10 2004 and n nextprime 102004 10 2004 4863

an efficient cost scaling algorithm for the assignment problem the cost scaling push relabel method has been shown to be efficient for solving minimum cost flow problems in this paper we apply the method to the assignment problem and investigate implementations of the method that take advantage of assignment s special structure the results show that the method is very promising for practical use

joint induction of shape features and tree classifiers we introduce a very large family of binary features for two dimensional shapes the salient ones for separating particular shapes are determined by inductive learning during the construction of classification trees there is a feature for every possible geometric arrangement of local topographic codes the arrangements express coarse constraints on relative angles and distances among the code locations and are nearly invariant to substantial affine and nonlinear deformations they are also partially ordered which makes it possible to narrow the search for informative ones at each node of the tree different trees correspond to different aspects of shape they are statistically and weakly dependent due to randomization and are aggregated in a simple way adapting the algorithm to a shape family is then fully automatic once training samples are provided as an illustration we classified handwritten digits from the nist database the error rate was 0 7 percent

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

spectra of shape contexts an application to symbol recognition the pixel level constraint plc histograms are known for robustness and invariance in symbol recognition but limited in o n 3 complexity this paper proves that matching two plc histograms can approximately be solved as matching the power spectra of the corresponding shape contexts as a result spectra of shape contexts ssc inherit robustness and invariance from plc while the computational cost can be reduced moreover a maximum clique based scheme is proposed for outlier rejection the theoretical and experimental validation justifies that ssc possesses the desired properties for symbol recognition that is robustness invariance and efficiency it outperforms plc in terms of robustness and time efficiency and shape context in terms of rotation invariance

elliptic curves over finite fields and the computation of square roots mod p in this paper we present a deterministic algorithm to compute the number of f points of an elliptic curve that is defined over a finite field fv and which is given by a weierstrass equation the algorithm takes 0 log9 q elementary operations as an application wc give an algorithm to compute square roots mod p for fixed i e z it takes 0 log9p elementary operations to compute fx mod p 1 introduction in this paper we present an algorithm to compute the number of f points of an elliptic curve defined over a finite field f which is given by a weierstrass equation we restrict ourselves to the case where the characteristic of f is not 2 or 3 the algorithm is deterministic does not depend on any unproved hypotheses and takes 0 log9 0 if one applies fast multiplication techniques the algorithm will take 0 x 1 2log p 6 f elementary operations for any e 0 let be an elliptic curve defined over the prime field fp and let an affine model of it be given by a weierstrass equation y2 x3 ax b a befp an explicit formula for the number of f points on is given by

evaluating shape correspondence for statistical shape analysis a benchmark study this paper introduces a new benchmark study to evaluate the performance of landmark based shape correspondence used for statistical shape analysis different from previous shape correspondence evaluation methods the proposed benchmark first generates a large set of synthetic shape instances by randomly sampling a given statistical shape model that defines a ground truth shape space we then run a test shape correspondence algorithm on these synthetic shape instances to identify a set of corresponded landmarks according to the identified corresponded landmarks we construct a new statistical shape model which defines a new shape space we finally compare this new shape space against the ground truth shape space to determine the performance of the test shape correspondence algorithm in this paper we introduce three new performance measures that are landmark independent to quantify the difference between the ground truth and the newly derived shape spaces by introducing a ground truth shape space that is defined by a statistical shape model and three new landmark independent performance measures we believe the proposed benchmark allows for a more objective evaluation of shape correspondence than previous methods in this paper we focus on developing the proposed benchmark for 2d shape correspondence however it can be easily extended to 3d cases

constructing ensembles of classifiers by means of weighted instance selection in this paper we approach the problem of constructing ensembles of classifiers from the point of view of instance selection instance selection is aimed at obtaining a subset of the instances available for training capable of achieving at least the same performance as the whole training set in this way instance selection algorithms try to keep the performance of the classifiers while reducing the number of instances in the training set meanwhile boosting methods construct an ensemble of classifiers iteratively focusing each new member on the most difficult instances by means of a biased distribution of the training instances in this work we show how these two methodologies can be combined advantageously we can use instance selection algorithms for boosting using as objective to optimize the training error weighted by the biased distribution of the instances given by the boosting method our method can be considered as boosting by instance selection instance selection has mostly been developed and used for k nearest neighbor k nn classifiers so as a first step our methodology is suited to construct ensembles of k nn classifiers constructing ensembles of classifiers by means of instance selection has the important feature of reducing the space complexity of the final ensemble as only a subset of the instances is selected for each classifier however the methodology is not restricted to k nn classifier other classifiers such as decision trees and support vector machines svms may also benefit from a smaller training set as they produce simpler classifiers if an instance selection algorithm is performed before training in the experimental section we show that the proposed approach is able to produce better and simpler ensembles than random subspace method rsm method for k nn and standard ensemble methods for c4 5 and svms

morphometric tools for landmark data geometry and biology 1 introduction 2 preliminaries 3 landmarks 4 distance measures 5 shape coordinates 6 principal axes of shape change for triangles 7 features of shape comparison 8 retrospect and prospect

robust symbolic representation for shape recognition and retrieval a new method for shape recognition and retrieval is proposed here the suggested algorithm is based on several steps the algorithm analyzes the contour of pairs of shapes their contours are recovered and represented by a pair of n points obtained by linear interpolation given two points p i and q j from the two shapes the cost of their matching is evaluated by using the shape context and by using dynamic programming the best matching between the point sets is obtained dynamic programming not only recovers the best matching but also identifies occlusions i e points in the two shapes which cannot be properly matched given the correspondence between the two point sets the two contours are aligned using procrustes analysis after alignment each contour is transformed into a string of symbols and a modified version of edit distance is used to compute the similarity between strings of symbols finally recognition and retrieval are obtained by a simple nearest neighbor procedure the algorithm has been tested on a large set of shape databases kimia mpeg 7 natural silhouette database gesture database marine database swedish leaf database diatom database eth 80 3d object database providing performances for both in recognition and in retrieval superior to most of previously proposed approaches

shape of elastic strings in euclidean space we construct a 1 parameter family of geodesic shape metrics on a space of closed parametric curves in euclidean space of any dimension the curves are modeled on homogeneous elastic strings whose elasticity properties are described in terms of their tension and rigidity coefficients as we change the elasticity properties we obtain the various elastic models the metrics are invariant under reparametrizations of the curves and induce metrics on shape space analysis of the geometry of the space of elastic strings and path spaces of elastic curves enables us to develop a computational model and algorithms for the estimation of geodesics and geodesic distances based on energy minimization we also investigate a curve registration procedure that is employed in the estimation of shape distances and can be used as a general method for matching the geometric features of a family of curves several examples of geodesics are given and experiments are carried out to demonstrate the discriminative quality of the elastic metrics

finding minimum cost circulations by successive approximation we develop a new approach to solving minimum cost circulation problems our approach combines methods for solving the maximum flow problem with successive approximation techniques based on cost scaling we measure the accuracy of a solution by the amount that the complementary slackness conditions are violated we propose a simple minimum cost circulation algorithm one version of which runs in on3lognc time on an n vertex network with integer arc costs of absolute value at most c by incorporating sophisticated data structures into the algorithm we obtain a time bound of onm logn2 mlognc on a network with m arcs a slightly different use of our approach shows that a minimum cost circulation can be computed by solving a sequence of on lognc blocking flow problems a corollary of this result is an on2log nlognc time m processor parallel minimum cost circulation algorithm our approach also yields strongly polynomial minimum cost circulation algorithms our results provide evidence that the minimum cost circulation problem is not much harder than the maximum flow problem we believe that a suitable implementation of our method will perform extremely well in practice

local grayvalue invariants for image retrieval this paper addresses the problem of retrieving images from large image databases the method is based on local grayvalue invariants which are computed at automatically detected interest points a voting algorithm and semilocal constraints make retrieval possible indexing allows for efficient retrieval from a database of more than 1 000 images experimental results show correct retrieval in the case of partial visibility similarity transformations extraneous features and small perspective deformations

implementing the asymptotically fast version of the elliptic curve primality proving algorithm the elliptic curve primality proving ecpp algorithm is one of the current fastest practical algorithms for proving the primality of large numbers its running time cannot be proven rigorously but heuristic arguments show that it should run in time o log n 5 to prove the primality of n an asymptotically fast version of it attributed to j o shallit runs in time o log n 4 the aim of this article is to describe this version in more details leading to actual implementations able to handle numbers with several thousands of decimal digits

unified detection of skewed rotation reflection and translation symmetries from affine invariant contour features symmetry detection is significant for object detection and recognition since symmetries are salient cues for distinguishing geometrical structures from cluttered backgrounds this paper proposes a unified framework to detect rotation reflection and translation symmetries simultaneously on unsegmented real world images the detection is performed based on affine invariant contour features and is applicable under skewed imaging with distortions contours on a natural image are first matched to each other to find affine invariant matching pairs which are then classified into three groups using a sign change criterion corresponding to the three types of symmetries the three groups are used to vote for the corresponding symmetries the voting for rotation utilizes a scheme of short line segments the voting for reflection is based on a parameterization of axis equation and the voting for translation uses a cascade like approach experimental results of real world images are provided with quantitative evaluations validating that the proposed framework achieves desired performance

view based recognition using an eigenspace approximation to the hausdorff measure view based recognition methods such as those using eigenspace techniques have been successful for a number of recognition tasks such approaches however are somewhat limited in their ability to recognize objects that are partly hidden from view or occur against cluttered backgrounds in order to address these limitations we have developed a view matching technique based on an eigenspace approximation to the generalized hausdorff measure this method achieves compact storage and fast indexing that are the main advantages of eigenspace view matching techniques while also being tolerant of partial occlusion and background clutter the method applies to binary feature maps such as intensity edges rather than directly to intensity images

flexible syntactic matching of curves and its application to automatic hierarchical classification of silhouettes curve matching is one instance of the fundamental correspondence problem our flexible algorithm is designed to match curves under substantial deformations and arbitrary large scaling and rigid transformations a syntactic representation is constructed for both curves and an edit transformation which maps one curve to the other is found using dynamic programming we present extensive experiments where we apply the algorithm to silhouette matching in these experiments we examine partial occlusion viewpoint variation articulation and class matching where silhouettes of similar objects are matched based on the qualitative syntactic matching we define a dissimilarity measure and we compute it for every pair of images in a database of 121 images we use this experiment to objectively evaluate our algorithm first we compare our results to those reported by others second we use the dissimilarity values in order to organize the image database into shape categories the veridical hierarchical organization stands as evidence to the quality of our matching and similarity estimation

correlated logistic model with elastic net regularization for multilabel image classification in this paper we present correlated logistic corrlog model for multilabel image classification corrlog extends conventional logistic regression model into multilabel cases via explicitly modeling the pairwise correlation between labels in addition we propose to learn the model parameters of corrlog with elastic net regularization which helps exploit the sparsity in feature selection and label correlations and thus further boost the performance of multilabel classification corrlog can be efficiently learned though approximately by regularized maximum pseudo likelihood estimation and it enjoys a satisfying generalization bound that is independent of the number of labels corrlog performs competitively for multilabel image classification on benchmark data sets mulan scene mit outdoor scene pascal voc 2007 and pascal voc 2012 compared with the state of the art multilabel classification algorithms

rigid shape matching by segmentation averaging we use segmentations to match images by shape the new matching technique does not require point to point edge correspondence and is robust to small shape variations and spatial shifts to address the unreliability of segmentations computed bottom up we give a closed form approximation to an average over all segmentations our method has many extension yielding new algorithms for tracking object detection segmentation and edge preserving smoothing for segmentation instead of a maximum a posteriori approach we compute the central segmentation minimizing the average distance to all segmentations of an image for smoothing instead of smoothing images based on local structures we smooth based on the global optimal image structures our methods for segmentation smoothing and object detection perform competitively and we also show promising results in shape based tracking

disconnected skeleton shape at its absolute scale we present a new skeletal representation along with a matching framework to address the deformable shape recognition problem the disconnectedness arises as a result of excessive regularization that we use to describe a shape at an attainably coarse scale our motivation is to rely on stable properties the shape instead of inaccurately measured secondary details the new representation does not suffer from the common instability problems of the traditional connected skeletons and the matching process gives quite successful results on a diverse database of 2d shapes an important difference of our approach from the conventional use of skeleton is that we replace the local coordinate frame with a global euclidean frame supported by additional mechanisms to handle articulations and local boundary deformations as a result we can produce descriptions that are sensitive to any combination of changes in scale position orientation and articulation as well as invariant ones

a new approach to the maximum flow problem all previously known efficient maximum flow algorithms work by finding augmenting paths either one path at a time as in the original ford and fulkerson algorithm or all shortest length augmenting paths at once using the layered network approach of dinic an alternative method based on the preflow concept of karzanov is introduced a preflow is like a flow except that the total amount flowing into a vertex is allowed to exceed the total amount flowing out the method maintains a preflow in the original network and pushes local flow excess toward the sink along what are estimated to be shortest paths the algorithm and its analysis are simple and intuitive yet the algorithm runs as fast as any other known method on dense graphs achieving an o n 3 time bound on an n vertex graph by incorporating the dynamic tree data structure of sleator and tarjan we obtain a version of the algorithm running in o nm log n 2 m time on an n vertex m edge graph this is as fast as any known method for any graph density and faster on graphs of moderate density the algorithm also admits efficient distributed and parallel implementations a parallel implementation running in o n 2 log n time using n processors and o m space is obtained this time bound matches that of the shiloach vishkin algorithm which also uses n processors but requires o n 2 space

an eulerian approach for die compaction processes summary for the analysis of deformation processes the eulerian approach is usually formulated in material velocities to describe the die compaction of compressible media this paper presents an eulerian simulation method basically expressed in displacements the material behaviour is modelled by the theory of elastoplasticity frictional interaction with the surroundings is included as a spatially fixed finite element mesh is applied rezoning is governed by the process specification and not as in the lagrangian approach by the mesh distortion the solution scheme using a newton raphson algorithm is considered in detail a consistent iteration procedure is derived examples demonstrate the merits of the method developed

computing hilbert class polynomials with the chinese remainder theorem we present a space efficient algorithm to compute the hilbert class polynomial h d x modulo a positive integer p based on an explicit form of the chinese remainder theorem under the generalized riemann hypothesis the algorithm uses o d 1 2 e log p space and has an expected running time of o d 1 e we describe practical optimizations that allow us to handle larger discriminants than other methods with d as large as 10 13 and h d up to 10 6 we apply these results to construct pairing friendly elliptic curves of prime order using the cm method

proving primality in essentially quartic random time this paper presents an algorithm that given a prime n finds and verifies a proof of the primality of n in random time lg n 4 o 1 several practical speedups are incorporated into the algorithm and discussed in detail