graph matching based on stochastic perturbation this paper presents a novel perspective on characterizing the spectral correspondence between the nodes of weighted graphs for image matching applications the algorithm is based on the principal feature components obtained by stochastic perturbation of a graph there are three areas of contributions in this paper first a stochastic normalized laplacian matrix of a weighted graph is obtained by perturbing the matrix of a sensed graph model second we obtain the eigenvectors based on an eigen decomposition approach where representative elements of each row of this matrix can be considered to be the feature components of a feature point third correct correspondences are determined in a low dimensional principal feature component space between the graphs in order to further enhance image matching we also exploit the random sample consensus algorithm as a post processing step to eliminate mismatches in feature correspondences the experiments on synthetic and real world images demonstrate the effectiveness and accuracy of the proposed method

a method for registration of 3 d shapes the authors describe a general purpose representation independent method for the accurate and computationally efficient registration of 3 d shapes including free form curves and surfaces the method handles the full six degrees of freedom and is based on the iterative closest point icp algorithm which requires only a procedure to find the closest point on a geometric entity to a given point the icp algorithm always converges monotonically to the nearest local minimum of a mean square distance metric and the rate of convergence is rapid during the first few iterations therefore given an adequate set of initial rotations and translations for a particular class of objects with a certain level of shape complexity one can globally minimize the mean square distance metric over all six degrees of freedom by testing each initial registration one important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model prior to shape inspection experimental results show the capabilities of the registration algorithm on point sets curves and surfaces

arbitrarily tight bounds on differential entropy of gaussian mixtures a sequence of lower and upper bounds is derived on the differential entropy of a gaussian mixture where the gaussian components only differ in mean values as the sequence index increases the computational complexity of the bounds increases however the gap between the lower and upper bounds becomes vanishingly small we address the applications of these bounds in several communication scenarios where the transmitters utilize pulse amplitude modulation pam constellations to transmit data

convergence analysis of hybrid projection with cesaro mean method for the split equilibrium and general system of finite variational inequalities in this paper we present a new iterative algorithm for finding a common element of three solution sets i split equilibrium problem ii general system of finite variational inequality problem and iii fixed point problem this algorithm is modified by hybrid method based on cesaro mean in real hilbert spaces furthermore a strong convergent theorem is established and this theorem is the generalization of many previously known results in this research area finally we study the rate of convergence of the iterative algorithm and some illustrative numerical examples are presented

globally consistent correspondence of multiple feature sets using proximal gauss seidel relaxation feature correspondence between two or more images is a fundamental problem towards many computer vision applications the case of correspondence between two images has been intensively studied however few works so far have been concerned with multi image correspondence in this paper we address the problem of establishing a globally consistent correspondence among multiple more than two feature sets given the pairwise feature affinity information our main contribution is to propose a novel optimization framework for solving this problem based on the so called proximal gauss seidel relaxation pgsr the proposed method is distinguished from previous works mainly in three aspects 1 it is more robust to noise and outliers 2 its solution is based on convex relaxation and the principled pgsr method which in general has convergence guarantee 3 the scale of the problem in our method is linear with respect to the number of feature sets making it computationally practical to be used in real world applications experimental results both synthetic and real image datasets have demonstrated the effectiveness and superiority of the proposed method highlightswe study finding globally consistent correspondence from multiple feature sets a novel method is proposed based on proximal gauss seidel relaxation pgsr pgsr is more robust to noise outliers and deformation than competitors the optimization has general convergence guarantee the scale of our formulation is linear w r t the number of feature sets

on some inequalities for the incomplete gamma function let p 1 be a positive real number we determine all real numbers p and p such that the inequalities formula math formula math are valid for all x 0 and we determine all real numbers a and b such that log 1 e ax x e t t log 1 e bx hold for all 0

strong convergence to common fixed points of families of nonexpansive mappings abstract we introduce iteration schemes for families of nonexpansive mappings in hilbert spaces and prove that the iterates converge strongly to common fixed points of the mappings

global minimax approximations and bounds for the gaussian q function by sums of exponentials this paper presents a novel systematic methodology to obtain new simple and tight approximations lower bounds and upper bounds for the gaussian q function and functions thereof in the form of a weighted sum of exponential functions they are based on minimizing the maximum absolute or relative error resulting in globally uniform error functions with equalized extrema in particular we construct sets of equations that describe the behaviour of the targeted error functions and solve them numerically in order to find the optimized sets of coefficients for the sum of exponentials this also allows for establishing a trade off between absolute and relative error by controlling weights assigned to the error functions extrema we further extend the proposed procedure to derive approximations and bounds for any polynomial of the q function which in turn allows approximating and bounding many functions of the q function that meet the taylor series conditions and consider the integer powers of the q function as a special case in the numerical results other known approximations of the same and different forms as well as those obtained directly from quadrature rules are compared with the proposed approximations and bounds to demonstrate that they achieve increasingly better accuracy in terms of the global error thus requiring significantly lower number of sum terms to achieve the same level of accuracy than any reference approach of the same form

using spin images for efficient object recognition in cluttered 3d scenes we present a 3d shape based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion recognition is based on matching surfaces by matching points using the spin image representation the spin image is a data level shape descriptor that is used to match surfaces represented as surface meshes we present a compression scheme for spin images that results in efficient multiple object recognition which we verify with results showing the simultaneous recognition of multiple objects from a library of 20 models furthermore we demonstrate the robust performance of recognition in the presence of clutter and occlusion through analysis of recognition trials on 100 scenes

robust point matching via vector field consensus in this paper we propose an efficient algorithm called vector field consensus for establishing robust point correspondences between two sets of points our algorithm starts by creating a set of putative correspondences which can contain a very large number of false correspondences or outliers in addition to a limited number of true correspondences inliers next we solve for correspondence by interpolating a vector field between the two point sets which involves estimating a consensus of inlier points whose matching follows a nonparametric geometrical constraint we formulate this a maximum a posteriori map estimation of a bayesian model with hidden latent variables indicating whether matches in the putative set are outliers or inliers we impose nonparametric geometrical constraints on the correspondence as a prior distribution using tikhonov regularizers in a reproducing kernel hilbert space map estimation is performed by the em algorithm which by also estimating the variance of the prior model initialized to a large value is able to obtain good estimates very quickly e g avoiding many of the local minima inherent in this formulation we illustrate this method on data sets in 2d and 3d and demonstrate that it is robust to a very large number of outliers even up to 90 we also show that in the special case where there is an underlying parametric geometrical model e g the epipolar line constraint that we obtain better results than standard alternatives like ransac if a large number of outliers are present this suggests a two stage strategy where we use our nonparametric model to reduce the size of the putative set and then apply a parametric variant of our approach to estimate the geometric parameters our algorithm is computationally efficient and we provide code for others to use it in addition our approach is general and can be applied to other problems such as learning with a badly corrupted training data set

non rigid visible and infrared face registration via regularized gaussian fields criterion registration of multi sensor data particularly visible color sensors and infrared sensors is a prerequisite for multimodal image analysis such as image fusion typically the relationships between image pairs are modeled by rigid or affine transformations however this cannot produce accurate alignments when the scenes are not planar for example face images in this paper we propose a regularized gaussian fields criterion for non rigid registration of visible and infrared face images the key idea is to represent an image by its edge map and align the edge maps by a robust criterion with a non rigid model we model the transformation between images in a reproducing kernel hilbert space and a sparse approximation is applied to the transformation to avoid high computational complexity moreover a coarse to fine strategy by applying deterministic annealing is used to overcome local convergence problems the qualitative and quantitative comparisons on two publicly available databases demonstrate that our method significantly outperforms the state of the art method with an affine model as a result our method will be beneficial for fusion based face recognition highlightswe analyze the robustness of gaussian fields criterion both in theory and experiment the gaussian fields criterion is generalized from rigid to the non rigid case we propose a new non rigid registration method to deal with more real world problems a sparse approximation on the transformation is applied to speed up the method we customize and apply the proposed method to visible thermal ir face registration

efficient computation of erfc x for large arguments a new infinite series representation for the error function is developed it is especially suitable for computing erfc x for large x for instance for any x spl ges 4 the error function can be evaluated with a relative error less than 10 sup 10 by using only eight terms similarly the error function can be evaluated with a relative error less than 8 spl times 10 sup 7 for any x spl ges 2 using just six terms an analytical bound is derived to show that the total error due to series truncation and undersampling rapidly decreases as x increases comparisons with two other series are provided

icp registration using invariant features investigates the use of euclidean invariant features in a generalization of iterative closest point icp registration of range images pointwise correspondences are chosen as the closest point with respect to a weighted linear combination of positional and feature distances it is shown that under ideal noise free conditions correspondences formed using this distance function are correct more often than correspondences formed using the positional distance alone in addition monotonic convergence to at least a local minimum is shown to hold for this method when noise is present a method that automatically sets the optimal relative contribution of features and positions is described this method trades off the error in feature values due to noise against the error in positions due to misalignment experimental results suggest that using invariant features decreases the probability of being trapped in a local minimum and may be an effective solution for difficult range image registration problems where the scene is very small compared to the model

versatile accurate and analytically tractable approximation for the gaussian q function the existing approximations for the gaussian q function have been developed bearing in mind applications that require high estimation accuracies e g derivation of the error probability for digital modulation schemes unfortunately the associated mathematical expressions are too complex to be easily employed in many other analytical studies even if they do not require such a high accuracy this letter proposes a simple yet accurate mathematical approximation to the gaussian q function when compared to other existing approximations the proposed model provides an adequate balance between accuracy and analytical tractability its simplicity enables its application over a wider range of analytical studies at reasonable accuracy levels as an illustrative example the proposed approximation is employed to obtain a new and simple closed form expression for the probability of detection of an energy detector under rayleigh fading channels

a fast algorithm for learning a ranking function from large scale data sets we consider the problem of learning a ranking function that maximizes a generalization of the wilcoxon mann whitney statistic on the training data relying on an e accurate approximation for the error function we reduce the computational complexity of each iteration of a conjugate gradient algorithm for learning ranking functions from o m2 to o m where m is the number of training samples experiments on public benchmarks for ordinal regression and collaborative filtering indicate that the proposed algorithm is as accurate as the best available methods in terms of ranking accuracy when the algorithms are trained on the same data however since it is several orders of magnitude faster than the current state of the art approaches it is able to leverage much larger training data sets

robust l_ 2 e estimation of transformation for non rigid registration we introduce a new transformation estimation algorithm using the l 2 e estimator and apply it to non rigid registration for building robust sparse and dense correspondences in the sparse point case our method iteratively recovers the point correspondence and estimates the transformation between two point sets feature descriptors such as shape context are used to establish rough correspondence we then estimate the transformation using our robust algorithm this enables us to deal with the noise and outliers which arise in the correspondence step the transformation is specified in a functional space more specifically a reproducing kernel hilbert space in the dense point case for nonrigid image registration our approach consists of matching both sparsely and densely sampled sift features and it has particular advantages in handling significant scale changes and rotations the experimental results show that our approach greatly outperforms state of the art methods particularly when the data contains severe outliers

very simple tight bounds on the q function we present new lower and upper bounds on the gaussian q function unified in a single and simple algebraic expression which contains only two exponential terms with a constant and a rational coefficient respectively lower and upper bounding properties are obtained from such unified expression by selecting the coefficients accordingly despite the remarkable simplicity the bounds are found to be as tight as multi term alternatives obtained e g from the exponential 2 and jensen cotes 3 families of bounds a corollary result is that the n th integer power of q x can also be tightly bounded both below and above with only n 1 algebraic terms in addition to offering remarkable accuracy and mathematical tractability combined the new bounds are very consistent in which both lower and upper counterparts are similarly tight over the entire domain

comparison of texture features based on gabor filters texture features that are based on the local power spectrum obtained by a bank of gabor filters are compared the features differ in the type of nonlinear post processing which is applied to the local power spectrum the following features are considered gabor energy complex moments and grating cell operator features the capability of the corresponding operators to produce distinct feature vector clusters for different textures is compared using two methods the fisher 1923 criterion and the classification result comparison both methods give consistent results the grating cell operator gives the best discrimination and segmentation results the texture detection capabilities of the operators and their robustness to nontexture features are also compared the grating cell operator is the only one that selectively responds only to texture and does not give false response to nontexture features such as object contours

jensen cotes upper and lower bounds on the gaussian q function and related functions we present new families of lower and upper bounds on q functions first we consider the craig form of the gaussian q function q x and shown that its integrand phi thetas x can be partitioned into a pair of complementary convex and concave segments this property is then exploited in conjunction with the jensen inequality and the newton cotes quadrature rule to produce a complete family of upper and lower bounds on q x which can be made arbitrarily tight by finer segmentation the basic idea is then utilized to derive families of upper and lower bounds also for the squared gaussian q function q2 x the 2d joint gaussian q function q x y p and the generalized marcum q function qm x y the bounds are shown to be tighter than alternatives found in the literature and in some cases the lower bounds provided find no equivalent in current literature the generality of the principle is the elegant point of the method and the resulting jensen cotes bounds are easy to implement and evaluate since only elementary transcendental functions are involved as an example of application to the analysis of communication systems we consider the bit error rates ber s of decode and forward df cooperative relaying schemes with coherent and differential phase shift keying psk modulations which have been shown to have an intricate dependence on the gaussian q function complicated by crossproducts irrational functional arguments and multiple numerical integrations in that example the bounds substantially reduce the complexity required to evaluate the expressions retaining tightness despite multiple numerical integrations with infinite limits

alignment by maximization of mutual information a new information theoretic approach is presented for finding the pose of an object in an image the technique does not require information about the surface properties of the object besides its shape and is robust with respect to variations of illumination in our derivation few assumptions are made about the nature of the imaging process as a result the algorithms are quite general and may foreseeably be used in a wide variety of imaging situations experiments are presented that demonstrate the approach registering magnetic resonance mr images aligning a complex 3d object model to real scenes including clutter and occlusion tracking a human head in a video sequence and aligning a view based 2d object model to real images the method is based on a formulation of the mutual information between the model and the image as applied here the technique is intensity based rather than feature based it works well in domains where edge or gradient magnitude based methods have difficulty yet it is more robust than traditional correlation additionally it has an efficient implementation that is based on stochastic approximation

non rigid point set registration by preserving global and local structures in previous work on point registration the input point sets are often represented using gaussian mixture models and the registration is then addressed through a probabilistic approach which aims to exploit global relationships on the point sets for non rigid shapes however the local structures among neighboring points are also strong and stable and thus helpful in recovering the point correspondence in this paper we formulate point registration as the estimation of a mixture of densities where local features such as shape context are used to assign the membership probabilities of the mixture model this enables us to preserve both global and local structures during matching the transformation between the two point sets is specified in a reproducing kernel hilbert space and a sparse approximation is adopted to achieve a fast implementation extensive experiments on both synthesized and real data show the robustness of our approach under various types of distortions such as deformation noise outliers rotation and occlusion it greatly outperforms the state of the art methods especially when the data is badly degraded

regularized vector field learning with sparse approximation for mismatch removal in vector field learning regularized kernel methods such as regularized least squares require the number of basis functions to be equivalent to the training sample size n the learning process thus has o n 3 and o n 2 in the time and space complexity respectively this poses significant burden on the vector learning problem for large datasets in this paper we propose a sparse approximation to a robust vector field learning method sparse vector field consensus sparsevfc and derive a statistical learning bound on the speed of the convergence we apply sparsevfc to the mismatch removal problem the quantitative results on benchmark datasets demonstrate the significant speed advantage of sparsevfc over the original vfc algorithm two orders of magnitude faster without much performance degradation we also demonstrate the large improvement by sparsevfc over traditional methods like ransac moreover the proposed method is general and it can be applied to other applications in vector field learning

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

inequalities for the generalized marcum q function abstract in this paper we consider the generalized marcum q function of order 0 real defined by q a b 1 a 1 b t e t 2 a 2 2 i 1 at d t where a b 0 i stands for the modified bessel function of the first kind and the right hand side of the above equation is replaced by its limiting value when a 0 our aim is to prove that the function q a b is strictly increasing on 0 for each a 0 b 0 and to deduce some interesting inequalities for the function q moreover we present a somewhat new viewpoint of the generalized marcum q function by showing that satisfies the new is better than used nbu property which arises in economic theory

gaussian fields a new criterion for 3d rigid registration abstract this paper introduces a new and simple criterion for rigid registration based on gaussian fields the criterion is always differentiable and convex in a large neighborhood of the alignment parameters allowing for the use of well proven optimization techniques using this method we can extend the size of the region of convergence so that no close initialization is needed thus overcoming local convergence problems of iterative closest point algorithms furthermore the gaussian energy function can be evaluated with linear complexity using the fast gauss transform which permits efficient implementation of the registration algorithm experimental analysis on real world data sets shows the usefulness and points the limits of the approach

robust non rigid point set registration using spatially constrained gaussian fields estimating transformations from degraded point sets is necessary for many computer vision and pattern recognition applications in this paper we propose a robust non rigid point set registration method based on spatially constrained context aware gaussian fields we first construct a context aware representation e g shape context for assignment initialization then we use a graph laplacian regularized gaussian fields to estimate the underlying transformation from the likely correspondences on the one hand the intrinsic manifold is considered and used to preserve the geometrical structure and a priori knowledge of the point set is extracted on the other hand by using the deterministic annealing the presented method is extended to a projected high dimensional feature space i e reproducing kernel hilbert space through a kernel trick to solve the transformation in which the local structure is propagated by the coarse to fine scaling strategy in this way the proposed method gradually recovers much more correct correspondences and then estimates the transformation parameters accurately and robustly when facing degradations experimental results on 2d and 3d synthetic and real data point sets demonstrate that the proposed method reaches better performance than the state of the art algorithms

downlink average rate and sinr distribution in cellular networks the statistical characteristics of the signal to interference plus noise ratio sinr are closely related to many performance metrics of cellular networks in this paper the downlink average rate and sinr distribution are studied for orthogonal frequency division multiple access ofdma based cellular networks subject to the distance dependent path loss and shadow fading sf with the analytical approximate mean and moment generating function mgf of the sinr in the logarithmic domain the closed form approximation for the lower and upper bounds of the average rate is obtained then the distribution of the sinr in the logarithmic domain is proposed to be approximated as the normal inverse gaussian nig distribution whose parameters are computed explicitly through moment matching also the closed form expression for the cumulative distribution function cdf of the sinr based on the nig approximation is derived simulation results not only verify the tightness of the bounds but also show that the nig approximation is up to one order of magnitude more accurate than the pearson type iv approximation and at least one order of magnitude more accurate than the lognormal approximation when the sf correlation coefficient is small or the standard deviations of the sf are large or different

multisource image fusion method using support value transform with the development of numerous imaging sensors many images can be simultaneously pictured by various sensors however there are many scenarios where no one sensor can give the complete picture image fusion is an important approach to solve this problem and produces a single image which preserves all relevant information from a set of different sensors in this paper we proposed a new image fusion method using the support value transform which uses the support value to represent the salient features of image this is based on the fact that in support vector machines svms the data with larger support values have a physical meaning in the sense that they reveal relative more importance of the data points for contributing to the svm model the mapped least squares svm mapped ls svm is used to efficiently compute the support values of image the support value analysis is developed by using a series of multiscale support value filters which are obtained by filling zeros in the basic support value filter deduced from the mapped ls svm to match the resolution of the desired level compared with the widely used image fusion methods such as the laplacian pyramid discrete wavelet transform methods the proposed method is an undecimated transform based approach the fusion experiments are undertaken on multisource images the results demonstrate that the proposed approach is effective and is superior to the conventional image fusion methods in terms of the pertained quantitative fusion evaluation indexes such as quality of visual information qab f the mutual information etc

gaussian field consensus a robust nonparametric matching method for outlier rejection abstract in this paper we propose a robust method called gaussian field consensus gfc for outlier rejection from given putative point set matching correspondences finding correct correspondences inliers is a key component in many computer vision and pattern recognition tasks and the goal of outlier mismatch rejection is to fit the transformation function that maps one feature point set to another our gfc starts by inputting a putative correspondence set which is contaminated by many outliers and the main task of our gfc is to identify the underlying true correspondences from outliers then we formulate this challenging problem by gaussian field nonparametric matching model which bases on the exponential distance loss and kernel method in a reproducing kernel hilbert space next we introduce a local linear constraint based on the regularization theory to preserve the topological structure of the feature points moreover the sparse approximation is used to reduce the search space in this way we can handle a large number of points easily finally we test the gfc method on several real image datasets in the presence of outliers where the experimental results show that our proposed method outperforms current state of the art methods in most test scenarios

point set registration coherent point drift point set registration is a key component in many computer vision tasks the goal of point set registration is to assign correspondences between two sets of points and to recover the transformation that maps one point set to the other multiple factors including an unknown nonrigid spatial transformation large dimensionality of point set noise and outliers make the point set registration a challenging problem we introduce a probabilistic method called the coherent point drift cpd algorithm for both rigid and nonrigid point set registration we consider the alignment of two point sets as a probability density estimation problem we fit the gaussian mixture model gmm centroids representing the first point set to the data the second point set by maximizing the likelihood we force the gmm centroids to move coherently as a group to preserve the topological structure of the point sets in the rigid case we impose the coherence constraint by reparameterization of gmm centroid locations with rigid parameters and derive a closed form solution of the maximization step of the em algorithm in arbitrary dimensions in the nonrigid case we impose the coherence constraint by regularizing the displacement field and using the variational calculus to derive the optimal transformation we also introduce a fast algorithm that reduces the method computation complexity to linear we test the cpd algorithm for both rigid and nonrigid transformations in the presence of noise outliers and missing points where cpd shows accurate results and outperforms current state of the art methods

hybrid algorithm for generalized mixed equilibrium problems and variational inequality problems and fixed point problems in this paper we introduce a new iterative algorithm by hybrid method for finding a common element of the set of solutions of finite general mixed equilibrium problems and the set of solutions of a general variational inequality problem for finite inverse strongly monotone mappings and the set of common fixed points of infinite family of strictly pseudocontractive mappings in a real hilbert space then we prove strong convergence of the scheme to a common element of the three above described sets our result improves and extends the corresponding results announced by many others

chernoff type bounds for the gaussian error function we study single term exponential type bounds also known as chernoff type bounds on the gaussian error function this type of bound is analytically the simplest such that the performance metrics in most fading channel models can be expressed in a concise closed form we derive the conditions for a general single term exponential function to be an upper or lower bound on the gaussian error function we prove that there exists no tighter single term exponential upper bound beyond the chernoff bound employing a factor of one half regarding the lower bound we prove that the single term exponential lower bound of this letter outperforms previous work numerical results show that the tightness of our lower bound is comparable to that of previous work employing eight exponential terms

some new twists to problems involving the gaussian probability integral using an alternate form of the gaussian probability integral discovered a number of years ago it is shown that the solution to a number of previously considered communication problems can be simplified and in some cases made more accurate i e exact rather than bounded these problems include the evaluation of 1 the bit error probability of uncoded phase shift keying psk with costas loop tracking 2 word error probability of antipodal modulation in the presence of fading 3 bit error probability of coded m ary psk mpsk over the memoryless fading channel with given channel state information 4 conditional symbol error probability of mpsk in the presence of carrier synchronization error and 5 the average error probability for the binary additive white gaussian noise awgn intersymbol interference channel also obtained is a generalization of this new alternate form to the case of a two dimensional gaussian probability integral with arbitrary correlation which can be used to evaluate the symbol error probability of mpsk with i q unbalance

the evaluation of error probabilities for intersymbol and cochannel interference an efficient series that can be used to calculate the probability of error in a binary symmetric channel with intersymbol interference and additive noise is derived the series is derived by representing the noise complementary probability distribution function by an exact or approximate fourier series bounds on the accuracy of the estimate are derived for gaussian noise examples show that only a small numerical effort may be required to compute error probabilities of interest using the series applications to both finite and infinite intersymbol interference systems are discussed a similar technique is used to derive series representations for the probability of error of additive noise channels with cochannel interference or with combined intersymbol and cochannel interferences the accuracy of the results is bounded for gaussian noise

robust point set registration using gaussian mixture models in this paper we present a unified framework for the rigid and nonrigid point set registration problem in the presence of significant amounts of noise and outliers the key idea of this registration framework is to represent the input point sets using gaussian mixture models then the problem of point set registration is reformulated as the problem of aligning two gaussian mixtures such that a statistical discrepancy measure between the two corresponding mixtures is minimized we show that the popular iterative closest point icp method and several existing point set registration methods in the field are closely related and can be reinterpreted meaningfully in our general framework our instantiation of this general framework is based on the the l2 distance between two gaussian mixtures which has the closed form expression and in turn leads to a computationally efficient registration algorithm the resulting registration algorithm exhibits inherent statistical robustness has an intuitive interpretation and is simple to implement we also provide theoretical and experimental comparisons with other robust methods for point set registration

an infinite series for the computation of the complementary probability distribution function of a sum of independent random variables and its application to the sum of rayleigh random variables the properties of the series are studied for both bounded and unbounded random variables the technique is used to find efficient series for computation of the distributions of sums of uniform random variables and sums of rayleigh random variables a useful closed form expression for the characteristic function of a rayleigh random variable is presented and an efficient method for computing a confluent hypergeometric function is given an infinite series for the probability density function of a sum of independent random variables is also derived the inversion of characteristic functions a trapezoidal rule for numerical integration and the sampling theorem in the frequency domain are related to and interpreted in terms of the results

analytic expressions and bounds for special functions and applications in communication theory this paper is devoted to the derivation of novel analytic expressions and bounds for a family of special functions that are useful in wireless communication theory these functions are the well known nuttall q function incomplete toronto function rice ie function and incomplete lipschitz hankel integrals capitalizing on the offered results useful identities are additionally derived between the above functions and humbert phi 1 function as well as for specific cases of the kampede feriet function these functions can be considered as useful mathematical tools that can be employed in applications relating to the analytic performance evaluation of modern wireless communication systems such as cognitive radio cooperative and free space optical communications as well as radar diversity and multiantenna systems as an example new closed form expressions are derived for the outage probability over nonlinear generalized fading channels namely alpha eta mu alpha lambda mu and alpha kappa mu as well as for specific cases of the eta mu and lambda mu fading channels furthermore simple expressions are presented for the channel capacity for the truncated channel inversion with fixed rate and corresponding optimum cutoff signal to noise ratio for single antenna and multiantenna communication systems over rician fading channels the accuracy and validity of the derived expressions is justified through extensive comparisons with respective numerical results

on learning vector valued functions in this letter we provide a study of learning in a hilbert space of vectorvalued functions we motivate the need for extending learning theory of scalar valued functions by practical considerations and establish some basic results for learning vector valued functions that should prove useful in applications specifically we allow an output space y to be a hilbert space and we consider a reproducing kernel hilbert space of functions whose values lie in y in this setting we derive the form of the minimal norm interpolant to a finite set of data and apply it to study some regularization functionals that are important in learning theory we consider specific examples of such functionals corresponding to multiple output regularization networks and support vector machines for both regression and classification finally we provide classes of operator valued kernels of the dot product and translation invariant type

removing mismatches for retinal image registration via multi attribute driven regularized mixture model in order to address the problem of retinal image registration this paper proposes and analyzes a novel and general matching algorithm called multi attribute driven regularized mixture model mad rmm mismatches removal can play a key role in image registration which refers to establish reliable matches between two point sets here the presented approach starts from multi feature attributes which are used to guide the feature matching to identify inliers correct matches from outliers incorrect matches and then estimates the spatial transformation in this paper motivated by the problem of feature matching that the initial correspondence is always contaminated by outliers thereby we formulate this issue as a probability deformable mixture model which consists of gaussian components for inliers and uniform components for outliers moreover the algorithm takes full advantage of using multiple attributes for better general matching performance here we are assuming all inliers are mapped into a high dimensional feature space namely reproducing kernel hilbert space rkhs and the closed form solution to the mapping function is given by the representation theorem with l2 norm regularization under the expectation maximization em algorithm finally we evaluate the performance of the algorithm by applying it to retinal image registration on several datasets where experimental results demonstrate that the mad rmm outperforms current state of the art methods and shows the robustness to outliers on real retinal images

a sharp polya based approximation to the normal cumulative distribution function abstract we study an expansion of the cumulative distribution function of the standard normal random variable that results in a family of closed form approximations that converge at 0 one member of the family that has only five explicit constants offers the absolute error of 5 79 10 6 across the entire range of real numbers with its simple form and applicability for all real numbers our approximation surpasses either in computational efficiency or in relative error and most often in both other approximation formulas based on numerical algorithms or ad hoc approximations an extensive overview and classification of the existing approximations from the literature is included

mills ratio monotonicity patterns and functional inequalities in this paper we study the monotonicity properties of some functions involving the mills ratio of the standard normal law from these we deduce some new functional inequalities involving the mills ratio and we show that the mills ratio is strictly completely monotonic at the end of this paper we present some turan type inequalities for mills ratio