convergence rate analysis of a multiplicative schwarz method for variational inequalities this paper derives a linear convergence for the schwarz overlapping domain decomposition method when applied to constrained minimization problems the convergence analysis is based on a minimization approach to the corresponding functional over a convex set a general framework of convergence is established for some multiplicative schwarz algorithm the abstract theory is particularly applied to some obstacle problems which yields a linear convergence for the corresponding schwarz overlapping domain decomposition method of one and two levels numerical experiments are presented to confirm the convergence estimate derived in this paper

a domain decomposition method for a kind of optimization problems in this paper we are concerned with the monotone convergence of a multiplicative method for solving a kind of optimization problems we show that the iterate sequence produced by the method converges to the solution of the problem monotonically

on the asymptotic properties of ldu based tests of the rank of a matrix abstract gill and lewbel recently introduced a test for the rank of a matrix based on the ldu decomposition unfortunately the asymptotic distribution suggested by them is incorrect except in a very limited problem in general the asymptotic distribution is that of a highly complicated nonlinear function of a normally distributed random vector that appears to defy useful characterization the ldu decomposition can be used to produce a valid test asymptotically equivalent to the minimum x 2 test

a central limit theorem for generalized quadratic forms random variables of the form w n mathop sum limits 1 leqq i leqq n mathop sum limits text 1 leqq j leqq n w ijn x i x j are considered with x i independent not necessarily identically distributed and w ijn borel functions such that w ijn x i x j is square integrable and has vanishing conditional expectations e w ijn x i x j x i e w ijn x i x j x j 0 text a text s text a central limit theorem is proved under the condition that the normed fourth moment tends to 3 under some restrictions the condition is also necessary finally conditions on the individual tails of w ijn x i x j and an eigenvalue condition are given that ensure asymptotic normality of w n

discriminative wavelet shape descriptors for recognition of 2 d patterns in this paper we present a set of wavelet moment invariants together with a discriminative feature selection method for the classification of seemingly similar objects with subtle di erences these invariant features are selected automatically based on the discrimination measures defined for the invariant features using a minimum distance classifier our wavelet moment invariants achieved the highest classification rate for all four di erent sets tested compared with zernike s moment invariants and li s moment invariants for a test set consisting of 26 upper cased english letters wavelet moment invariants could obtain 100 classification rate when applied to 26 30 randomly generated noisy and scaled letters whereas zernike s moment invariants and li s moment invariants obtained only 98 7 and 75 3 respectively the theoretical and experimental analyses in this paper prove that the proposed method has the ability to classify many types of image objects and is particularly suitable for classifying seemingly similar objects with subtle di erences 1999 pattern recognition society published by elsevier science ltd all rights reserved

geometric and illumination invariants for object recognition we propose invariant formulations that can potentially be combined into a single system in particular we describe a framework for computing invariant features which are insensitive to rigid motion affine transform changes of parameterization and scene illumination perspective transform and view point change this is unlike most current research on image invariants which concentrates on either geometric or illumination invariants exclusively the formulations are widely applicable to many popular basis representations such as wavelets short time fourier analysis and splines exploiting formulations that examine information about shape and color at different resolution levels the new approach is neither strictly global nor local it enables a quasi localized hierarchical shape analysis which is rarely found in other known invariant techniques such as global invariants furthermore it does not require estimating high order derivatives in computing invariants unlike local invariants whence is more robust we provide results of numerous experiments on both synthetic and real data to demonstrate the validity and flexibility of the proposed framework

the rank of demand systems theory and nonparametric estimation w m gorman s 1981 concept of engel curve rank is extended to apply to any demand system rank is shown to have implications for specification separability and aggregation of demands a simple nonparametric test of rank using engel curve data is described and applied to u s and u k consumer survey data the test employs a new general method for testing the rank of estimated matrices the results are used to assess theoretical and empirical aggregation error in representative consumer models and to explain a representative consumer paradox copyright 1991 by the econometric society

rate of convergence for some constraint decomposition methods for nonlinear variational inequalities some general subspace correction algorithms are proposed for a convex optimization problem over a convex constraint subset one of the nontrivial applications of the algorithms is the solving of some obstacle problems by multilevel domain decomposition and multigrid methods for domain decomposition and multigrid methods the rate of convergence for the algorithms for obstacle problems is of the same order as the rate of convergence for jump coefficient linear elliptic problems in order to analyse the convergence rate we need to decompose a finite element function into a sum of functions from the subspaces and also satisfying some constraints a special nonlinear interpolation operator is introduced for decomposing the functions

weighted max norm estimate of additive schwarz iteration scheme for solving linear complementarity problems in this paper we consider an algebraic additive schwarz iteration scheme for solving the finite dimensional linear complementarity problem that involves an m matrix the scheme contains some existing algorithms as special cases we establish monotone convergence of the iteration scheme under appropriate conditions moreover using the concept of weak regular splitting we estimate weighted max norm bounds for iteration errors thereby we show that the sequence generated by the iteration scheme converges to the unique solution of the problem without any restriction on the initial point

convergence analysis ofgeneralized schwarz algorithms for solving obstacle problems with t monotone operator this paper proves the convergence of some generalized schwarz algorithms for solvingthe obstacle problems with a t monotone operator numerical results show that the generalized schwarz algorithms converge faster than the classical schwarz algorithms

on monotone and geometric convergence of schwarz methods for two sided obstacle problems convergence of schwarz methods is discussed for certain discretizations of two sided obstacle problems monotone and especially geometrical convergence are established if the stiffness matrix is a kind of m matrix and an h independent convergence rate is proved for uniformly overlapping decomposition

convergence rate of a schwarz multilevel method for the constrained minimization of nonquadratic functionals in l badea convergence rate of a multiplicative schwarz method for strongly nonlinear inequalities v barbu i lasiecka d tiba and c varsan eds kluwer academic publishers boston 2003 the convergence of a subspace correction method applied to the constrained minimization of a functional in a general reflexive banach space has been proved provided that the convex set verifies a certain assumption this assumption is weaker than that in which the convex set is decomposed according to the space decomposition as a sum of subsets in the sobolev spaces the proposed method becomes a multiplicative schwarz method for the solution of the variational inequalities coming from the minimization of nonquadratic functionals we prove in this paper that this assumption holds for the one two and multilevel multiplicative schwarz methods in the finite element space and we explicitly write the constants in the error estimations depending on the overlapping and mesh parameters our error estimates are similar with those obtained for the minimization of quadratic functionals in l badea x c tai and j wang siam j numer anal 2003 pp 1052 1073 or with those obtained for the one obstacle problem in x c tai numer math 93 2003 pp 755 786

domain decomposition method for a parabolic variational inequality abstract in this paper we propose a domain decomposition method for the euler galerkin approximation of a parabolic variational inequality it is proved that the convergence rate of the algorithm is independent of the space meshsize h and the time stepsize

generalized reduced rank tests using the singular value decomposition we propose a novel statistic to test the rank of a matrix the rank statistic overcomes deficiencies of existing rank statistics like a kronecker covariance matrix for the canonical correlation rank statistic of anderson annals of mathematical statistics 1951 22 327 351 sensitivity to the ordering of the variables for the ldu rank statistic of cragg and donald journal of the american statistical association 1996 91 1301 1309 and gill and lewbel journal of the american statistical association 1992 87 766 776 a limiting distribution that is not a standard chi squared distribution for the rank statistic of robin and smith econometric theory 2000 16 151 175 usage of numerical optimization for the objective function statistic of cragg and donald journal of econometrics 1997 76 223 250 and ignoring the non negativity restriction on the singular values in ratsimalahelo 2002 rank test based on matrix perturbation theory unpublished working paper u f r science economique university de franche comte in the non stationary cointegration case the limiting distribution of the new rank statistic is identical to that of the johansen trace statistic

a dyadic wavelet affine invariant function for 2d shape recognition dyadic wavelet transform has been used to derive an affine invariant function first an invariant function using two dyadic levels is derived then this invariant function is used to derive another invariant function using six dyadic levels we introduce the wavelet based conic equation the invariant function is based on analyzing the object boundary using the dyadic wavelet transform experimental results on both synthetic and real data are used to demonstrate the discriminating power of the proposed invariant function it has also been compared with some traditional methods the stability of the proposed invariant function is examined in addition the stability under large perspective transformation is tested

wavelet approximation based affine invariant shape representation functions in this paper new wavelet based affine invariant functions for shape representation are presented unlike the previous representation functions only the approximation coefficients are used to obtain the proposed functions one of the derived functions is computed by applying a single wavelet transform the other function is calculated by applying two different wavelet transforms with two different wavelet families one drawback of the previously derived detail based invariant representation functions is that they are sensitive to noise at the finer scale levels which limits the number of scale levels that can be used the experimental results in this paper demonstrate that the proposed functions are more stable and less sensitive to noise than the detail based functions

monotone multigrid methods for elliptic variational inequalities i we derive fast solvers for discrete elliptic variational inequalities of the first kind obstacle problems as resulting from the approximation of related continuous problems by piecewise linear finite elements using basic ideas of successive subspace correction we modify well known relaxation methods by extending the set of search directions extended underrelaxations are called monotone multigrid methods if they are quasioptimal in a certain sense by construction all monotone multigrid methods are globally convergent we take a closer look at two natural variants the standard monotone multigrid method and a truncated version for the considered model problems the asymptotic convergence rates resulting from the standard approach suffer from insufficient coarse grid transport while the truncated monotone multigrid method provides the same efficiency as in the unconstrained case

nonlinear errors in variables estimation of some engel curves abstract the most common solution to the errors in variables problem for the linear regression model is the use of instrumental variable estimation however this methodology cannot be applied in the nonlinear regression framework in this paper we develop consistent estimators for nonlinear regression specifications when errors in variables are present we apply our methodology to estimation of engel curves on household data first we find that the lesser working specification of budget shares regressed on the log of income or expenditure should be generalized to higher order terms in log income also we find that errors in variables in either reported income or expenditure should be accounted for lastly and perhaps most interesting we find rather strong support for the gorman rank restriction on the matrix of coefficients for the polynomial terms in income

multigrid algorithms for variational inequalities we present an iterative scheme for obstacle problems described by variational inequalities at each step of the iteration the method leads to a reduced linear resp nonlinear algebraic system whi

nonparametric estimation of common regressors for similar curve data the paper is concerned with data from a collection of different but related regression curves m j j 1 n n much greater than 1 in statistical practice analysis of such data is most frequently based on low dimensional linear models it is then assumed that each regression curve mj is a linear combination of a small number l much less than n of common functions g 1 g l for example if all m j s are straight lines this holds with l 2 g 1 1 and g 2 x x in this paper the assumption of a prespecified model is dropped a nonparametric method is presented which allows estimation of the smallest l and corresponding functions g 1 g l from the data the procedure combines smoothing techniques with ideas related to principal component analysis an asymptotic theory is presented which yields detailed insight into properties of the resulting estimators an application to household expenditure data illustrates the approach

local rank tests in a multivariate nonparametric relationship abstract consider a multivariate nonparametric model where the unknown vector of functions depends on two sets of explanatory variables for a fixed level of one set of explanatory variables we provide consistent statistical tests called local rank tests to determine whether the multivariate relationship can be explained by a smaller number of functions we also provide estimators for the smallest number of functions called local rank explaining the relationship the local rank tests and the estimators of local rank are defined in terms of the eigenvalues of a kernel based estimator of some matrix the asymptotics of the eigenvalues is established by using the so called fujikoshi expansion along with some techniques of the theory of u statistics we present a simulation study which examines the small sample properties of local rank tests we also apply the local rank tests and the local rank estimators to a demand system given by a newly constructed data set this work can be viewed as a local extension of the tests for a number of factors in a nonparametric relationship introduced by stephen donald

free boundary problems with nonlinear source terms the method of lines is used to semi discretize the non linear poisson equation over a domain with a free boundary the resulting multipoint free boundary problem is solved with a line gauss seidel method which is shown to converge monotonically the method of lines solution is then shown to converge to the continuous solution of the variational inequality form of the obstacle problem some numerical results for the diffusion reaction equation indicate that the method is applicable to more general free boundary problems for nonlinear elliptic equations

a generalization of the schwarz alternating method to an arbitrary number of subdomains we give a generalization of the schwarz alternating method to an arbitrary number of subdomains as applications we prove that the method converges for the main second order boundary value problems as well as for the system of linear elasticity for the dirichlet problems it is proved that the method converges geometrically and some numerical examples are given

geometry related convergence results for domain decomposition algorithms for general second order elliptic partial differential equations the schwarz alternating procedure is proved to converge at a rate independent of the aspect ratio for l shaped t shaped and c shaped domains these results cover both continuous and discrete versions of the schwarz algorithm moreover they apply to the nonoverlapping schur complement algorithms with the preconditioner proposed in chan siam j numer anal 24 1987 pp 382 390 in particular it is shown that the condition number of the preconditioned interface operator is bounded by 2 for all l shaped and t shaped domains this improves similar geometry independent convergence results for the schur complement algorithms obtained previously by chan and resasco advances in computer methods for partial differential equations vi r vichnevetsky and r s stepleman eds pp 317 322

convergence estimates for product iterative methods with applications to domain decomposition in this paper we consider iterative methods for the solution of symmetric positive definite problems on a space which are defined in terms of products of operators defined with respect to a number of subspaces the simplest algorithm of this sort has an error reducing operator which is the product of orthogonal projections onto the complement of the subspaces new normreduction estimates for these iterative techniques will be presented in an abstract setting applications are given for overlapping schwarz algorithms with many subregions for finite element approximation of second order elliptic problems

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

schwarz algorithm for the solution of variational inequalities with nonlinear source terms numerical solution of variational inequalities of obstacle type associated with some free boundary problems with nonlinear source terms is considered iterative methods based on the combination of the domain decomposition technique and the newton like approach are proposed for solving discrete obstacle problems arising from the continuous piecewise linear finite element approximation of the problems the calculation processes are easily to be parallelized the convergence and specially the convergent rate of the algorithms are obtained

an iterative algorithm for a quasivariational inequality system related to hjb equation this paper presents a new iterative algorithm for a quasivariational inequality system related to hjb equation a domain decomposition method based on this algorithm is proposed the convergence theorems have been established

computationally efficient wavelet affine invariant functions for shape recognition an affine invariant function for object recognition is constructed from wavelet coefficients of the object boundary in previous works undecimated dyadic wavelet transform was used to construct affine invariant functions in this paper an algorithm based on decimated wavelet transform is developed to compute an affine invariant function as a result computational complexity is reduced without decreasing recognition performance experimental results are presented

wavelet based affine invariant representation a tool for recognizing planar objects in 3d space a technique is developed to construct a representation of planar objects undergoing a general affine transformation the representation can be used to describe planar or nearly planar objects in a three dimensional space observed by a camera under arbitrary orientations the technique is based upon object contours parameterized by an affine invariant parameter and the dyadic wavelet transform the role of the wavelet transform is the extraction of multiresolution affine invariant features from the affine invariant contour representation a dissimilarity function is also developed and used to distinguish among different object representations this function makes use of the extrema on the representations thus making its computation very efficient a study of the effect of using different wavelet functions and their order or vanishing moments is also carried out experimental results show that the performance of the proposed representation is better than that of other existing methods particularly when objects are heavily corrupted with noise

testing the rank and definiteness of estimated matrices with applications to factor state space and arma models abstract consider any consistent asymptotically normal estimate of an arbitrary rectangular or square matrix a this article derives an explicit test for the rank of a and a related test of semi definiteness of a potential applications include testing for identification of structural models testing for the number of state variables in state space models including tests for the order of autoregressive moving average arma processes consumer demand analysis applications and testing for the number of factors in factor analysis and related procedures the test is based on the gaussian elimination lower diagonal upper triangular ldu decomposition the test is illustrated with an empirical application to testing the order of arma processes

a posteriori error analysis for a class of integral equations and variational inequalities we consider elliptic and parabolic variational equations and inequalities governed by integro differential operators of order 2s in 0 2 our main motivation is the pricing of european or american options under levy processes in particular pure jump processes or jump diffusion processes with tempered stable processes the problem is discretized using piecewise linear finite elements in space and the implicit euler method in time we construct a residual type a posteriori error estimator which gives a computable upper bound for the actual error in h s norm the estimator is localized in the sense that the residuals are restricted to the discrete non contact region numerical experiments illustrate the accuracy of the space and time estimators and show that they can be used to measure local errors and drive adaptive algorithms

on the schwarz alternating method with more than two subdomains for nonlinear monotone problems in a hilbert space an iterative process is defined which generalizes the schwarz alternating method for nonlinear monotone problems to more than two subdomains and the convergence of this process is proved in the case of the problems with the solution in a convex of h 0 1 omega it is proved that the method converges geometrically numerical results are given concerning the problem of elastic plastic torsion of prismatic bars and the problem of seepage of fluids through porous media

affine similar shape retrieval application to multiview 3 d object recognition the feasibility of representing a three dimensional 3 d object with a small number of standard views is studied the object boundary of each view is considered as a two dimensional 2 d shape and is represented by the locations of the maxima of its curvature scale space css image contours the idea is to identify an unknown object from an image taken from a random view by using the stored descriptions of the standard views the css image has been selected for mpeg 7 standardization the maxima of css image have already been used to represent 2 d shapes in different applications under similarity transforms since the new application involves affine transforms we first examine the effects of general affine transforms on the representation and show that the locations of the maxima of the css image do not move dramatically even under large affine transformations our system for shape based retrieval from large image databases is then applied to multiview 3 d object representation and recognition our collection of 3 d objects consists of 18 aircraft of different shapes three silhouette contours corresponding to random views are separately used as input for each object results indicate that robust and efficient 3 d free form object recognition through multiview representation can be achieved using the css representation

analysis of planar shapes using geodesic paths on shape spaces for analyzing shapes of planar closed curves we propose differential geometric representations of curves using their direction functions and curvature functions shapes are represented as elements of infinite dimensional spaces and their pairwise differences are quantified using the lengths of geodesics connecting them on these spaces we use a fourier basis to represent tangents to the shape spaces and then use a gradient based shooting method to solve for the tangent that connects any two shapes via a geodesic using the surrey fish database we demonstrate some applications of this approach 1 interpolation and extrapolations of shape changes 2 clustering of objects according to their shapes 3 statistics on shape spaces and 4 bayesian extraction of shapes in low quality images

convergence rate analysis of an asynchronous space decomposition method for convex minimization we analyze the convergence rate of an asynchronous space decomposition method for constrained convex minimization in a reflexive banach space this method includes as special cases parallel domain decomposition methods and multigrid methods for solving elliptic partial differential equations in particular the method generalizes the additive schwarz domain decomposition methods to allow for asynchronous updates it also generalizes the bpx multigrid method to allow for use as solvers instead of as preconditioners possibly with asynchronous updates and is applicable to nonlinear problems applications to an overlapping domain decomposition for obstacle problems are also studied the method of this work is also closely related to relaxation methods for nonlinear network flow accordingly we specialize our convergence rate results to the above methods the asynchronous method is implementable in a multiprocessor system allowing for communication and computation delays among the processors

generalized schwarz algorithm for obstacle problems abstract in this paper we present so called generalized additive and multiplicative schwarz algorithms for solving the discretization problems of obstacle problems with a self adjoint elliptic operator we establish convergence theorems for the proposed algorithms numerical tests show that a faster convergence rate can be obtained by choosing suitable parameters in the algorithms

inference concerning the number of factors in a multivariate nonparametric relationship this paper considers the problem of determining the number of nonparametric factors in a multivariate nonparametric relationship the definition given is broad enough to encompass a number of potential applications in econometrics including inferring the rank of demand testing whether for a given set of instruments it is possible to identify a linear model and testing arbitrage pricing theory the paper gives methods for testing hypotheses concerning the number of factors using series and kernel based nonparametric methods and also considers the problem of estimating the number of factors the methods are compared in a small simulation study and an application to determining the rank of demand systems is considered

convergence analysis of p1 finite element method for free boundary problems on non overlapping subdomains abstract the paper applies a conforming p1 finite element method to general variational inequalities derived from free boundary problems the domain of the free boundary problem can be properly split into two non overlapping subdomains where the free boundary is located in only one subdomain the variational inequality is reduced to a partial differential equation in the subdomain which does not contain the free boundary but still keeps its original form in the other subdomain therefore the original variational inequality can be discretized separately with p1 finite element in different subdomains a non overlapping domain decomposition method is introduced to solve these two discretized sub problems by p1 finite element method iteratively while a robin type boundary condition is utilized for the data transfer on the common boundary we show that the sequence of such finite element solutions converges to the discretized solution of the original problem application to a free seepage problem verifies the theory

inferring the rank of a matrix abstract this paper considers methods of inference concerning the rank of matrix a based on an asymptotically normal estimate of and some identifiable specification for one such specification is 0 in which case one is interested in the rank of we first propose and examine the properties of a test of the hypothesis that the rank is of a given size against the alternative that the rank is larger we then look at the problem of estimating the rank of this matrix using model selection procedures and sequential hypothesis testing conditions for consistency of such procedures are obtained some economic applications are discussed and the various methods are compared in a monte carlo experiment

an additive schwarz method for variational inequalities this paper proposes an additive schwarz method for variational inequalities and their approximations by finite element methods the schwarz domain decomposition method is proved to converge with a geometric rate depending on the decomposition of the domain the result is based on an framework of convergence analysis established for general variational inequalities in hilbert spaces

a region based fuzzy feature matching approach to content based image retrieval this paper proposes a fuzzy logic approach ufm unified feature matching for region based image retrieval in our retrieval system an image is represented by a set of segmented regions each of which is characterized by a fuzzy feature fuzzy set reflecting color texture and shape properties as a result an image is associated with a family of fuzzy features corresponding to regions fuzzy features naturally characterize the gradual transition between regions blurry boundaries within an image and incorporate the segmentation related uncertainties into the retrieval algorithm the resemblance of two images is then defined as the overall similarity between two families of fuzzy features and quantified by a similarity measure ufm measure which integrates properties of all the regions in the images compared with similarity measures based on individual regions and on all regions with crisp valued feature representations the ufm measure greatly reduces the influence of inaccurate segmentation and provides a very intuitive quantification the ufm has been implemented as a part of our experimental simplicity image retrieval system the performance of the system is illustrated using examples from an image database of about 60 000 general purpose images

quaternionic local ranking binary pattern a local descriptor of color images this paper proposes a local descriptor called quaternionic local ranking binary pattern qlrbp for color images different from traditional descriptors that are extracted from each color channel separately or from vector representations qlrbp works on the quaternionic representation qr of the color image that encodes a color pixel using a quaternion qlrbp is able to handle all color channels directly in the quaternionic domain and include their relations simultaneously applying a clifford translation to qr of the color image qlrbp uses a reference quaternion to rank qrs of two color pixels and performs a local binary coding on the phase of the transformed result to generate local descriptors of the color image experiments demonstrate that the qlrbp outperforms several state of the art methods

a theory of multiscale curvature based shape representation for planar curves a shape representation technique suitable for tasks that call for recognition of a noisy curve of arbitrary shape at an arbitrary scale or orientation is presented the method rests on the describing a curve at varying levels of detail using features that are invariant with respect to transformations that do not change the shape of the curve three different ways of computing the representation are described they result in three different representations the curvature scale space image the renormalized curvature scale space image and the resampled curvature scale space image the process of describing a curve at increasing levels of abstraction is referred to as the evolution or arc length evolution of that curve several evolution and arc length evolution properties of planar curves are discussed

a subspace approach for matching 2d shapes under affine distortions this paper presents a subspace approach to matching a pair of 2d shapes and estimating the affine transformation that aligns the two 2d shapes in the proposed method by considering each shape as a 2d signal one shape is projected onto the subspace spanned by the other and the affine transformation is estimated by minimizing the projection error in the subspace the proposed method is fast easy to implement and with a clear physical interpretation furthermore it is robust to noise due to the merit of the subspace method the proposed approach has been tested for registration accuracy computation time and robustness to noise its performance on synthetic and real images is compared with the state of the art reference algorithms the experimental results show that our approach compares favorably to the reference methods in terms of registration accuracy computation speed and robustness