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

a new algorithm for distorted fingerprints matching based on normalized fuzzy similarity measure coping with nonlinear distortions in fingerprint matching is a challenging task this paper proposes a novel algorithm normalized fuzzy similarity measure nfsm to deal with the nonlinear distortions the proposed algorithm has two main steps first the template and input fingerprints were aligned in this process the local topological structure matching was introduced to improve the robustness of global alignment second the method nfsm was introduced to compute the similarity between the template and input fingerprints the proposed algorithm was evaluated on fingerprints databases of fvc2004 experimental results confirm that nfsm is a reliable and effective algorithm for fingerprint matching with nonliner distortions the algorithm gives considerably higher matching scores compared to conventional matching algorithms for the deformed fingerprints

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

dense registration of fingerprints abstract dense registration of different impressions of the same finger is beneficial to various fingerprint matching methods this is a challenging problem due to elastic distortion of finger skin and sparsity of distinctive features namely minutiae in fingerprints most existing fingerprint registration algorithms produce only correspondences between minutiae which are not sufficient for dense registration of fingerprints in this paper we proposed a novel dense fingerprint registration algorithm which consists of a composite initial registration step and a dual resolution block based registration step the dual resolution block based registration is approached in an energy minimization framework which consists of local search energy function construction and global optimization in local search step a candidate set of transformations of every input image block are found using image correlation w r t the corresponding reference image block in energy function construction two factors are considered 1 the similarity between the transformed input block and the corresponding reference block and 2 the compatibility between transformations of neighboring input blocks in global optimization a region growing style algorithm is proposed to minimize the energy function experimental results on three databases containing many distorted fingerprints namely fvc2004 db1 tsinghua distorted fingerprint database and nist sd27 latent fingerprint database show that the proposed algorithm not only produces more accurate registration results but also improves the matching performance by fusion of minutiae matching and image correlation

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

feature guided gaussian mixture model with semi supervised em and local geometric constraint for retinal image registration abstract retinal image registration which can be formulated by matching two sets of sparse features extracted from two observed retinal images is a crucial step in the diagnosis and treatment of various eye diseases existing methods suffer from missing true correspondences or do not fully consider local appearance information which causes difficulty in matching low quality retinal images due to insufficient reliable features in addition the relationships between retinal image pairs are usually modeled by linear transformation such as affine transformation which cannot generate accurate alignments in large viewpoint changes due to the nonplanar eyeball surface to address these issues a feature guided gaussian mixture model gmm is proposed for the non rigid registration of retinal images we formulate the problem as an estimation of a feature guided mixture of densities a gmm is fitted to one point set in which the centers of the gaussian densities characterized by spatial positions associated with local appearance descriptors are constrained to coincide with the other point set the problem is solved under a maximum likelihood framework and semi supervised expectation maximization is used to iteratively estimate the feature correspondence and spatial transformation which is initialized by a set of confidential feature matches obtained previously non rigid transformation is specified in a reproducing kernel hilbert space and a local geometric constraint is imposed to establish the transformation estimation for obtaining a meaningful solution a fast implementation based on sparse approximation is also provided and reduces the time complexity from cubic to quadratic moreover we use the edge map which can extract more reliable features as a uniform representation of retinal images experimental results on publicly available retinal images show that our approach is robust in different registration tasks and outperforms several competing approaches especially when data is severely degraded

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

bogoyavlensky volterra and birkhoff integrable systems in this paper we examine an interesting connection between the generalized volterra lattices of bogoyavlensky and a special case of an integrable system defined by sklyanin the sklyanin system happens to be one of the cases in the classification of kozlov and treshchev of birkhoff integrable hamiltonian systems using this connection we demonstrate the integrability of the system and define a new lax pair representation in addition we comment on the bi hamiltonian structure of the system

approximately clean quantum probability measures a quantum probability measure or quantum measurement is said to be clean if it cannot be irreversibly connected to any other quantum probability measure via a quantum channel the notion of a clean quantum measure was introduced by buscemi et al clean positive operator valued measures j math phys 46 8 082109 2005 10 1063 1 2008996 for finite dimensional hilbert space and was studied subsequently by kahn clean positive operator valued measures for qubits and similar cases j phys a 40 18 4817 4832 2007 10 1088 1751 8113 40 18 009 and pellonpaa complete characterization of extreme quantum observables in finite dimensions j phys a 44 8 085304 2011 10 1088 1751 8113 44 8 085304 the present paper provides new descriptions of clean quantum probability measures in the case of finite dimensional hilbert space for hilbert spaces of infinite dimension we introduce the notion of approximately clean quantum probability measures and characterise this property for measures whose ran

fast and scalable approximate spectral graph matching for correspondence problems establishing consistent correspondences between two sets of features is a fundamental problem in computer vision this problem can be well formulated as graph matching in which nodes and edges represent feature points and pairwise relationships between feature points respectively spectral matching 19 is the state of the art eigenvector based method for graph matching the spectral matching algorithm has been used successfully for small data but its heavy memory requirement limited the maximum data sizes and contexts it can be used in this paper we propose fasm a fast and scalable approximate spectral matching algorithm the main ideas are twofold first we exploit the redundancy in the data generation process to approximate the affinity matrix with the linear combination of kronecker products between bases and index matrices the bases and index matrices are highly compressed representation of the approximated affinity matrix requiring much smaller memory than in previous works which store the whole affinity matrix second we compute the eigenvector of the approximated affinity matrix using the small bases and index matrices without explicitly materializing the approximated matrix experimental results show that our proposed method is up to 33x faster requiring up to 645x smaller memory than the exact algorithm with little or no loss of accuracy

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

new global solutions of the jacobi partial differential equations abstract a new family of solutions of the jacobi partial differential equations for finite dimensional poisson systems is investigated this family is mathematically remarkable as the functional dependences of the solutions appear to be associated to the distinguished invariants of the solutions themselves this kind of poisson structure termed distinguished solutions or d solutions is defined for every nontrivial combination of values of the dimension and the rank and is also determined in terms of functions of arbitrary nonlinearity properties usually not present simultaneously in the already known solution families in addition d solutions display several properties allowing the generation of an infinity of d solutions from a given one which is an uncommon feature in the framework of the jacobi equations furthermore a special family of d solutions complying with the previous requirements is constructively characterized and analyzed examples are discussed focusing on physical implications and including an application for the global construction of the darboux canonical form

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

fingerprint image postprocessing a combined statistical and structural approach abstract for complex patterns such as fingerprints it is difficult to describe the dactylotype by using a set of feature vectors as well as to construct and parse all of the individual fingerprint pattern with a syntactical approach in this paper which is different from the conventional methods we present an approach in which the statistical and structural approaches are combined to represent the fingerprint false minutia patterns based on the proposed approach a fingerprint image postprocessing algorithm is developed to eliminate the false minutiae in fingerprint images because a global minutia relation is considered on the whole image experimental results demonstrate the advantages of the combined approach

lax formalism for a family of integrable toda related n particle systems this article can be divided in two parts first a lax representation is obtained for a family of integrable three particle toda related systems the second part contains the generalization to the case of n particles

clean positive operator valued measures for qubits and similar cases in a recent paper buscemi et al 2005 j math phys 46 082109 a notion of clean positive operator valued measures povms was defined we characterize those povms which are clean in a class that we call quasi qubit povms namely povms whose elements are all rank 1 or full rank elements we give an algorithm to check whether a given quasi qubit povm satisfies this condition we describe explicitly all the povms that are clean for the qubit meanwhile we give a sufficient condition for a general povm to be clean

information theoretic shape matching in this paper we describe two related algorithms that provide both rigid and non rigid point set registration with different computational complexity and accuracy the first algorithm utilizes a nonlinear similarity measure known as correntropy the measure combines second and high order moments in its decision statistic showing improvements especially in the presence of impulsive noise the algorithm assumes that the correspondence between the point sets is known which is determined with the surprise metric the second algorithm mitigates the need to establish a correspondence by representing the point sets as probability density functions pdf the registration problem is then treated as a distribution alignment the method utilizes the cauchy schwarz divergence to measure the similarity distance between the point sets and recover the spatial transformation function needed to register them both algorithms utilize information theoretic descriptors however correntropy works at the realizations level whereas cauchy schwarz divergence works at the pdf level this allows correntropy to be less computationally expensive and for correct correspondence more accurate the two algorithms are robust against noise and outliers and perform well under varying levels of distortion they outperform several well known and state of the art methods for point set registration

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

boundary conditions for integrable quantum systems a new class of boundary conditions is described for quantum systems integrable by means of the quantum inverse scattering r matrix method the method proposed allows the author to treat open quantum chains with appropriate boundary terms in the hamiltonian the general considerations are applied to the xxz and xyz models the nonlinear schrodinger equation and toda chain

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

integrability of toda lattice by generalized variational symmetry approach a systematic method of finding nontrivial generalized variational symmetries are presented herein and the corresponding first integrals for nonlinear dynamical system with two degrees of freedom are derived then a method is applied to a toda lattice given by the potential v ex y c1e2y c2ey c3e 2x c4 e x where c1 c2 c3 and c4 are parameters the symmetries and constants of motion are presented explicitly

kowalewski s asymptotic method kac moody lie algebras and regularization we use an effective criterion based on the asymptotic analysis of a class of hamiltonian equations to determine whether they are linearizable on an abelian variety i e solvable by quadrature the criterion is applied to a system with hamiltonian h 1 mathord left vphantom 1 2 right kern nulldelimiterspace 2 sum limits i 1 l p i 2 sum limits i 1 l 1 exp left sum limits j 1 l n ij x j right parametrized by a real matrixn n ij of full rank it will be solvable by quadrature if and only if for alli j 2 n nt ij n n t jj 1 is a nonpositive integer i e the interactions correspond to the toda systems for the kac moody lie algebras the criterion is also applied to a system of gross neveu

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

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

fingerprint matching by thin plate spline modelling of elastic deformations this paper presents a novel minutiae matching method that describes elastic distortions in fingerprints by means of a thin plate spline model which is estimated using a local and a global matching stage after registration of the fingerprints according to the estimated model the number of matching minutiae can be counted using very tight matching thresholds for deformed fingerprints the algorithm gives considerably higher matching scores compared to rigid matching algorithms while only taking 100 ms on a 1 ghz p iii machine furthermore it is shown that the observed deformations are different from those described by theoretical models proposed in the literature

combining minutiae descriptors for fingerprint matching a novel minutiae based fingerprint matching algorithm is proposed a minutiae matching algorithm has to solve two problems correspondence and similarity computation for the correspondence problem we assign each minutia two descriptors texture based and minutiae based descriptors and use an alignment based greedy matching algorithm to establish the correspondences between minutiae for the similarity computation we extract a 17 d feature vector from the matching result and convert the feature vector into a matching score using support vector classifier the proposed algorithm is tested on fvc2002 databases and compared to all participators in fvc2002 according to equal error rate the proposed algorithm ranks 1st on db3 the most difficult database in fvc2002 and on the average ranks 2nd on all 4 databases

fingerprint matching based on global comprehensive similarity this paper introduces a novel algorithm based on global comprehensive similarity with three steps to describe the euclidean space based relative features among minutiae we first build a minutia simplex that contains a pair of minutiae as well as their associated textures with its transformation variant and invariant relative features employed for the comprehensive similarity measurement and parameter estimation respectively by the second step we use the ridge based nearest neighborhood among minutiae to represent the ridge based relative features among minutiae with these ridge based relative features minutiae are grouped according to their affinity with a ridge the euclidean space based and ridge based relative features among minutiae reinforce each other in the representation of a fingerprint finally we model the relationship between transformation and the comprehensive similarity between two fingerprints in terms of histogram for initial parameter estimation through these steps our experiment shows that the method mentioned above is both effective and suitable for limited memory afis owing to its less than 1k byte template size

robust and efficient ridge based palmprint matching during the past decade many efforts have been made to use palmprints as a biometric modality however most of the existing palmprint recognition systems are based on encoding and matching creases which are not as reliable as ridges this affects the use of palmprints in large scale person identification applications where the biometric modality needs to be distinctive as well as insensitive to changes in age and skin conditions recently several ridge based palmprint matching algorithms have been proposed to fill the gap major contributions of these systems include reliable orientation field estimation in the presence of creases and the use of multiple features in matching while the matching algorithms adopted in these systems simply follow the matching algorithms for fingerprints however palmprints differ from fingerprints in several aspects 1 palmprints are much larger and thus contain a large number of minutiae 2 palms are more deformable than fingertips and 3 the quality and discrimination power of different regions in palmprints vary significantly as a result these matchers are unable to appropriately handle the distortion and noise despite heavy computational cost motivated by the matching strategies of human palmprint experts we developed a novel palmprint recognition system the main contributions are as follows 1 statistics of major features in palmprints are quantitatively studied 2 a segment based matching and fusion algorithm is proposed to deal with the skin distortion and the varying discrimination power of different palmprint regions and 3 to reduce the computational complexity an orientation field based registration algorithm is designed for registering the palmprints into the same coordinate system before matching and a cascade filter is built to reject the nonmated gallery palmprints in early stage the proposed matcher is tested by matching 840 query palmprints against a gallery set of 13 736 palmprints experimental results show that the proposed matcher outperforms the existing matchers a lot both in matching accuracy and speed

a visual shape descriptor using sectors and shape context of contour lines this paper describes a visual shape descriptor based on the sectors and shape context of contour lines to represent the image local features used for image matching the proposed descriptor consists of two component feature vectors first the local region is separated into sectors and their gradient magnitude and orientation values are extracted a feature vector is then constructed from these values second local shape features are obtained using the shape context of contour lines another feature vector is then constructed from these contour lines the proposed approach calculates the local shape feature without needing to consider the edges this can overcome the difficulty associated with textured images and images with ill defined edges the combination of two component feature vectors makes the proposed descriptor more robust to image scale changes illumination variations and noise the proposed visual shape descriptor outperformed other descriptors in terms of the matching accuracy 14 525 better than sift 21 better than pca sift 11 86 better than gloh and 25 66 better than the shape context

classical and quantum mechanical systems of toda lattice type i solutions to the classical periodic and non periodic toda lattice type hamiltonian systems are expressed in terms of an iwasawa type factorization of a large lie group the scattering of these systems is determined in the non periodic case for the generalized periodic toda lattices a generalization of kostant s formula is obtained using standard representations of affine lie groups

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

clean positive operator valued measures in quantum mechanics the statistics of the outcomes of a measuring apparatus is described by a positive operator valued measure povm a quantum channel transforms povms into povms generally irreversibly thus losing some of the information retrieved from the measurement this poses the problem of which povms are undisturbed i e they are not irreversibly connected to another povm we will call such povms clean in a sense the clean povms would be perfect since they would not have any additional extrinsical noise quite unexpectedly it turns out that such a cleanness property is largely unrelated to the convex structure of povms and there are clean povms that are not extremal and vice versa in this article we solve the cleannes classification problem for number n of outcomes n d d dimension of the hilbert space and we provide a set of either necessary or sufficient conditions for n d along with an iff condition for the case of informationally complete povms for n d2

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

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

combining weighted linear project analysis with orientation diffusion for fingerprint orientation field reconstruction this paper presents a novel algorithm for reconstructing the fingerprint orientation field fof the basic idea of the algorithm is to reconstruct the fof by combining weighted linear project analysis with orientation diffusion we first compute the weight values of point gradients according to the similarity of point orientations in the second procedure the qualities of blocks are assessed by the coherence of point orientations and then the block orientation with high quality are estimated by the weighted linear projection analysis based on the vector set of point gradients the fingerprint blocks that will be reconstructed is selected by the priority of them in the third procedure in the end the fof is reconstructed by orientation diffusion based on the reconstruction priorities of blocks to validate the performance the proposed method has been applied to fingerprint enhancement fingerprint singularity detection fingerprint minutiae extraction and fingerprint matching using the fvc2000 and fvc2004 databases the experiments show that the proposed method is more accurate and more reliable and it is more robust against noise

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

detection and characterization of transport barriers in complex flows via ridge extraction of the finite time lyapunov exponent field this paper proposes an algorithm to detect and characterize ridges in the finite time lyapunov exponent ftle field obtained from a continuous dynamical system or flow these ridges represent time dependent separatrices of the flow and are also called lagrangian coherent structures lcs lcs have been demonstrated to be an effective way to analyze realistic time chaotic flows although they can be quite complex therefore in order to exploit the information that lcs can provide it is important to locate and characterize these structures in a systematic way this can be accomplished by interpreting the ftle as a height field and detecting the lcs as ridges of this graph methodologies developed in the image processing framework are integrated with dynamical system inspired approaches in order to characterize ridge strength and location the main novel contribution of the proposed algorithm is a scheme to connect sets of points into curves or surfaces rather than distributions of points around a ridge axis and classify these curves or surfaces using a dynamical systems measure of strength this approach provides the capability to track ranked lcs in space and time the results are presented for a simple analytical model and noisy lcs from realistic three dimensional geophysical fluid data copyright 2010 john wiley sons ltd

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

motion trajectory reproduction from generalized signature description free form motion trajectories prove to be an informative and compact motion clue in sketching long term spatiotemporal motions hence motion trajectories have been used for characterizing human behaviors activities robot actions and other objects movements however it is observed that most of the previous studies merely use motion trajectories straightforwardly in the raw data form which is inflexible as they rely largely on the absolute positions to solve this problem we propose to achieve effective motion trajectory descriptions by developing a systematic trajectory description mechanism to this end a flexible motion trajectory signature descriptor has been proposed in our previous work which can offer generalized descriptions to the raw trajectory data thanks to its rich description invariants moreover for an effective descriptor it is sometimes desired to have mutual description functions i e describing and un describing capability to support some applications like robot learning hence opposite to describing a motion trajectory using the signature this paper focuses on the un describing problem that is reproducing a trajectory instance from a given signature description the moving frame technique is used in formulating the trajectory reproduction method a nonlinear signature matching based metric is also developed to measure the quality of the reproductions experiments are conducted to verify the effectiveness of the trajectory reproduction it is shown that the trajectory signature is flexible and easy to implement in both the description and reproduction of trajectory instances

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

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

fingerprint matching using an orientation based minutia descriptor we introduce a novel fingerprint representation scheme that relies on describing the orientation field of the fingerprint pattern with respect to each minutia detail this representation allows the derivation of a similarity function between minutiae that is used to identify corresponding features and evaluate the resemblance between two fingerprint impressions a fingerprint matching algorithm based on the proposed representation is developed and tested with a series of experiments conducted on two public domain collections of fingerprint images the results reveal that our method can achieve good performance on these data collections and that it outperforms other alternative approaches implemented for comparison

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