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

a simple two sample test in high dimensions based on l2 norm testing the equality of two means is a fundamental inference problem for high dimensional data the hotelling s t2 test either performs poorly or becomes inapplicable several modifications have b

computational aspects of cui freeden statistics for equidistribution on the sphere abstract in this paper we derive the asymptotic statistical properties of a class of generalized discrepancies introduced by cui and freeden siam j sci comput 1997 to test equidistribution on the sphere we show that they have highly desirable properties and encompass several statistics already proposed in the literature in particular it turns out that the limiting distribution is an infinite weighted sum of chi squared random variables issues concerning the approximation of this distribution are considered in detail and explicit bounds for the approximation error are given the statistics are then applied to assess the equidistribution of hammersley low discrepancy sequences on the sphere and the uniformity of a dataset concerning magnetic orientations

an investigation of the nature of parameterization for the hough transform a novel parameterization method for the hough transform is reported instead of the conventional non parametric form the parametric form is used and copies of the transformed shape are plotted on two dimensional slices of the hough space it is shown that the corresponding parameterization has uniform precision with respect to translation and cancels out the quantization uncertainty due to image digitization a problem of the hough transform is discovered which is due to non uniform discretized voting it is shown that the above class of parameterizations avoids the problem finally a particular solution of the parameterization scheme is described which is called the fourier parameterization it is shown that the parameterization has uniform precision with respect to the affine transformation

fundamental principles on learning new features for effective dense matching in dense matching including stereo matching and optical flow nearly all existing approaches are based on simple features such as gray or rgb color gradient or simple transformations like census to calculate matching costs these features do not perform well in complex scenes that may involve radiometric changes noises overexposure and or textureless regions various problems may appear such as wrong matching at the pixel or region level flattening breaking of edges and or even entire structural collapse in this paper we propose two fundamental principles based on the consistency and the distinctiveness of features we show that almost all existing problems in dense matching are caused by features that violate one or both of these principles to systematically learn good features for dense matching we develop a general multi objective optimization based on these two principles and apply convolutional neural networks to find new features that lie on the pareto frontier by using two frame optical flow and stereo matching as applications our experimental results show that the features learned can significantly improve the performance of state of the art approaches based on the kitti benchmarks our method ranks first on the two stereo benchmarks and is the best among existing two frame optical flow algorithms on flow benchmarks

semiparametric maximum likelihood estimation exploiting gene environment independence in case control studies we consider the problem of maximum likelihood estimation in case control studies of gene environment associations with disease when genetic and environmental exposures can be assumed to be independent in the underlying population traditional logistic regression analysis may not be efficient in this setting we study the semiparametric maximum likelihood estimates of logistic regression parameters that exploit the gene environment independence assumption and leave the distribution of the environmental exposures to be nonparametric we use a profile likelihood technique to derive a simple algorithm for obtaining the estimator and we study the asymptotic theory the results are extended to situations where genetic and environmental factors are independent conditional on some other factors simulation studies investigate small sample properties the method is illustrated using data from a case control study designed to investigate the interplay of brca1 2 mutations and oral contraceptive use in the aetiology of ovarian cancer

adaptive support weight approach for correspondence search we present a new window based method for correspondence search using varying support weights we adjust the support weights of the pixels in a given support window based on color similarity and geometric proximity to reduce the image ambiguity our method outperforms other local methods on standard stereo benchmarks

unsupervised feature selection based on maximum information and minimum redundancy for hyperspectral images unsupervised feature selection plays an important role in hyperspectral image processing it is a very challenge issue to select an effective feature subset with the unavailability of class labels to select the features maximally preserving the information of original features a maximum joint mutual information mjmi criterion is defined since the high order distribution involved in mjmi is hard to calculate a maximum information and minimum redundancy mimr criterion is derived as the low order approximation of mjmi from information theory many classical unsupervised feature selection criteria can also be considered as the low order approximations of mjmi compared with them mimr requires more relaxed approximation condition moreover a new clonal selection algorithm csa in artificial immune system is devised to optimize the selected features with the guidance of mimr experimental results on several hyperspectral datasets demonstrate that the proposed method obtains better feature subsets compared with classical unsupervised feature selection methods a new mimr criterion is proposed for unsupervised feature selection mimr can select more informative and distinctive features many classical criteria and mimr can be unified into the same framework the theoretical advantage of mimr over many classical criteria is given feature selection problem is solved by combinatorial optimization with a new csa

fuzzy hough transform and an mlp with fuzzy input output for character recognition abstract a neuro fuzzy system for character recognition using a fuzzy hough transform technique is presented in this paper for each character pattern membership values are determined for a number of fuzzy sets defined on the standard hough transform accumulator cells these basic fuzzy sets are combined by t norms to synthesize additional fuzzy sets whose heights form an n dimensional feature vector for the pattern a 3n dimensional fuzzy linguistic vector is generated from the n dimensional feature vector by defining three linguistic fuzzy sets namely weak moderate and strong the linguistic set membership functions are derived from the butterworth polynomials and are similar to the gain functions of low pass band pass and high pass filters respectively a multilayer perceptron mlp is trained with the fuzzy linguistic vectors by the back propagation of errors the mlp outputs represent fuzzy sets denoting similarity of an input feature vector to a number of character pattern classes recognition accuracy of the system is more than 98

a hierarchical symmetric stereo algorithm using dynamic programming in this paper a new hierarchical stereo algorithm is presented the algorithm matches individual pixels in corresponding scanlines by minimizing a cost function several cost functions are compared the algorithm achieves a tremendous gain in speed and memory requirements by implementing it hierarchically the images are downsampled an optimal number of times and the disparity map of a lower level is used as offset disparity map at a higher level an important contribution consists of the complexity analysis of the algorithm it is shown that this complexity is independent of the disparityrange this result is also used to determine the optimal number of downsample levels this speed gain results in the ability to use more complex compute intensive cost functions that deliver high quality disparity maps another advantage of this algorithm is that cost functions can be chosen independent of the optimisation algorithm the algorithm in this paper is symmetric i e exactly the same matches are found if left and right image are swapped finally the algorithm was carefully implemented so that a minimal amount of memory is used it has proven its efficiency on large images with a high disparity range as well as its quality examples are given in this paper

two stage testing procedures with independent filtering for genome wide gene environment interaction several two stage multiple testing procedures have been proposed to detect gene environment interaction in genome wide association studies in this article we elucidate general conditions that are required for validity and power of these procedures and we propose extensions of two stage procedures using the case only estimator of gene treatment interaction in randomized clinical trials we develop a unified estimating equation approach to proving asymptotic independence between a filtering statistic and an interaction test statistic in a range of situations including marginal association and interaction in a generalized linear model with a canonical link we assess the performance of various two stage procedures in simulations and in genetic studies from women s health initiative clinical trials

feature augmentation for learning confidence measure in stereo matching confidence estimation is essential for refining stereo matching results through a post processing step this problem has recently been studied using a learning based approach which demonstrates a substantial improvement on conventional simple non learning based methods however the formulation of learning based methods that individually estimates the confidence of each pixel disregards spatial coherency that might exist in the confidence map thus providing a limited performance under challenging conditions our key observation is that the confidence features and resulting confidence maps are smoothly varying in the spatial domain and highly correlated within the local regions of an image we present a new approach that imposes spatial consistency on the confidence estimation specifically a set of robust confidence features is extracted from each superpixel decomposed using the gaussian mixture model and then these features are concatenated with pixel level confidence features the features are then enhanced through adaptive filtering in the feature domain in addition the resulting confidence map estimated using the confidence features with a random regression forest is further improved through k nearest neighbor based aggregation scheme on both pixel and superpixel level to validate the proposed confidence estimation scheme we employ cost modulation or ground control points based optimization in stereo matching experimental results demonstrate that the proposed method outperforms state of the art approaches on various benchmarks including challenging outdoor scenes

a hybrid multiview stereo algorithm for modeling urban scenes we present an original multiview stereo reconstruction algorithm which allows the 3d modeling of urban scenes as a combination of meshes and geometric primitives the method provides a compact model while preserving details irregular elements such as statues and ornaments are described by meshes whereas regular structures such as columns and walls are described by primitives planes spheres cylinders cones and tori we adopt a two step strategy consisting first in segmenting the initial mesh based surface using a multilabel markov random field based model and second in sampling primitive and mesh components simultaneously on the obtained partition by a jump diffusion process the quality of a reconstruction is measured by a multi object energy model which takes into account both photo consistency and semantic considerations i e geometry and shape layout the segmentation and sampling steps are embedded into an iterative refinement procedure which provides an increasingly accurate hybrid representation experimental results on complex urban structures and large scenes are presented and compared to state of the art multiview stereo meshing algorithms

independent filtering increases detection power for high throughput experiments with high dimensional data variable by variable statistical testing is often used to select variables whose behavior differs across conditions such an approach requires adjustment for multiple testing which can result in low statistical power a two stage approach that first filters variables by a criterion independent of the test statistic and then only tests variables which pass the filter can provide higher power we show that use of some filter test statistics pairs presented in the literature may however lead to loss of type i error control we describe other pairs which avoid this problem in an application to microarray data we found that gene by gene filtering by overall variance followed by a t test increased the number of discoveries by 50 we also show that this particular statistic pair induces a lower bound on fold change among the set of discoveries independent filtering using filter test pairs that are independent under the null hypothesis but correlated under the alternative is a general approach that can substantially increase the efficiency of experiments

exploiting gene environment independence for analysis of case control studies an empirical bayes type shrinkage estimator to trade off between bias and efficiency summary standard prospective logistic regression analysis of case control data often leads to very imprecise estimates of gene environment interactions due to small numbers of cases or controls in cells of crossing genotype and exposure in contrast under the assumption of gene environment independence modern retrospective methods including the case only approach can estimate the interaction parameters much more precisely but they can be seriously biased when the underlying assumption of gene environment independence is violated in this article we propose a novel empirical bayes type shrinkage estimator to analyze case control data that can relax the gene environment independence assumption in a data adaptive fashion in the special case involving a binary gene and a binary exposure the method leads to an estimator of the interaction log odds ratio parameter in a simple closed form that corresponds to an weighted average of the standard case only and case control estimators we also describe a general approach for deriving the new shrinkage estimator and its variance within the retrospective maximum likelihood framework developed by chatterjee and carroll 2005 biometrika92 399 418 both simulated and real data examples suggest that the proposed estimator strikes a balance between bias and efficiency depending on the true nature of the gene environment association and the sample size for a given study

fuzzy set based models of neurons and knowledge based networks we will introduce and study different fuzzy set oriented computational models of neurons the generic topologies of the neurons emerging there are significantly influenced by basic logic operators and or not encountered in the theory of fuzzy sets the logical flavor of the proposed constructs is expressed in terms of operators used in their formalization and a way of their superposition in the neurons the two broad categories of neurons embrace basic aggregation neurons named and and or neurons and referential processing units such as matching dominance inclusion neurons the specific features of the neurons are flexibly modeled with the aid of triangular norms the inhibitory and excitatory characteristics are captured by embodying direct and complemented negated input signals we will propose various topologies of neural networks put together with the use of these neurons and demonstrate straightforward relationships coming off between the problem specificity and the resulting architecture of the network this limpid way of mapping the domain knowledge onto the structure of the network contributes significantly toward enhancements in learning processes in the network and substantially facilitates its interpretation

comparison of three summary statistics for ranking genes in genome wide association studies problems associated with insufficient power have haunted the analysis of genome wide association studies and are likely to be the main challenge for the analysis of next generation sequencing data ranking genes according to their strength of association with the investigated phenotype is one solution to obtain rankings for genes researchers can draw from a wide range of statistics summarizing the relationships between variants mapped to a gene and the phenotype hence it is of interest to explore the performance of these statistics in the context of rankings to this end we conducted a simulation study limited to genes of equal sizes of three different summary statistics examining the ability to rank genes in a meaningful order the weighted sum of squared marginal score test pan 2009 rarecover algorithm bahtia et al 2010 and the elastic net regularization zou and hastie 2005 were chosen because they can handle common as well as rare variants the test based on the score statistic outperformed both other methods in almost all investigated scenarios it was the only measure to consistently detect genes with interacting causal variants however the rarecover algorithm proved better at identifying genes including causal variants with small effect sizes and low minor allele frequency than the weighted sum of squared marginal score test the performance of the elastic net regularization was unimpressive for all but the simplest scenarios

robust matching cost function for stereo correspondence using matching by tone mapping and adaptive orthogonal integral image real world stereo images are inevitably affected by radiometric differences including variations in exposure vignetting lighting and noise stereo images with severe radiometric distortion can have large radiometric differences and include locally nonlinear changes in this paper we first introduce an adaptive orthogonal integral image which is an improved version of an orthogonal integral image after that based on matching by tone mapping and the adaptive orthogonal integral image we propose a robust and accurate matching cost function that can tolerate locally nonlinear intensity distortion by using the adaptive orthogonal integral image the proposed matching cost function can adaptively construct different support regions of arbitrary shapes and sizes for different pixels in the reference image so it can operate robustly within object boundaries furthermore we develop techniques to automatically estimate the values of the parameters of our proposed function we conduct experiments using the proposed matching cost function and compare it with functions employing the census transform supporting local binary pattern and adaptive normalized cross correlation as well as a mutual information based matching cost function using different stereo data sets by using the adaptive orthogonal integral image the proposed matching cost function reduces the error from 21 51 to 15 73 in the middlebury data set and from 15 9 to 10 85 in the kitti data set as compared with using the orthogonal integral image the experimental results indicate that the proposed matching cost function is superior to the state of the art matching cost functions under radiometric variation

variational recursive joint estimation of dense scene structure and camera motion from monocular high speed traffic sequences we present an approach to jointly estimating camera motion and dense structure of a static scene in terms of depth maps from monocular image sequences in driver assistance scenarios at each instant of time only two consecutive frames are processed as input data of a joint estimator that fully exploits second order information of the corresponding optimization problem and effectively copes with the non convexity due to both the imaging geometry and the manifold of motion parameters additionally carefully designed gaussian approximations enable probabilistic inference based on locally varying confidence and globally varying sensitivity due to the epipolar geometry with respect to the high dimensional depth map estimation embedding the resulting joint estimator in an online recursive framework achieves a pronounced spatio temporal filtering effect and robustness we evaluate hundreds of images taken from a car moving at speed up to 100 km h and being part of a publicly available benchmark data set the results compare favorably with two alternative settings stereo based scene reconstruction and camera motion estimation in batch mode using multiple frames they however require a calibrated camera pair or storage for more than two frames which is less attractive from a technical viewpoint than the proposed monocular and recursive approach in addition to real data a synthetic sequence is considered which provides reliable ground truth

robust and dense depth estimation for light field images we propose a depth estimation method for light field images light field images can be considered as a collection of 2d images taken from different viewpoints arranged in a regular grid we exploit this configuration and compute the disparity maps between specific pairs of views this computation is carried out by a state of the art two view stereo method providing a nondense disparity estimation we propose a disparity interpolation method increasing the density and improving the accuracy of this initial estimate disparities obtained from several pairs of views are fused to obtain a unique and robust estimation finally different experiments on synthetic and real images show how the proposed method outperforms the state of the art results

stereo matching using tree filtering matching cost aggregation is one of the oldest and still popular methods for stereo correspondence while effective and efficient cost aggregation methods typically aggregate the matching cost by summing averaging over a user specified local support region this is obviously only locally optimal and the computational complexity of the full kernel implementation usually depends on the region size in this paper the cost aggregation problem is re examined and a non local solution is proposed the matching cost values are aggregated adaptively based on pixel similarity on a tree structure derived from the stereo image pair to preserve depth edges the nodes of this tree are all the image pixels and the edges are all the edges between the nearest neighboring pixels the similarity between any two pixels is decided by their shortest distance on the tree the proposed method is non local as every node receives supports from all other nodes on the tree the proposed method can be naturally extended to the time domain for enforcing temporal coherence unlike previous methods the non local property guarantees that the depth edges will be preserved when the temporal coherency between all the video frames are considered a non local weighted median filter is also proposed based on the non local cost aggregation algorithm it has been demonstrated to outperform all local weighted median filters on disparity depth upsampling and refinement

frequentist model average estimators the traditional use of model selection methods in practice is to proceed as if the final selected model had been chosen in advance without acknowledging the additional uncertainty introduced by model selection this often means underreporting of variability and too optimistic confidence intervals we build a general large sample likelihood apparatus in which limiting distributions and risk properties of estimators post selection as well as of model average estimators are precisely described also explicitly taking modeling bias into account this allows a drastic reduction in complexity as competing model averaging schemes may be developed discussed and compared inside a statistical prototype experiment where only a few crucial quantities matter in particular we offer a frequentist view on bayesian model averaging methods and give a link to generalized ridge estimators our work also leads to new model selection criteria the methods are illustrated with real data applications

fast segmentation from blurred data in 3d fluorescence microscopy we develop a fast algorithm for segmenting 3d images from linear measurements based on the potts model or piecewise constant mumford shah model to that end we first derive suitable space discretizations of the 3d potts model which are capable of dealing with 3d images defined on non cubic grids our discretization allows us to utilize a specific splitting approach which results in decoupled subproblems of moderate size the crucial point in the 3d setup is that the number of independent subproblems is so large that we can reasonably exploit the parallel processing capabilities of the graphics processing units gpus our gpu implementation is up to 18 times faster than the sequential cpu version this allows to process even large volumes in acceptable runtimes as a further contribution we extend the algorithm in order to deal with non negativity constraints we demonstrate the efficiency of our method for combined image deconvolution and segmentation on simulated data and on real 3d wide field fluorescence microscopy data

a unified set based test with adaptive filtering for gene environment interaction analyses in genome wide gene environment interaction gxe studies a common strategy to improve power is to first conduct a filtering test and retain only the snps that pass the filtering in the subsequent gxe analyses inspired by two stage tests and gene based tests in gxe analysis we consider the general problem of jointly testing a set of parameters when only a few are truly from the alternative hypothesis and when filtering information is available we propose a unified set based test that simultaneously considers filtering on individual parameters and testing on the set we derive the exact distribution and approximate the power function of the proposed unified statistic in simplified settings and use them to adaptively calculate the optimal filtering threshold for each set in the context of gene based gxe analysis we show that although the empirical power function may be affected by many factors the optimal filtering threshold corresponding to the peak of the power curve primarily depends on the size of the gene we further propose a resampling algorithm to calculate p values for each gene given the estimated optimal filtering threshold the performance of the method is evaluated in simulation studies and illustrated via a genome wide gene gender interaction analysis using pancreatic cancer genome wide association data

quantitative analysis of properties and spatial relations of fuzzy image regions properties of objects and spatial relations between objects play an important role in rule based approaches for high level vision the partial presence or absence of such properties and relationships can supply both positive and negative evidence for region labeling hypotheses similarly fuzzy labeling of a region can generate new hypotheses pertaining to the properties of the region its relation to the neighboring regions and finally hypotheses pertaining to the labels of the neighboring regions a unified methodology that can be used to characterize both properties and spatial relationships of object regions in a digital image is presented the methods proposed for computing the properties and relations of image regions can be used to arrive at more meaningful decisions about the contents of the scene

euler spiral for shape completion in this paper we address the curve completion problem e g the geometric continuation of boundaries of objects which are temporarily interrupted by occlusion also known as the gap completion or shape completion problem this problem is a significant element of perceptual grouping of edge elements and has been approached by using cubic splines or biarcs which minimize total curvature squared elastica as motivated by a physical analogy our approach is motivated by railroad design methods of the early 1900 s which connect two rail segments by transition curves and by the work of knuth on mathematical typography we propose that in using an energy minimizing solution completion curves should not penalize curvature as in elastica but curvature variation the minimization of total curvature variation leads to an euler spiral solution a curve whose curvature varies linearly with arclength the construction of this curve from a pair of points and tangents at these points is reduced to a nonlinear system of equations involving fresnel integrals whose solution relies on optimization from a suitable initial condition constrained to satisfy given boundary conditions since the choice of an appropriate initial curve is critical in this optimization we analytically derive an optimal solution in the class of biarc curves which is then used as the initial curve the resulting interpolations yield intuitive interpolation across gaps and occlusions and are extensible in contrast to scale invariant elastica in addition euler spiral segments can be used in other applications of curve completions e g modeling boundary segments between curvature extrema or skeletal branch geometry

approximate and asymptotic distributions of chi squared type mixtures with applications in this article we study how to approximate a random variable t of general chi squared type mixtures by a random variable of the form via matching the first three cumulants the approximation error bounds for the density functions of the chi squared approximation and the normal approximation are established applications of the results to some nonparametric goodness of fit tests including those tests based on orthogonal series smoothing splines and local polynomial smoothers are investigated two simulation studies are conducted to compare the chi squared approximation and the normal approximation numerically the chi squared approximation is illustrated using a real data example for polynomial goodness of fit tests

a simple multivariate test for one sided alternatives abstract a simple test for multivariate normal data is proposed that has good power for alternatives where the mean vector tends to be positive the test rejects if the quadratic form of the sample mean vector exceeds its 2 critical value and the sum of the elements of the mean vector exceeds zero the proposed test is shown to have type i error rate equal to whether or not the covariance matrix is known tight bounds on the power of the proposed test are provided and the test is compared to three likelihood ratio tests

residual variance and residual pattern in nonlinear regression summary a nonparametric estimator of residual variance in nonlinear regression is proposed it is based on local linear fitting asymptotically the estimator has a small bias but a larger variance compared with the parametric estimator in linear regression finite sample properties are investigated in a simulation study including a comparison with other nonparametric estimators the method is also useful for spotting heteroscedasticity and outliers in the residuals at an early stage of the data analysis a further application is checking the fit of parametric models this is illustrated for longitudinal growth data

incorporating fuzzy membership functions into the perceptron algorithm the perceptron algorithm one of the class of gradient descent techniques has been widely used in pattern recognition to determine linear decision boundaries while this algorithm is guaranteed to converge to a separating hyperplane if the data are linearly separable it exhibits erratic behavior if the data are not linearly separable fuzzy set theory is introduced into the perceptron algorithm to produce a fuzzy algorithm which ameliorates the convergence problem in the nonseparable case it is shown that the fuzzy perceptron like its crisp counterpart converges in the separable case a method of generating membership functions is developed and experimental results comparing the crisp to the fuzzy perceptron are presented

stereo processing by semiglobal matching and mutual information this paper describes the semiglobal matching sgm stereo method it uses a pixelwise mutual information ml based matching cost for compensating radiometric differences of input images pixelwise matching is supported by a smoothness constraint that is usually expressed as a global cost function sgm performs a fast approximation by pathwise optimizations from all directions the discussion also addresses occlusion detection subpixel refinement and multibaseline matching additionally postprocessing steps for removing outliers recovering from specific problems of structured environments and the interpolation of gaps are presented finally strategies for processing almost arbitrarily large images and fusion of disparity images using orthographic projection are proposed a comparison on standard stereo images shows that sgm is among the currently top ranked algorithms and is best if subpixel accuracy is considered the complexity is linear to the number of pixels and disparity range which results in a runtime of just 1 2 seconds on typical test images an in depth evaluation of the ml based matching cost demonstrates a tolerance against a wide range of radiometric transformations finally examples of reconstructions from huge aerial frame and pushbroom images demonstrate that the presented ideas are working well on practical problems

robust multiview photometric stereo using planar mesh parameterization we propose a robust uncalibrated multiview photometric stereo method for high quality 3d shape reconstruction in our method a coarse initial 3d mesh obtained using a multiview stereo method is projected onto a 2d planar domain using a planar mesh parameterization technique we describe methods for surface normal estimation that work in the parameterized 2d space that jointly incorporates all geometric and photometric cues from multiple viewpoints using an estimated surface normal map a refined 3d mesh is then recovered by computing an optimal displacement map in the same 2d planar domain our method avoids the need of merging view dependent surface normal maps that is often required in conventional methods we conduct evaluation on various real world objects containing surfaces with specular reflections multiple albedos and complex topologies in both controlled and uncontrolled settings and demonstrate that accurate 3d meshes with fine geometric details can be recovered by our method

depth preserving warping for stereo image retargeting the popularity of stereo images and various display devices poses the need of stereo image retargeting techniques existing warping based retargeting methods can well preserve the shape of salient objects in a retargeted stereo image pair nevertheless these methods often incur depth distortion since they attempt to preserve depth by maintaining the disparity of a set of sparse correspondences rather than directly controlling the warping in this paper by considering how to directly control the warping functions we propose a warping based stereo image retargeting approach that can simultaneously preserve the shape of salient objects and the depth of 3d scenes we first characterize the depth distortion in terms of warping functions to investigate the impact of a warping function on depth distortion based on the depth distortion model we then exploit binocular visual characteristics of stereo images to derive region based depth preserving constraints which directly control the warping functions so as to faithfully preserve the depth of 3d scenes third with the region based depth preserving constraints we present a novel warping based stereo image retargeting framework since the depth preserving constraints are derived regardless of shape preservation we relax the depth preserving constraints to fulfill a tradeoff between shape preservation and depth preservation finally we propose a quad based implementation of the proposed framework the results demonstrate the efficacy of our method in both depth and shape preservation for stereo image retargeting

stereo seam carving a geometrically consistent approach image retargeting algorithms attempt to adapt the image content to the screen without distorting the important objects in the scene existing methods address retargeting of a single image in this paper we propose a novel method for retargeting a pair of stereo images naively retargeting each image independently will distort the geometric structure and hence will impair the perception of the 3d structure of the scene we show how to extend a single image seam carving to work on a pair of images our method minimizes the visual distortion in each of the images as well as the depth distortion a key property of the proposed method is that it takes into account the visibility relations between pixels in the image pair occluded and occluding pixels as a result our method guarantees as we formally prove that the retargeted pair is geometrically consistent with a feasible 3d scene similar to the original one hence the retargeted stereo pair can be viewed on a stereoscopic display or further processed by any computer vision algorithm we demonstrate our method on a number of challenging indoor and outdoor stereo images

on the use of nonparametric regression for model checking summary the use of nonparametric regression is explored to check the fit of a parametric regression model the principal aim is to check the validity of the regression curve rather than necessarily to detect outliers a pseudo likelihood ratio test is developed to provide a global assessment of fit and simulation bands are used to indicate the nature of departures from the model the types of data considered include discrete response variables where standard diagnostic techniques are often not appropriate and first order autoregressive series several numerical examples are given nonparametric regression can be used in an informal graphical way to assess the relationship between a response and an explanatory variable in this paper we aim to develop more formal methods of assessing the assumptions of a parametric model in particular when regression diagnostics of the type developed for normal linear models are not readily available the principal aim is to check the validity of the systematic part of the model by comparing a nonparametric estimate of the regression curve with a parametric one such a comparison may also identify outliers although the distinction between outliers and model inadequacy is not always easy two techniques are used to assess the fit of a parametric model in 2 confidence bands are constructed around the fitted regression curve by simulation a comparison of these with the nonparametric curve gives an indication of the nature of any departures from the model in 3 a pseudo likelihood ratio test is developed this provides a quantitative global assessment of fit in applying these ideas special emphasis is given to discrete data and notably logistic regression because of the difficulty in applying standard residual based model checking techniques to this type of response variable a poisson regression example is discussed in 4 however the underlying ideas have wider applications autoregressive time series of order 1 are discussed in 6 sections 5 and 7 discuss general issues we first discuss the context of binary regression with a single covariate and the difficulties caused by the discreteness of the response variable the observed data are assumed to be of the form xi yi ni where xi is a covariate value and yi has a binomial

linear time illumination invariant stereo matching this paper proposes a new similarity measure that is invariant to global and local affine illumination changes unlike existing methods its computational complexity is very low when used for stereo correspondence estimation its computational complexity is linear in the number of image pixels and disparity searching range it also outperforms the current state of the art similarity measures in terms of accuracy on the middlebury benchmark with radiometric differences

fast approximate energy minimization via graph cuts many tasks in computer vision involve assigning a label such as disparity to every pixel a common constraint is that the labels should vary smoothly almost everywhere while preserving sharp discontinuities that may exist e g at object boundaries these tasks are naturally stated in terms of energy minimization the authors consider a wide class of energies with various smoothness constraints global minimization of these energy functions is np hard even in the simplest discontinuity preserving case therefore our focus is on efficient approximation algorithms we present two algorithms based on graph cuts that efficiently find a local minimum with respect to two types of large moves namely expansion moves and swap moves these moves can simultaneously change the labels of arbitrarily large sets of pixels in contrast many standard algorithms including simulated annealing use small moves where only one pixel changes its label at a time our expansion algorithm finds a labeling within a known factor of the global minimum while our swap algorithm handles more general energy functions both of these algorithms allow important cases of discontinuity preserving energies we experimentally demonstrate the effectiveness of our approach for image restoration stereo and motion on real data with ground truth we achieve 98 percent accuracy

light field reconstruction using shearlet transform in this article we develop an image based rendering technique based on light field reconstruction from a limited set of perspective views acquired by cameras our approach utilizes sparse representation of epipolar plane images epi in shearlet transform domain the shearlet transform has been specifically modified to handle the straight lines characteristic for epi the devised iterative regularization algorithm based on adaptive thresholding provides high quality reconstruction results for relatively big disparities between neighboring views the generated densely sampled light field of a given 3d scene is thus suitable for all applications which require light field reconstruction the proposed algorithm compares favorably against state of the art depth image based rendering techniques and shows superior performance specifically in reconstructing scenes containing semi transparent objects

comparing nonparametric versus parametric regression fits in general there will be visible differences between a parametric and a nonparametric curve estimate it is therefore quite natural to compare these in order to decide whether the parametric model could be justified an asymptotic quantification is the distribution of the integrated squared difference between these curves we show that the standard way of bootstrapping this statistic fails we use and analyse a different form of bootstrapping for this task we call this method the wild bootstrap and apply it to fitting engel curves in expenditure data analysis

a quasi f test for functional linear models with functional covariates and its application to longitudinal data functional linear models are useful in analyzing data from designed experiments and observational studies with functional responses as well as longitudinal data with a large number of repeated measures on each subject we propose a quasi f test for functional linear models with functional covariates and outcomes we develop a numerical procedure and an efficient approximation for computing p values and present a simple way to test individual predictors for illustration we apply the proposed procedure to a longitudinal depression data set with repeatedly measured methamphetamine use as a predictor we conduct a simulation study to assess the size and the power of the test

a component wise analysis of constructible match cost functions for global stereopsis match cost functions are common elements of every stereopsis algorithm that are used to provide a dissimilarity measure between pixels in different images global stereopsis algorithms incorporate assumptions about the smoothness of the resulting distance map that can interact with match cost functions in unpredictable ways in this paper we present a large scale study on the relative performance of a structured set of match cost functions within several global stereopsis frameworks we compare 272 match cost functions that are built from component parts in the context of four global stereopsis frameworks with a data set consisting of 57 stereo image pairs at three different variances of synthetic sensor noise from our analysis we infer a set of general rules that can be used to guide derivation of match cost functions for use in global stereopsis algorithms

testing the goodness of fit of a linear model via nonparametric regression techniques abstract this article investigates the use of nonparametric regression methodology to test the adequacy of a parametric linear model the large sample properties of parametric goodness of fit tests for linearity are considered the inadequacies of such tests lead to the proposal of new tests that are constructed from nonparametric regression fits to the residuals from linear regression large sample theory is derived for two variants of this type of statistic the results demonstrate that such tests are consistent against all fixed smooth alternatives to linearity but are incapable of detecting local alternatives converging to a linear model at the parametric rate n 1 2 simulation experiments involving a test based on fitting cubic smoothing splines to residuals reveals that this test has good power properties against several reasonable alternatives

3d scene flow estimation with a piecewise rigid scene model 3d scene flow estimation aims to jointly recover dense geometry and 3d motion from stereoscopic image sequences thus generalizes classical disparity and 2d optical flow estimation to realize its conceptual benefits and overcome limitations of many existing methods we propose to represent the dynamic scene as a collection of rigidly moving planes into which the input images are segmented geometry and 3d motion are then jointly recovered alongside an over segmentation of the scene this piecewise rigid scene model is significantly more parsimonious than conventional pixel based representations yet retains the ability to represent real world scenes with independent object motion it furthermore enables us to define suitable scene priors perform occlusion reasoning and leverage discrete optimization schemes toward stable and accurate results assuming the rigid motion to persist approximately over time additionally enables us to incorporate multiple frames into the inference to that end each view holds its own representation which is encouraged to be consistent across all other viewpoints and frames in a temporal window we show that such a view consistent multi frame scheme significantly improves accuracy especially in the presence of occlusions and increases robustness against adverse imaging conditions our method currently achieves leading performance on the kitti benchmark for both flow and stereo

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

test of significance based on wavelet thresholding and neyman s truncation abstract traditional nonparametric tests such as the kolmogorov smirnov test and the cramer von mises test are based on the empirical distribution functions although these tests possess root n consistency they effectively use only information contained in the low frequencies this leads to low power in detecting fine features such as sharp and short aberrants as well as global features such as high frequency alternations the drawback can be repaired via smoothing based test statistics in this article we propose two such kind of test statistics based on the wavelet thresholding and the neyman truncation we provide extensive evidence to demonstrate that the proposed tests have higher power in detecting sharp peaks and high frequency alternations while maintaining the same capability in detecting smooth alternative densities as the traditional tests similar conclusions can be made for two sample nonparametric tests of distribution functions in that case the traditional linear rank tests such as th

test of significance when data are curves abstract with modern technology massive data can easily be collected in a form of multiple sets of curves new statistical challenge includes testing whether there is any statistically significant difference among these sets of curves in this article we propose some new tests for comparing two groups of curves based on the adaptive neyman test and the wavelet thresholding techniques introduced earlier by fan we demonstrate that these tests inherit the properties outlined by fan and that they are simple and powerful for detecting differences between two sets of curves we then further generalize the idea to compare multiple sets of curves resulting in an adaptive high dimensional analysis of variance called hanova these newly developed techniques are illustrated by using a dataset on pizza commercials where observations are curves and an analysis of cornea topography in ophthalmology where images of individuals are observed a simulation example is also presented to illustrate the power of the adapti

a composite likelihood approach to latent multivariate gaussian modeling of snp data with application to genetic association testing many statistical tests have been proposed for case control data to detect disease association with multiple single nucleotide polymorphisms snps in linkage disequilibrium the main reason for the existence of so many tests is that each test aims to detect one or two aspects of many possible distributional differences between cases and controls largely due to the lack of a general and yet simple model for discrete genotype data here we propose a latent variable model to represent snp data the observed snp data are assumed to be obtained by discretizing a latent multivariate gaussian variate because the latent variate is multivariate gaussian its distribution is completely characterized by its mean vector and covariance matrix in contrast to much more complex forms of a general distribution for discrete multivariate snp data we propose a composite likelihood approach for parameter estimation a direct application of this latent variable model is to association testing with multiple snps in a candidate gene or region in contrast to many existing tests that aim to detect only one or two aspects of many possible distributional differences of discrete snp data we can exclusively focus on testing the mean and covariance parameters of the latent gaussian distributions for cases and controls our simulation results demonstrate potential power gains of the proposed approach over some existing methods

near real time stereo matching using geodesic diffusion adaptive weight algorithms currently represent the state of the art in local stereo matching however due to their computational requirements these types of solutions are not suitable for real time implementation here we present a novel aggregation method inspired by the anisotropic diffusion technique used in image filtering the proposed aggregation algorithm produces results similar to adaptive weight solutions while reducing the computational requirements moreover near real time performance is demonstrated with a gpu implementation of the algorithm

generalized likelihood ratio statistics and wilks phenomenon likelihood ratio theory has had tremendous success in parametric inference due to the fundamental theory of wilks yet there is no general applicable approach for nonparametric inferences based on function estimation maximum likelihood ratio test statistics in general may not exist in nonparametric function estimation setting even if they exist they are hard to find and can not be optimal as shown in this paper we introduce the generalized likelihood statistics to overcome the drawbacks of nonparametric maximum likelihood ratio statistics a new s wilks phenomenon is unveiled we demonstrate that a class of the generalized likelihood statistics based on some appropriate nonparametric estimators are asymptotically distribution free and follow chi 2 distributions under null hypotheses for a number of useful hypotheses and a variety of useful models including gaussian white noise models nonparametric regression models varying coefficient models and generalized varying coefficient models we further demonstrate that generalized likelihood ratio statistics are asymptotically optimal in the sense that they achieve optimal rates of convergence given by ingster they can even be adaptively optimal in the sense of spokoiny by using a simple choice of adaptive smoothing parameter our work indicates that the generalized likelihood ratio statistics are indeed general and powerful for nonparametric testing problems based on function estimation

correctness prediction accuracy improvement and generalization of stereo matching using supervised learning machine learning has been instrumental in most areas of computer vision but has not been applied to the problem of stereo matching with similar frequency or success in this paper we present a supervised learning approach by defining a set of features that capture various forms of information about each pixel and then by using them to predict the correctness of stereo matches based on a random forest we show highly competitive results in predicting the correctness of matches and in confidence estimation which allows us to rank pixels according to the reliability of their assigned disparities moreover we show how these confidence values can be used to improve the accuracy of disparity maps by integrating them with an mrf based stereo algorithm this is an important distinction from current literature that has mainly focused on sparsification by removing potentially erroneous disparities to generate quasi dense disparity maps finally we demonstrate domain generalization of our method by applying classifiers to datasets different than those they were trained on with minimal loss of accuracy

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

a unified approach to likelihood inference on stochastic orderings in a nonparametric context abstract for data in a two way contingency table with ordered margins we consider various hypotheses of stochastic orders among the conditional distributions considered by rows and show that each is equivalent to requiring that an invertible transformation of the vectors of conditional row probabilities satisfies an appropriate set of linear inequalities this leads to the construction of a general algorithm for maximum likelihood estimation under multinomial sampling and provides a simple framework for deriving the asymptotic distribution of log likelihood ratio tests the usual stochastic ordering and the so called uniform and likelihood ratio orderings are considered as special cases in particular for each of these three orderings we determine the transformation required to apply the estimation algorithm we then consider testing the hypothesis that the rows are identically distributed against the alternative that they are stochastically ordered as well as testing each stochastic order against an u

the control of the false discovery rate in multiple testing under dependency benjamini and hochberg suggest that the false discovery rate may be the appropriate error rate to control in many applied multiple testing problems a simple procedure was given there as an fdr controlling procedure for independent test statistics and was shown to be much more powerful than comparable procedures which control the traditional familywise error rate we prove that this same procedure also controls the false discovery rate when the test statistics have positive regression dependency on each of the test statistics corresponding to the true null hypotheses this condition for positive dependency is general enough to cover many problems of practical interest including the comparisons of many treatments with a single control multivariate normal test statistics with positive correlation matrix and multivariate t furthermore the test statistics may be discrete and the tested hypotheses composite without posing special difficulties for all other forms of dependency a simple conservative modification of the procedure controls the false discovery rate thus the range of problems for which a procedure with proven fdr control can be offered is greatly increased 1 1 simultaneous hypotheses testing the control of the increased type i error when testing simultaneously a family of hypotheses is a central issue in the area of multiple comparisons rarely are we interested only in whether all hypotheses are jointly true or not which is the test of the intersection null hypothesis in most applications we infer about the individual hypotheses realizing that some of the tested hypotheses are usually true we hope not all and some are not we wish to decide which ones are not true indicating statistical discoveries an important such problem is that of multiple endpoints in a clinical trial a new treatment is compared with an existing one in terms of a large number of potential benefits endpoints

effective degrees of freedom and the likelihood ratio test summary normal theory problems involving several unknown variances may require use of estimates not having simple chi squared distributions confidence intervals are then commonly based on the use of effective degrees of freedom i e the fitting of a gamma distribution via the first two moments replacing parameters by estimates the confidence intervals given by this method are compared with those resulting from the likelihood ratio test inserting a bartlett adjustment factor to achieve close approximation by a chi squared distribution two special problems one concerning components of variance and one a generalization of the behrens fisher problem are studied in detail

motion estimation based on mutual information and adaptive multi scale thresholding this paper proposes a new method of calculating a matching metric for motion estimation the proposed method splits the information in the source images into multiple scale and orientation subbands reduces the subband values to a binary representation via an adaptive thresholding algorithm and uses mutual information to model the similarity of corresponding square windows in each image a moving window strategy is applied to recover a dense estimated motion field whose properties are explored the proposed matching metric is a sum of mutual information scores across space scale and orientation this facilitates the exploitation of information diversity in the source images experimental comparisons are performed amongst several related approaches revealing that the proposed matching metric is better able to exploit information diversity generating more accurate motion fields

statistical inference of genetic pathway analysis in high dimensions genetic pathway analysis has become an important tool for investigating the association between a group of genetic variants and traits with dense genotyping and extensive imputation the number of genetic variants in biological pathways has increased considerably and sometimes exceeds the sample size formula see text conducting genetic pathway analysis and statistical inference in such settings is challenging we introduce an approach that can handle pathways whose dimension formula see text could be greater than formula see text our method can be used to detect pathways that have nonsparse weak signals as well as pathways that have sparse but stronger signals we establish the asymptotic distribution for the proposed statistic and conduct theoretical analysis on its power simulation studies show that our test has correct type i error control and is more powerful than existing approaches an application to a genome wide association study of high density lipoproteins demonstrates the proposed approach

statistical inferences for functional data with modem technology development functional data are being observed frequently in many scientific fields a popular method for analyzing such functional data is smoothing first then estimation that is statistical inference such as estimation and hypothesis testing about functional data is conducted based on the substitution of the underlying individual functions by their reconstructions obtained by one smoothing technique or another however little is known about this substitution effect on functional data analysis in this paper this problem is investigated when the local polynomial kernel lpk smoothing technique is used for individual function reconstructions we find that under some mild conditions the substitution effect can be ignored asymptotically based on this we construct lpk reconstruction based estimators for the mean covariance and noise variance functions of a functional data set and derive their asymptotics we also propose a gcv rule for selecting good bandwidths for the lpk reconstructions when the mean function also depends on some time independent covariates we consider a functional linear model where the mean function is linearly related to the covariates but the covariate effects are functions of time the lpk reconstruction based estimators for the covariate effects and the covariance function are also constructed and their asymptotics are derived moreover we propose a l 2 norm based global test statistic for a general hypothesis testing problem about the covariate effects and derive its asymptotic random expression the effect of the bandwidths selected by the proposed gcv rule on the accuracy of the lpk reconstructions and the mean function estimator is investigated via a simulation study the proposed methodologies are illustrated via an application to a real functional data set collected in climatology

asymptotic distribution of test statistics in the analysis of moment structures under inequality constraints summary the analysis of moment structural models has become an important tool of investigation in behavioural educational and economic studies the chi squared largesample test is routinely employed to assess the goodness of fit of the model considered however in order to invoke the standard asymptotic distribution theory certain regularity conditions have to be met here we consider the case where the population value of the parameter vector is a boundary point of the feasible region we show that in this case the asymptotic distribution of test statistic is a mixture of chi squared distributions the problem of finding the corresponding weights is discussed

controlling the false discovery rate a practical and powerful approach to multiple testing summary the common approach to the multiplicity problem calls for controlling the familywise error rate fwer this approach though has faults and we point out a few a different approach to problems of multiple significance testing is presented it calls for controlling the expected proportion of falsely rejected hypotheses the false discovery rate this error rate is equivalent to the fwer when all hypotheses are true but is smaller otherwise therefore in problems where the control of the false discovery rate rather than that of the fwer is desired there is potential for a gain in power a simple sequential bonferronitype procedure is proved to control the false discovery rate for independent test statistics and a simulation study shows that the gain in power is substantial the use of the new procedure and the appropriateness of the criterion are illustrated with examples

order restricted statistical tests on multinomial and poisson parameters the starshaped restriction abstract likelihood ratio statistics for i testing the homogeneity of a collection of multinomial parameters against the alternative which accounts for the restriction that those parameters are starshaped cf shaked ann statist 1979 and for ii testing the null hypothesis that this parameter vector is starshaped are considered for both tests the asymptotic distribution of the test statistic under the null hypothesis is a version of the chi bar square distribution analogous tests on a collection of poisson means are also found to have asymptotic chi bar square distributions author

estimation of regression coefficients when some regressors are not always observed abstract in applied problems it is common to specify a model for the conditional mean of a response given a set of regressors a subset of the regressors may be missing for some study subjects either by design or happenstance in this article we propose a new class of semiparametric estimators based on inverse probability weighted estimating equations that are consistent for parameter vector 0 of the conditional mean model when the data are missing at random in the sense of rubin and the missingness probabilities are either known or can be parametrically modeled we show that the asymptotic variance of the optimal estimator in our class attains the semiparametric variance bound for the model by first showing that our estimation problem is a special case of the general problem of parameter estimation in an arbitrary semiparametric model in which the data are missing at random and the probability of observing complete data is bounded away from 0 and then deriving a representation for the efficient score

some comments on maximum likelihood and partial least squares methods abstract the paper discusses some general aspects of two estimation methods which are designed for analysis of interrelationships between indirectly and directly observable variables the paper s main object is to summarize in broad terms what appears to be known about tthe asymptotic properties of maximum likelihood and partial least squares estimators the author would be pleased if as a side effect interest is stirred up in the analysis of estimators under non textbook assumptions

insertion of 3 d primitives in mesh based representations towards compact models preserving the details we propose an original hybrid modeling process of urban scenes that represents 3 d models as a combination of mesh based surfaces and geometric 3 d primitives meshes describe details such as ornaments and statues whereas 3 d primitives code for regular shapes such as walls and columns starting from an 3 d surface obtained by multiview stereo techniques these primitives are inserted into the surface after being detected this strategy allows the introduction of semantic knowledge the simplification of the modeling and even correction of errors generated by the acquisition process we design a hierarchical approach exploring different scales of an observed scene each level consists first in segmenting the surface using a multilabel energy model optimized by expansion and then in fitting 3 d primitives such as planes cylinders or tori on the obtained partition where relevant experiments on real meshes depth maps and synthetic surfaces show good potential for the proposed approach

semiparametric estimation exploiting covariate independence in two phase randomized trials recent results for case control sampling suggest when the covariate distribution is constrained by gene environment independence semiparametric estimation exploiting such independence yields a great deal of efficiency gain we consider the efficient estimation of the treatment biomarker interaction in two phase sampling nested within randomized clinical trials incorporating the independence between a randomized treatment and the baseline markers we develop a newton raphson algorithm based on the profile likelihood to compute the semiparametric maximum likelihood estimate spmle our algorithm accommodates both continuous phase one outcomes and continuous phase two biomarkers the profile information matrix is computed explicitly via numerical differentiation in certain situations where computing the spmle is slow we propose a maximum estimated likelihood estimator mele which is also capable of incorporating the covariate independence this estimated likelihood approach uses a one step empirical covariate distribution thus is straightforward to maximize it offers a closed form variance estimate with limited increase in variance relative to the fully efficient spmle our results suggest exploiting the covariate independence in two phase sampling increases the efficiency substantially particularly for estimating treatment biomarker interactions

a novel image representation via local frequency analysis for illumination invariant stereo matching in this paper we propose a novel image representation approach to tackle illumination variations in stereo matching problems images are mapped using their fourier transforms which are convolved with a set of monogenic filters frequency analysis is carried out at different scales to account for most image content the phase congruency and the local weighted mean phase angle are then computed over all the scales the original image is transformed into a new representation using these two mappings this representation is invariant to illumination and contrast variations more importantly it is generic and can be used with most sparse as well as dense stereo matching algorithms in addition sequential feature matching or tracking can also benefit from our approach in varying radiometric conditions we demonstrate the improvements introduced with our image mappings on well established data sets in the literature as well as on our own experimental scenarios that include high dynamic range imagery the experiments include both dense and sparse stereo and sequential matching algorithms where the latter is considered in the very challenging visual odometry framework