a consensus sampling technique for fast and robust model fitting in this paper a new algorithm is proposed to improve the efficiency and robustness of random sampling consensus ransac without prior information about the error scale three techniques are developed in an iterative hypothesis and evaluation framework firstly we propose a consensus sampling technique to increase the probability of sampling inliers by exploiting the feedback information obtained from the evaluation procedure secondly the preemptive multiple k th order approximation pmka is developed for efficient model evaluation with unknown error scale furthermore we propose a coarse to fine strategy for the robust standard deviation estimation to determine the unknown error scale experimental results of the fundamental matrix computation on both simulated and real data are shown to demonstrate the superiority of the proposed algorithm over the previous methods

estimating the fundamental matrix via constrained least squares a convex approach in this paper a new method for the estimation of the fundamental matrix from point correspondences in stereo vision is presented the minimization of the algebraic error is performed while taking explicitly into account the rank two constraint on the fundamental matrix it is shown how this nonconvex optimization problem can be solved avoiding local minima by using recently developed convexification techniques the obtained estimate of the fundamental matrix turns out to be more accurate than the one provided by the linear criterion where the rank constraint of the matrix is imposed after its computation by setting the smallest singular value to zero this suggests that the proposed estimate can be used to initialize nonlinear criteria such as the distance to epipolar lines and the gradient criterion in order to obtain a more accurate estimate of the fundamental matrix

influence in principal components analysis summary in linear regression the theoretical influence function and the various sample versions of it have an established place as diagnostic tools these same functions are developed here to provide methods for the detection of influential observations in principal components analysis the perturbation theory of real symmetric matrices unifies this development some interesting points of contrast with the regression case are noted and explained theoretically

a riemannian approach to graph embedding in this paper we make use of the relationship between the laplace beltrami operator and the graph laplacian for the purposes of embedding a graph onto a riemannian manifold to embark on this study we review some of the basics of riemannian geometry and explain the relationship between the laplace beltrami operator and the graph laplacian using the properties of jacobi fields we show how to compute an edge weight matrix in which the elements reflect the sectional curvatures associated with the geodesic paths on the manifold between nodes for the particular case of a constant sectional curvature surface we use the kruskal coordinates to compute edge weights that are proportional to the geodesic distance between points we use the resulting edge weight matrix to embed the nodes of the graph onto a riemannian manifold to do this we develop a method that can be used to perform double centring on the laplacian matrix computed from the edge weights the embedding coordinates are given by the eigenvectors of the centred laplacian with the set of embedding coordinates at hand a number of graph manipulation tasks can be performed in this paper we are primarily interested in graph matching we recast the graph matching problem as that of aligning pairs of manifolds subject to a geometric transformation we show that this transformation is pro crustean in nature we illustrate the utility of the method on image matching using the coil database

flexible spatial configuration of local image features local image features have been designed to be informative and repeatable under rigid transformations and illumination deformations even though current state of the art local image features present a high degree of repeatability their local appearance alone usually does not bring enough discriminative power to support a reliable matching resulting in a relatively high number of mismatches in the correspondence set formed during the data association procedure as a result geometric filters commonly based on global spatial configuration have been used to reduce this number of mismatches however this approach presents a trade off between the effectiveness to reject mismatches and the robustness to nonrigid deformations in this paper we propose two geometric filters based on a semilocal spatial configuration of local features that are designed to be robust to nonrigid deformations and to rigid transformations without compromising its efficacy to reject mismatches we compare our methods to the hough transform which is an efficient and effective mismatch rejection step based on the global spatial configuration of features in these comparisons our methods are shown to be more effective in the task of rejecting mismatches for rigid transformations and nonrigid deformations at comparable time complexity figures finally we demonstrate how we can integrate these methods in a probabilistic recognition system such that the final verification step uses not only the similarity between features but also their semilocal configuration

a minimal solution to radial distortion autocalibration simultaneous estimation of radial distortion epipolar geometry and relative camera pose can be formulated as a minimal problem and solved from a minimal number of image points finding the solution to this problem leads to solving a system of algebraic equations in this paper we provide two different solutions to the problem of estimating radial distortion and epipolar geometry from eight point correspondences in two images unlike previous algorithms which were able to solve the problem from nine correspondences only we enforce the determinant of the fundamental matrix be zero this leads to a system of eight quadratic and one cubic equation in nine variables we first simplify this system by eliminating six of these variables and then solve the system by two alternative techniques the first one is based on the grobner basis method and the second one on the polynomial eigenvalue computation we demonstrate that our solutions are efficient robust and practical by experiments on synthetic and real data

functional topology of evolving urban drainage networks we investigated the scaling and topology of engineered urban drainage networks udns in two cities and further examined udn evolution over decades udn scaling was analyzed using two power law scaling characteristics widely employed for river networks 1 hack s law of length l area a l ah and 2 exceedance probability distribution of upstream contributing area p a a for the smallest udns 2 km2 length area scales linearly h 1 but power law scaling h 0 6 emerges as the udns grow while p a plots for river networks are abruptly truncated those for udns display exponential tempering p a a exp c the tempering parameter c decreases as the udns grow implying that the distribution evolves in time to resemble those for river networks however the power law exponent for large udns tends to be greater than the range reported for river networks differences in generative processes and engineering design constraints contribute to observed differences in the evolution of udns and river networks including subnet heterogeneity and nonrandom branching

power law behavior in geometric characteristics of full binary trees natural river networks exhibit regular scaling laws in their topological organization here we investigate whether these scaling laws are unique characteristics of river networks or can be applicable to general binary tree networks we generate numerous binary trees ranging from purely ordered trees to completely random trees for each generated binary tree we analyze whether the tree exhibits any scaling property found in river networks i e the power laws in the size distribution the length distribution the distance load relationship and the power spectrum of width function we found that partially random trees generated on the basis of two distinct types of deterministic trees i e deterministic critical and supercritical trees show contrasting characteristics partially random trees generated on the basis of deterministic critical trees exhibit all power law characteristics investigated in this study with their fitted exponents close to the values observed in natural river networks over a wide range of random degree on the other hand partially random trees generated on the basis of deterministic supercritical trees rarely follow scaling laws of river networks

on the fitting of surfaces to data with covariances we consider the problem of estimating parameters of a model described by an equation of special form specific models arise in the analysis of a wide class of computer vision problems including conic fitting and estimation of the fundamental matrix we assume that noisy data are accompanied by known covariance matrices characterizing the uncertainty of the measurements a cost function is first obtained by considering a maximum likelihood formulation and applying certain necessary approximations that render the problem tractable a newton like iterative scheme is then generated for determining a minimizer of the cost function unlike alternative approaches such as sampson s method or the renormalization technique the new scheme has as its theoretical limit the minimizer of the cost function furthermore the scheme is simply expressed efficient and unsurpassed as a general technique in our testing an important feature of the method is that it can serve as a basis for conducting theoretical comparison of various estimation approaches

robust structure and motion from outlines of smooth curved surfaces this paper addresses the problem of estimating the motion of a camera as it observes the outline or apparent contour of a solid bounded by a smooth surface in successive image frames in this context the surface points that project onto the outline of an object depend on the viewpoint and the only true correspondences between two outlines of the same object are the projections of frontier points where the viewing rays intersect in the tangent plane of the surface in turn the epipolar geometry is easily estimated once these correspondences have been identified given the apparent contours detected in an image sequence a robust procedure based on ransac and a voting strategy is proposed to simultaneously estimate the camera configurations and a consistent set of frontier point projections by enforcing the redundancy of multiview epipolar geometry the proposed approach is in principle applicable to orthographic weak perspective and affine projection models experiments with nine real image sequences are presented for the orthographic projection case including a quantitative comparison with the ground truth data for the six data sets for which the latter information is available sample visual hulls have been computed from all image sequences for qualitative evaluation

uniqueness and estimation of three dimensional motion parameters of rigid objects with curved surfaces two main results are established in this paper first we show that seven point correspondences are sufficient to uniquely determine from two perspective views the three dimensional motion parameters within a scale factor for the translations of a rigid object with curved surfaces the seven points should not be traversed by two planes with one plane containing the origin nor by a cone containing the origin second a set of essential parameters are introduced which uniquely determine the motion parameters up to a scale factor for the translations and can be estimated by solving a set of linear equations which are derived from the correspondences of eight image points the actual motion parameters can subsequently be determined by computing the singular value decomposition svd of a 3 3 matrix containing the essential parameters

estimation of subspace arrangements with applications in modeling and segmenting mixed data recently many scientific and engineering applications have involved the challenging task of analyzing large amounts of unsorted high dimensional data that have very complicated structures from both geometric and statistical points of view such unsorted data are considered mixed as different parts of the data have significantly different structures which cannot be described by a single model in this paper we propose to use subspace arrangements a union of multiple subspaces for modeling mixed data each subspace in the arrangement is used to model just a homogeneous subset of the data thus multiple subspaces together can capture the heterogeneous structures within the data set in this paper we give a comprehensive introduction to a new approach for the estimation of subspace arrangements this is known as generalized principal component analysis gpca in particular we provide a comprehensive summary of important algebraic properties and statistical facts that are crucial for making the inference of subspace arrangements both efficient and robust even when the given data are corrupted by noise or contaminated with outliers this new method in many ways improves and generalizes extant methods for modeling or clustering mixed data there have been successful applications of this new method to many real world problems in computer vision image processing and system identification in this paper we will examine several of those representative applications this paper is intended to be expository in nature however in order that this may serve as a more complete reference for both theoreticians and practitioners we take the liberty of filling in several gaps between the theory and the practice in the existing literature

n dimensional tensor voting and application to epipolar geometry estimation we address the problem of epipolar geometry estimation by formulating it as one of hyperplane inference from a sparse and noisy point set in an 8d space given a set of noisy point correspondences in two images of a static scene without correspondences even in the presence of moving objects our method extracts good matches and rejects outliers the methodology is novel and unconventional since unlike most other methods optimizing certain scalar objective functions our approach does not involve initialization or any iterative search in the parameter space therefore it is free of the problem of local optima or poor convergence further since no search is involved it is unnecessary to impose simplifying assumption to the scene being analyzed for reducing the search complexity subject to the general epipolar constraint only we detect wrong matches by a computation scheme 8d tensor voting which is an instance of the more general n dimensional tensor voting framework in essence the input set of matches is first transformed into a sparse 8d point set dense 8d tensor kernels are then used to vote for the most salient hyperplane that captures all inliers inherent in the input with this filtered set of matches the normalized eight point algorithm can be used to estimate the fundamental matrix accurately by making use of efficient data structure and locality our method is both time and space efficient despite the higher dimensionality we demonstrate the general usefulness of our method using example image pairs for aerial image analysis with widely different views and from nonstatic 3d scenes each example contains a considerable number of wrong matches

sequential updating of projective and affine structure from motion a structure from motion algorithm is described which recovers structure and camera position modulo a projective ambiguity camera calibration is not required and camera parameters such as focal length can be altered freely during motion the structure is updated sequentially over an image sequence in contrast to schemes which employ a batch process a specialisation of the algorithm to recover structure and camera position modulo an affine transformation is described together with a method to periodically update the affine coordinate frame to prevent drift over time we describe the constraint used to obtain this specialisation structure is recovered from image corners detected and matched automatically and reliably in real image sequences results are shown for reference objects and indoor environments and accuracy of recovered structure is fully evaluated and compared for a number of reconstruction schemes a specific application of the work is demonstrated affine structure is used to compute free space maps enabling navigation through unstructured environments and avoidance of obstacles the path planning involves only affine constructions

interacting geometric priors for robust multimodel fitting recent works on multimodel fitting are often formulated as an energy minimization task where the energy function includes fitting error and regularization terms such as low level spatial smoothness and model complexity in this paper we introduce a novel energy with high level geometric priors that consider interactions between geometric models such that certain preferred model configurations may be induced we argue that in many applications such prior geometric properties are available and should be fruitfully exploited for example in surface fitting to point clouds the building walls are usually either orthogonal or parallel to each other our proposed energy function is useful in dealing with unknown distributions of data errors and outliers which are often the factors leading to biased estimation furthermore the energy can be efficiently minimized using the expansion move method we evaluate the performance on several vision applications using real data sets experimental results show that our method outperforms the state of the art methods without significant increase in computation

estimation of 2d displacement field based on affine geometric invariance and scene constraints in the paper a novel approach using affine transfer and scene constraint for estimation of 2d displacement field was developed in this approach we derived a system of 5 linear equations for computing corresponding point of any image point via the utilisation of affine invariant subsequently the characteristics of such a linear system was thoroughly studied and the way to obtain a reliable solution of the system via robust least squares rls approach in conjunction with total least squares technique was proposed in addition through the interpretation of the approach from both geometric point of view and numerical point of view we gave the limitation of the algorithm the limitation was then relaxed to a certain extent by using full fundamental matrix these findings were further verified through the experimental results

an algorithm for projective point matching in the presence of spurious points point matching is the task of finding correspondences between two sets of points such that the two sets of points are aligned with each other pure point matching uses only the location of the points to constrain the problem this is a problem with broad practical applications but it has only been well studied when the geometric transformation relating the two point sets is of a relatively low order here we present a heuristic local search algorithm that can find correspondences between point sets in two dimensions that are related by a projective transform point matching is a harder problem when spurious points appear in the sets to be matched we present a heuristic algorithm which minimizes the effects of spurious points

global propagation of affine invariant features for robust matching local invariant features have been successfully used in image matching to cope with viewpoint change partial occlusion and clutters however when these factors become too strong there will be a lot of mismatches due to the limited repeatability and discriminative power of features in this paper we present an efficient approach to remove the false matches and propagate the correct ones for the affine invariant features which represent the state of the art local invariance first a pair wise affine consistency measure is proposed to evaluate the consensus of the matches of affine invariant regions the measure takes into account both the keypoint location and the region shape size and orientation based on this measure a geometric filter is then presented which can efficiently remove the outliers from the initial matches and is robust to severe clutters and non rigid deformation to increase the correct matches we propose a global match refinement and propagation method that simultaneously finds a optimal group of local affine transforms to relate the features in two images the global method is capable of producing a quasi dense set of matches even for the weakly textured surfaces that suffer strong rigid transformation or non rigid deformation the strong capability of the proposed method in dealing with significant viewpoint change non rigid deformation and low texture objects is demonstrated in experiments of image matching object recognition and image based rendering

generalised relaxed radon transform gr2t for robust inference this paper introduces the generalised relaxed radon transform gr 2t as an extension to the generalised radon transform grt 1 this new modelling allows us to define a new framework for robust inference the resulting objective functions are probability density functions that can be chosen differentiable and that can be optimised using gradient methods one of this cost function is already widely used in the forms of the hough transform and generalised projection based m estimator and it is interpreted as a conditional density function on the latent variables of interest in addition the joint density function of the latent variables is also proposed as a cost function and it has the advantage of including a prior about the latent variable several applications including lines detection in images and volume reconstruction from silhouettes captured from multiple views are presented to underline the versatility of this framework

a reformulation of horton s laws for large river networks in terms of statistical self similarity the well known horton s laws are empirical observations on how the means of measurements for river networks and basins vary with horton strahler order it is now known that these laws are a consequence of an average sense self similarity in the bifurcation structure of river networks in this paper we present a reformulation of horton s laws which generalizes the familiar scaling of first moments or means to scaling of entire distributions we also present extensive data analysis which supports this reformulation and show that this feature is also exhibited by shreve s well known random topology model

model fitting with sufficient random sample coverage it has been observed previously that the number of iterations required to derive good model parameter values used by ransac like model estimators is too optimistic we present the derivation of an analytical formula that allows the calculation of the sufficient limit of iterations needed to obtain good parameter values with the prescribed probability for any number of model parameters it explains the values that had been found experimentally for certain numbers of model parameters by others very well furthermore the improvement that our approach of sufficient random sample coverage sursac offers in comparison to the original ransac algorithm as well as to its adaptive modification by hartley and zisserman is demonstrated with synthetic data for the case of a non linear model function over a wide range of outlier fractions and different ratios of inlier and outlier densities

a robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry abstract this paper proposes a robust approach to image matching by exploiting the only available geometric constraint namely the epipolar constraint the images are uncalibrated namely the motion between them and the camera parameters are not known thus the images can be taken by different cameras or a single camera at different time instants if we make an exhaustive search for the epipolar geometry the complexity is prohibitively high the idea underlying our approach is to use classical techniques correlation and relaxation methods in our particular implementation to find an initial set of matches and then use a robust technique the least median of squares lmeds to discard false matches in this set the epipolar geometry can then be accurately estimated using a meaningful image criterion more matches are eventually found as in stereo matching by using the recovered epipolar geometry a large number of experiments have been carried out and very good results have been obtained regarding the relaxation technique we define a new measure of matching support which allows a higher tolerance to deformation with respect to rigid transformations in the image plane and a smaller contribution for distant matches than for nearby ones a new strategy for updating matches is developed which only selects those matches having both high matching support and low matching ambiguity the update strategy is different from the classical winner take all which is easily stuck at a local minimum and also from loser take nothing which is usually very slow the proposed algorithm has been widely tested and works remarkably well in a scene with many repetitive patterns

an efficient solution to the five point relative pose problem an efficient algorithmic solution to the classical five point relative pose problem is presented the problem is to find the possible solutions for relative camera pose between two calibrated views given five corresponding points the algorithm consists of computing the coefficients of a tenth degree polynomial in closed form and subsequently finding its roots it is the first algorithm well suited for numerical implementation that also corresponds to the inherent complexity of the problem we investigate the numerical precision of the algorithm we also study its performance under noise in minimal as well as overdetermined cases the performance is compared to that of the well known 8 and 7 point methods and a 6 point scheme the algorithm is used in a robust hypothesize and test framework to estimate structure and motion in real time with low delay the real time system uses solely visual input and has been demonstrated at major conferences

impsac synthesis of importance sampling and random sample consensus this paper proposes a new method for recovery of epipolar geometry and feature correspondence between images which have undergone a significant deformation either due to large rotation or wide baseline of the cameras the method also encodes the uncertainty by providing an arbitrarily close approximation to the posterior distribution of the two view relation the method operates on a pyramid from coarse to fine resolution thus raising the problem of how to propagate information from one level to another in a statistically consistent way the distribution of the parameters at each resolution is encoded nonparametrically as a set of particles at the coarsest level a random sample consensus monte carlo markov chain ransac mcmc estimator is used to initialize this set of particles the posterior can then be approximated as a mixture of gaussians fitted to these particles the distribution at a coarser level influences the distribution at a finer level using the technique of sampling importance resampling sir and mcmc which allows for asymptotically correct approximations of the posterior distribution the estimate of the posterior distribution at the level above is being used as the importance sampling function to generate a new set of particles which can be further improved by mcmc it is shown that the method is superior to previous single resolution ransac style feature matchers

statistical bias of conic fitting and renormalization introducing a statistical model of noise in terms of the covariance matrix of the n vector we point out that the least squares conic fitting is statistically biased we present a new fitting scheme called renormalization for computing an unbiased estimate by automatically adjusting to noise relationships to existing methods are discussed and our method is tested using real and synthetic data

the dual bootstrap iterative closest point algorithm with application to retinal image registration motivated by the problem of retinal image registration this paper introduces and analyzes a new registration algorithm called dual bootstrap iterative closest point dual bootstrap icp the approach is to start from one or more initial low order estimates that are only accurate in small image regions called bootstrap regions in each bootstrap region the algorithm iteratively 1 refines the transformation estimate using constraints only from within the bootstrap region 2 expands the bootstrap region and 3 tests to see if a higher order transformation model can be used stopping when the region expands to cover the overlap between images steps 1 and 3 the bootstrap steps are governed by the covariance matrix of the estimated transformation estimation refinement step 2 uses a novel robust version of the icp algorithm in registering retinal image pairs dual bootstrap icp is initialized by automatically matching individual vascular landmarks and it aligns images based on detected blood vessel centerlines the resulting quadratic transformations are accurate to less than a pixel on tests involving approximately 6000 image pairs it successfully registered 99 5 of the pairs containing at least one common landmark and 100 of the pairs containing at least one common landmark and at least 35 image overlap

stochastic maximum principle for optimal control of a class of nonlinear spdes with dissipative drift we prove a version of the stochastic maximum principle in the sense of pontryagin for the finite horizon optimal control of a stochastic partial differential equation driven by an infinite dimensional additive noise in particular we treat the case in which the nonlinear term is of nemytskii type dissipative and with polynomial growth the performance functional to be optimized is fairly general and may depend on point evaluation of the controlled equation the results can be applied to a large class of nonlinear parabolic equations such as reaction diffusion equations

optimal motion and structure estimation the causes of existing linear algorithms exhibiting various high sensitivities to noise are analyzed it is shown that even a small pixel level perturbation may override the epipolar information that is essential for the linear algorithms to distinguish different motions this analysis indicates the need for optimal estimation in the presence of noise methods are introduced for optimal motion and structure estimation under two situations of noise distribution known and unknown computationally the optimal estimation amounts to minimizing a nonlinear function for the correct convergence of this nonlinear minimization a two step approach is used the first step is using a linear algorithm to give a preliminary estimate for the parameters the second step is minimizing the optimal objective function starting from that preliminary estimate as an initial guess a remarkable accuracy improvement has been achieved by this two step approach over using the linear algorithm alone

geometry of river networks i scaling fluctuations and deviations this paper is the first in a series of three papers investigating the detailed geometry of river networks branching networks are a universal structure employed in the distribution and collection of material large scale river networks mark an important class of two dimensional branching networks being not only of intrinsic interest but also a pervasive natural phenomenon in the description of river network structure scaling laws are uniformly observed reported values of scaling exponents vary suggesting that no unique set of scaling exponents exists to improve this current understanding of scaling in river networks and to provide a fuller description of branching network structure here we report a theoretical and empirical study of fluctuations about and deviations from scaling we examine data for continent scale river networks such as the mississippi and the amazon and draw inspiration from a simple model of directed random networks we center our investigations on the scaling of the length of a subbasin s dominant stream with its area a characterization of basin shape known as hack s law we generalize this relationship to a joint probability density and provide observations and explanations of deviations from scaling we show that fluctuations about scaling are substantial and grow with system size we find strong deviations from scaling at small scales which can be explained by the existence of a linear network structure at intermediate scales we find slow drifts in exponent values indicating that scaling is only approximately obeyed and that universality remains indeterminate at large scales we observe a breakdown in scaling due to decreasing sample space and correlations with overall basin shape the extent of approximate scaling is significantly restricted by these deviations and will not be improved by increases in network resolution

robust parameter estimation in computer vision estimation techniques in computer vision applications must estimate accurate model parameters despite small scale noise in the data occasional large scale measurement errors outliers and measurements from multiple populations in the same data set increasingly robust estimation techniques some borrowed from the statistics literature and others described in the computer vision literature have been used in solving these parameter estimation problems ideally these techniques should effectively ignore the outliers and measurements from other populations treating them as outliers when estimating the parameters of a single population two frequently used techniques are least median of squares lms p j rousseeuw j amer statist assoc 79 1984 pp 871 880 and m estimators robust statistics the approach based on influence functions f r hampel et al john wiley 1986 robust statistics p j huber john wiley 1981 lms handles large fractions of outliers up to the theoretical limit of 50 for estimators invariant to affine changes to the data but has low statistical efficiency m estimators have higher statistical efficiency but tolerate much lower percentages of outliers unless properly initialized while robust estimators have been used in a variety of computer vision applications three are considered here in analysis of range images images containing depth or x y z measurements at each pixel instead of intensity measurements robust estimators have been used successfully to estimate surface model parameters in small image regions in stereo and motion analysis they have been used to estimate parameters of what is called the fundamental matrix which characterizes the relative imaging geometry of two cameras imaging the same scene recently robust estimators have been applied to estimating a quadratic image to image transformation model necessary to create a composite mosaic image from a series of images of the human retina in each case a straightforward application of standard robust estimators is insufficient and carefully developed extensions are used to solve the problem

the geometry and matching of lines and curves over multiple views this paper describes the geometry of imaged curves in two and three views multi view relationships are developed for lines conics and non algebraic curves the new relationships focus on determining the plane of the curve in a projective reconstruction and in particular using the homography induced by this plane for transfer from one image to another it is shown that given the fundamental matrix between two views and images of the curve in each view then the plane of a conic may be determined up to a two fold ambiguity but local curvature of a curve uniquely determines the plane it is then shown that given the trifocal tensor between three views this plane defines a homography map which may be used to transfer a conic or the curvature from two views to a third simple expressions are developed for the plane and homography in each case a set of algorithms are then described for automatically matching individual line segments and curves between images the algorithms use both photometric information and the multiple view geometric relationships for image pairs the homography facilitates the computation of a neighbourhood cross correlation based matching score for putative line curve correspondences for image triplets cross correlation matching scores are used in conjunction with line curve transfer based on the trifocal geometry to disambiguate matches algorithms are developed for both short and wide baselines the algorithms are robust to deficiencies in the segment extraction and partial occlusion experimental results are given for image pairs and triplets for varying motions between views and for different scene types the methods are applicable to line curve matching in stereo and trinocular rigs and as a starting point for line curve matching through monocular image sequences

simultaneously fitting and segmenting multiple structure data with outliers we propose a robust fitting framework called adaptive kernel scale weighted hypotheses akswh to segment multiple structure data even in the presence of a large number of outliers our framework contains a novel scale estimator called iterative kth ordered scale estimator ikose ikose can accurately estimate the scale of inliers for heavily corrupted multiple structure data and is of interest by itself since it can be used in other robust estimators in addition to ikose our framework includes several original elements based on the weighting clustering and fusing of hypotheses akswh can provide accurate estimates of the number of model instances and the parameters and the scale of each model instance simultaneously we demonstrate good performance in practical applications such as line fitting circle fitting range image segmentation homography estimation and two view based motion segmentation using both synthetic data and real images

mutual information theory for adaptive mixture models many pattern recognition systems need to estimate an underlying probability density function pdf mixture models are commonly used for this purpose in which an underlying pdf is estimated by a finite mixing of distributions the basic computational element of a density mixture model is a component with a nonlinear mapping function which takes part in mixing selecting an optimal set of components for mixture models is important to ensure an efficient and accurate estimate of an underlying pdf previous work has commonly estimated an underlying pdf based on the information contained in patterns in this paper mutual information theory is employed to measure whether two components are statistically dependent if a component has small mutual information it is statistically independent of the other components hence that component makes a significant contribution to the system pdf and should not be removed however if a particular component has large mutual information it is unlikely to be statistically independent of the other components and may be removed without significant damage to the estimated pdf continuing to remove components with large and positive mutual information will give a density mixture model with an optimal structure which is very close to the true pdf

nonlinear mean shift over riemannian manifolds the original mean shift algorithm is widely applied for nonparametric clustering in vector spaces in this paper we generalize it to data points lying on riemannian manifolds this allows us to extend mean shift based clustering and filtering techniques to a large class of frequently occurring non vector spaces in vision we present an exact algorithm and prove its convergence properties as opposed to previous work which approximates the mean shift vector the computational details of our algorithm are presented for frequently occurring classes of manifolds such as matrix lie groups grassmann manifolds essential matrices and symmetric positive definite matrices applications of the mean shift over these manifolds are shown

a voting based computational framework for visual motion analysis and interpretation most approaches for motion analysis and interpretation rely on restrictive parametric models and involve iterative methods which depend heavily on initial conditions and are subject to instability further difficulties are encountered in image regions where motion is not smooth typically around motion boundaries this work addresses the problem of visual motion analysis and interpretation by formulating it as an inference of motion layers from a noisy and possibly sparse point set in a 4d space the core of the method is based on a layered 4d representation of data and a voting scheme for affinity propagation the inherent problem caused by the ambiguity of 2d to 3d interpretation is usually handled by adding additional constraints such as rigidity however enforcing such a global constraint has been problematic in the combined presence of noise and multiple independent motions by decoupling the processes of matching outlier rejection segmentation and interpretation we extract accurate motion layers based on the smoothness of image motion and then locally enforce rigidity for each layer in order to infer its 3d structure and motion the proposed framework is noniterative and consistently handles both smooth moving regions and motion discontinuities without using any prior knowledge of the motion model

on hack s law hack s law is reviewed emphasizing its implications for the elongation of river basins as well as its connections with their fractal characteristics the relation between hack s law and the internal structure of river basins is investigated experimentally through digital elevation models it is found that hack s exponent elongation and some relevant fractal characters are closely related the self affine character of basin boundaries is shown to be connected to the power law decay of the probability of total contributing areas at any link and to hack s law an explanation for hack s law is derived from scaling arguments from the results we suggest that a statistical framework referring to the scaling invariance of the entire basin structure should be used in the interpretation of hack s law

bias in robust estimation caused by discontinuities and multiple structures when fitting models to data containing multiple structures such as when fitting surface patches to data taken from a neighborhood that includes a range discontinuity robust estimators must tolerate both gross outliers and pseudo outliers pseudo outliers are outliers to the structure of interest but inliers to a different structure they differ from gross outliers because of their coherence such data occurs frequently in computer vision problems including motion estimation model fitting and range data analysis the focus in this paper is the problem of fitting surfaces near discontinuities in range data to characterize the performance of least median of the squares least trimmed squares m estimators hough transforms ransac and minpran on this type of data the pseudo outlier bias metric is developed using techniques from the robust statistics literature and it is used to study the error in robust fits caused by distributions modeling various types of discontinuities the results show each robust estimator to be biased at small but substantial discontinuities they also show the circumstances under which different estimators are most effective most importantly the results imply present estimators should be used with care and new estimators should be developed

real time epipolar geometry estimation of binocular stereo heads stereo is an important cue for visually guided robots while moving around in the world such a robot can use dynamic fixation to overcome limitations in image resolution and field of view in this paper a binocular stereo system capable of dynamic fixation is presented the external calibration is performed continuously taking temporal consistency into consideration greatly simplifying the process the essential matrix which is estimated in real time is used to describe the epipolar geometry it will be shown how outliers can be identified and excluded from the calculations an iterative approach based on a differential model of the optical flow commonly used in structure from motion is also presented and tested towards the essential matrix the iterative method will be shown to be superior in terms of both computational speed and robustness when the vergence angles are less than about 15 for larger angles the differential model is insufficient and the essential matrix is preferably used instead

a probabilistic criterion to detect rigid point matches between two images and estimate the fundamental matrix the perspective projections of n physical points on two views stereovision are constrained as soon as n 8 however to prove in practice the existence of a rigid motion between two images more than 8 point matches are desirable in order to compensate for the limited accuracy of the matches in this paper we propose a computational definition of rigidity and a probabilistic criterion to rate the meaningfulness of a rigid set as a function of both the number of pairs of points n and the accuracy of the matches this criterion yields an objective way to compare say precise matches of a few points and approximate matches of a lot of points it gives a yes no answer to the question could this rigid points correspondence have occurred by chance since it guarantees that the expected number of meaningful rigid sets found by chance in a random distribution of points is as small as desired it also yields absolute accuracy requirements for rigidity detection in the case of non matched points and optimal values of n depending on the expected accuracy of the matches and on the proportion of outliers we use it to build an optimized random sampling algorithm that is able to detect a rigid motion and estimate the fundamental matrix when the set of point matches contains up to 90 of outliers which outperforms the best currently known methods like m estimators lmeds classical ransac and tensor voting

robust adaptive scale parametric model estimation for computer vision robust model fitting essentially requires the application of two estimators the first is an estimator for the values of the model parameters the second is an estimator for the scale of the noise in the inlier data indeed we propose two novel robust techniques the two step scale estimator tsse and the adaptive scale sample consensus assc estimator tsse applies nonparametric density estimation and density gradient estimation techniques to robustly estimate the scale of the inliers the assc estimator combines random sample consensus ransac and tsse using a modified objective function that depends upon both the number of inliers and the corresponding scale assc is very robust to discontinuous signals and data with multiple structures being able to tolerate more than 80 percent outliers the main advantage of assc over ransac is that prior knowledge about the scale of inliers is not needed assc can simultaneously estimate the parameters of a model and the scale of the inliers belonging to that model experiments on synthetic data show that assc has better robustness to heavily corrupted data than least median squares lmeds residual consensus resc and adaptive least kth order squares alks we also apply assc to two fundamental computer vision tasks range image segmentation and robust fundamental matrix estimation experiments show very promising results

a generalized kernel consensus based robust estimator in this paper we present a new adaptive scale kernel consensus askc robust estimator as a generalization of the popular and state of the art robust estimators such as random sample consensus ransac adaptive scale sample consensus assc and maximum kernel density estimator mkde the askc framework is grounded on and unifies these robust estimators using nonparametric kernel density estimation theory in particular we show that each of these methods is a special case of askc using a specific kernel like these methods askc can tolerate more than 50 percent outliers but it can also automatically estimate the scale of inliers we apply askc to two important areas in computer vision robust motion estimation and pose estimation and show comparative results on both synthetic and real data

generalized principal component analysis gpca this paper presents an algebro geometric solution to the problem of segmenting an unknown number of subspaces of unknown and varying dimensions from sample data points we represent the subspaces with a set of homogeneous polynomials whose degree is the number of subspaces and whose derivatives at a data point give normal vectors to the subspace passing through the point when the number of subspaces is known we show that these polynomials can be estimated linearly from data hence subspace segmentation is reduced to classifying one point per subspace we select these points optimally from the data set by minimizing certain distance function thus dealing automatically with moderate noise in the data a basis for the complement of each subspace is then recovered by applying standard pca to the collection of derivatives normal vectors extensions of gpca that deal with data in a high dimensional space and with an unknown number of subspaces are also presented our experiments on low dimensional data show that gpca outperforms existing algebraic algorithms based on polynomial factorization and provides a good initialization to iterative techniques such as k subspaces and expectation maximization we also present applications of gpca to computer vision problems such as face clustering temporal video segmentation and 3d motion segmentation from point correspondences in multiple affine views

guided mlesac faster image transform estimation by using matching priors mlesac is an established algorithm for maximum likelihood estimation by random sampling consensus devised for computing multiview entities like the fundamental matrix from correspondences between image features a shortcoming of the method is that it assumes that little is known about the prior probabilities of the validities of the correspondences this paper explains the consequences of that omission and describes how the algorithm s theoretical standing and practical performance can be enhanced by deriving estimates of these prior probabilities using the priors in guided mlesac is found to give an order of magnitude speed increase for problems where the correspondences are described by one image transformation and clutter this paper describes two further modifications to guided mlesac the first shows how all putative matches rather than just the best from a particular feature can be taken forward into the sampling stage albeit at the expense of additional computation the second suggests how to propagate the output from one frame forward to successive frames the additional information makes guided mlesac computationally realistic at video rates for correspondence sets modeled by two transformations and clutter

a quasi dense approach to surface reconstruction from uncalibrated images this paper proposes a quasi dense approach to 3d surface model acquisition from uncalibrated images first correspondence information and geometry are computed based on new quasi dense point features that are resampled subpixel points from a disparity map the quasi dense approach gives more robust and accurate geometry estimations than the standard sparse approach the robustness is measured as the success rate of full automatic geometry estimation with all involved parameters fixed the accuracy is measured by a fast gauge free uncertainty estimation algorithm the quasi dense approach also works for more largely separated images than the sparse approach therefore it requires fewer images for modeling more importantly the quasi dense approach delivers a high density of reconstructed 3d points on which a surface representation can be reconstructed this fills the gap of insufficiency of the sparse approach for surface reconstruction essential for modeling and visualization applications second surface reconstruction methods from the given quasi dense geometry are also developed the algorithm optimizes new unified functionals integrating both 3d quasi dense points and 2d image information including silhouettes combining both 3d data and 2d images is more robust than the existing methods using only 2d information or only 3d data an efficient bounded regularization method is proposed to implement the surface evolution by level set methods its properties are discussed and proven for some cases as a whole a complete automatic and practical system of 3d modeling from raw images captured by hand held cameras to surface representation is proposed extensive experiments demonstrate the superior performance of the quasi dense approach with respect to the standard sparse approach in robustness accuracy and applicability

mode seeking over permutations for rapid geometric model fitting in this paper we show how to exploit the statistics of permutations to perform rapid hypothesis sampling for robust geometric model fitting the permutations encapsulate data preferences for random model hypotheses and we demonstrate that such permutations exhibit a clustering based on the membership of inliers to genuine structures in the data we perform non parametric mode seeking in the space of permutations the results of which are used to derive a set of sampling distributions for minimal subset selection our method fully takes advantage of the allocated time by using only the most relevant subsets of the input data for hypothesis generation moreover it can naturally handle data with multiple structures a condition that is usually disastrous for other methods that rely on ad hoc inlier probabilities such as keypoint matching scores compared to others our method consistently returns a much lower time to first solution and median fitting error given the same run time

simultaneous two view epipolar geometry estimation and motion segmentation by 4d tensor voting we address the problem of simultaneous two view epipolar geometry estimation and motion segmentation from nonstatic scenes given a set of noisy image pairs containing matches of n objects we propose an unconventional efficient and robust method 4d tensor voting for estimating the unknown n epipolar geometries and segmenting the static and motion matching pairs into n independent motions by considering the 4d isotropic and orthogonal joint image space only two tensor voting passes are needed and a very high noise to signal ratio up to five can be tolerated epipolar geometries corresponding to multiple rigid motions are extracted in succession only two uncalibrated frames are needed and no simplifying assumption such as affine camera model or homographic model between images other than the pin hole camera model is made our novel approach consists of propagating a local geometric smoothness constraint in the 4d joint image space followed by global consistency enforcement for extracting the fundamental matrices corresponding to independent motions we have performed extensive experiments to compare our method with some representative algorithms to show that better performance on nonstatic scenes are achieved results on challenging data sets are presented

the development and comparison of robust methodsfor estimating the fundamental matrix this paper has two goals the first is to develop a variety of robust methods for the computation of the fundamental matrix the calibration free representation of camera motion the methods are drawn from the principal categories of robust estimators viz case deletion diagnostics m estimators and random sampling and the paper develops the theory required to apply them to non linear orthogonal regression problems although a considerable amount of interest has focussed on the application of robust estimation in computer vision the relative merits of the many individual methods are unknown leaving the potential practitioner to guess at their value the second goal is therefore to compare and judge the methods comparative tests are carried out using correspondences generated both synthetically in a statistically controlled fashion and from feature matching in real imagery in contrast with previously reported methods the goodness of fit to the synthetic observations is judged not in terms of the fit to the observations per se but in terms of fit to the ground truth a variety of error measures are examined the experiments allow a statistically satisfying and quasi optimal method to be synthesized which is shown to be stable with up to 50 percent outlier contamination and may still be used if there are more than 50 percent outliers performance bounds are established for the method and a variety of robust methods to estimate the standard deviation of the error and covariance matrix of the parameters are examined the results of the comparison have broad applicability to vision algorithms where the input data are corrupted not only by noise but also by gross outliers

least median of squares regression abstract classical least squares regression consists of minimizing the sum of the squared residuals many authors have produced more robust versions of this estimator by replacing the square by something else such as the absolute value in this article a different approach is introduced in which the sum is replaced by the median of the squared residuals the resulting estimator can resist the effect of nearly 50 of contamination in the data in the special case of simple regression it corresponds to finding the narrowest strip covering half of the observations generalizations are possible to multivariate location orthogonal regression and hypothesis testing in linear models

linear scale and rotation invariant matching matching visual patterns that appear scaled rotated and deformed with respect to each other is a challenging problem we propose a linear formulation that simultaneously matches feature points and estimates global geometrical transformation in a constrained linear space the linear scheme enables search space reduction based on the lower convex hull property so that the problem size is largely decoupled from the original hard combinatorial problem our method therefore can be used to solve large scale problems that involve a very large number of candidate feature points without using prepruning in the search this method is more robust in dealing with weak features and clutter we apply the proposed method to action detection and image matching our results on a variety of images and videos demonstrate that our method is accurate efficient and robust

a computational approach to edge detection this paper describes a computational approach to edge detection the success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points these goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution we define detection and localization criteria for a class of edges and present mathematical forms for these criteria as functionals on the operator impulse response a third criterion is then added to ensure that the detector has only one response to a single edge we use the criteria in numerical optimization to derive detectors for several common image features including step edges on specializing the analysis to step edges we find that there is a natural uncertainty principle between detection and localization performance which are the two main goals with this principle we derive a single operator shape which is optimal at any scale the optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a gaussian smoothed image we extend this simple detector using operators of several widths to cope with different signal to noise ratios in the image we present a general method called feature synthesis for the fine to coarse integration of information from operators at different scales finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge

ror rejection of outliers by rotations we address the problem of rejecting false matches of points between two perspective views the two views are taken from two arbitrary unknown positions and orientations we present an algorithm for identification of the false matches between the views the algorithm exploits the possibility of rotating one of the images to achieve some common behavior of the correct matches those matches that deviate from this common behavior turn out to be false matches our algorithm does not in any way use the image characteristics of the matched features in particular it avoids problems that cause the false matches in the first place the algorithm works even in cases where the percentage of false matches is as high as 85 percent the algorithm may be run as a post processing step on output from any point matching algorithm use of the algorithm may significantly improve the ratio of correct matches to incorrect matches we present the algorithm identify the conditions under which it works and present results of the test

robust estimation for range image segmentation and reconstruction this correspondence presents a segmentation and fitting method using a new robust estimation technique we present a robust estimation method with high breakdown point which can tolerate more than 80 of outliers the method randomly samples appropriate range image points in the current processing region and solves equations determined by these points for parameters of selected primitive type from k samples we choose one set of sample points that determines a best fit equation for the largest homogeneous surface patch in the region this choice is made by measuring a residual consensus resc using a compressed histogram method which is effective at various noise levels after we get the best fit surface parameters the surface patch can be segmented from the region and the process is repeated until no pixel left the method segments the range image into planar and quadratic surfaces the resc method is a substantial improvement over the least median squares method by using histogram approach to inferring residual consensus a genetic algorithm is also incorporated to accelerate the random search

exact two image structure from motion for two image structure from motion we present a simple exact expression for a least squares image reprojection error for finite motion that depends only on the motion optimal estimates of the structure and motion can be computed by minimizing this expression just over the motion parameters also we present a solution to the triangulation problem an exact explicit expression for the optimal structure estimate given the motion we identify a new ambiguity in recovering the structure for known motion we study the exact error s properties experimentally and demonstrate that it often has several local minima for forward or backward motion estimates our experiments also show that the reflected local minimum of oliensis 2001 and soatto et al 1998 occurs for large translational motions our exact results assume that the camera is calibrated and use a least squares image reprojection error that applies most naturally to a spherical imaging surface we approximately extend our approach to the standard least squares error in the image plane and uncalibrated cameras we present an improved version of the sampson error which gives better results experimentally

hypergraph modelling for geometric model fitting in this paper we propose a novel hypergraph based method called hf to fit and segment multi structural data the proposed hf formulates the geometric model fitting problem as a hypergraph partition problem based on a novel hypergraph model in the hypergraph model vertices represent data points and hyperedges denote model hypotheses the hypergraph with large and data determined degrees of hyperedges can express the complex relationships between model hypotheses and data points in addition we develop a robust hypergraph partition algorithm to detect sub hypergraphs for model fitting hf can effectively and efficiently estimate the number of and the parameters of model instances in multi structural data heavily corrupted with outliers simultaneously experimental results show the advantages of the proposed method over previous methods on both synthetic data and real images highlightswe propose a novel hypergraph based method to fit and segment multi structural data the proposed method includes a hypergraph model with large degrees of hyperedges the proposed method includes a robust hypergraph partition algorithm experimental results show that the proposed method is superior to some state of the art fitting methods

optimal bilinear control of stochastic nonlinear schrodinger equations mass sub critical case we study optimal control problems for stochastic nonlinear schrodinger equations in both the mass subcritical and critical case for general initial data of the minimal l 2 regularity we prove the existence and first order lagrange condition of an open loop control in particular these results apply to the stochastic nonlinear schrodinger equations with the critical quintic and cubic nonlinearities in dimensions one and two respectively furthermore we obtain uniform estimates of backward stochastic solutions in new spaces of type u 2 and v 2 adapted to evolution operators related to linear schrodinger equations with lower order perturbations these estimates yield a new temporal regularity of backward stochastic solutions which is crucial for the tightness of approximating controls induced by ekeland s variational principle

on stochastic optimal control in ferromagnetism a model is proposed to e g control the domain wall motion in ferromagnets in the presence of thermal fluctuations and the existence of an optimal stochastic control process is proved the convergence of a finite element approximation of the problem is shown in space dimension one which then allows one to apply pontryagin s maximum principle for this finite dimensional setting the resulting coupled system of forward backward stochastic differential equations is numerically solved by means of the stochastic gradient method to enable practical simulations

the joy of sampling a standard method for handling bayesian models is to use markov chain monte carlo methods to draw samples from the posterior we demonstrate this method on two core problems in computer vision structure from motion and colour constancy these examples illustrate a samplers producing useful representations for very large problems we demonstrate that the sampled representations are trustworthy using consistency checks in the experimental design the sampling solution to structure from motion is strictly better than the factorisation approach because it reports uncertainty on structure and position measurements in a direct ways it can identify tracking errorss and its estimates of covariance in marginal point position are reliable our colour constancy solution is strictly better than competing approaches because it reports uncertainty on surface colour and illuminant measurements in a direct ways it incorporates all available constraints on surface reflectance and on illumination in a direct ways and it integrates a spatial model of reflectance and illumination distribution with a rendering model in a natural way one advantage of a sampled representation is that it can be resampled to take into account other information we demonstrate the effect of knowing that in our colour constancy example a surface viewed in two different images is in fact the same object we conclude with a general discussion of the strengths and weaknesses of the sampling paradigm as a tool for computer vision

dense versus sparse approaches for estimating the fundamental matrix there are two main strategies for solving correspondence problems in computer vision sparse local feature based approaches and dense global energy based methods while sparse feature based methods are often used for estimating the fundamental matrix by matching a small set of sophistically optimised interest points dense energy based methods mark the state of the art in optical flow computation the goal of our paper is to show that this separation into different application domains is unnecessary and can be bridged in a natural way as a first contribution we present a new application of dense optical flow for estimating the fundamental matrix comparing our results with those obtained by feature based techniques we identify cases in which dense methods have advantages over sparse approaches motivated by these promising results we propose as a second contribution a new variational model that recovers the fundamental matrix and the optical flow simultaneously as the minimisers of a single energy functional in experiments we show that our coupled approach is able to further improve the estimates of both the fundamental matrix and the optical flow our results prove that dense variational methods can be a serious alternative even in classical application domains of sparse feature based approaches

robust estimation of dispersion matrices and principal components abstract this paper uses monte carlo methods to compare the performances of several robust procedures for estimating a correlation matrix and its principal components the estimators are formed either from separate bivariate analyses or by simultaneous manipulation of all variables by using techniques such as multivariate trimming and m estimation the m estimators stand up exceptionally well they and the multivariate trimming procedure are especially effective at estimating the principal components including a near singularity however the m estimators can break down relatively easily when the dimensionality is large and the outliers are asymmetric with missing data the element wise approach becomes more attractive

adaptive outlier elimination in image registration using genetic programming abstract in feature based methods outlier removal plays an important role in attaining a reasonable accuracy for image registration in this paper we propose a genetic programming gp based adaptive method for outlier removal first features are extracted through the scale invariant feature transform sift from the reference and sensed images which were initially matched using euclidean distance the classification of feature points into inliers and outliers is done in two stages in the first stage feature vectors are computed using various distance and angle information feature points are categorized into three groups inliers outliers and non classified feature ncf points in the second stage a gp based classifier is developed to classify ncf points into inliers and outliers the gp based function takes features as an input feature vector and provides a scalar output by combining features with arithmetic operations finally registration is done by eliminating the outliers the effectiveness of the proposed outlier removal method is analyzed through the classification and positional accuracy the experimental results show a considerable improvement in the registration accuracy

a multi frame approach to visual motion perception one of the main issues in the area of motion estimation given the correspondences of some features in a sequence of images is sensitivity to error in the input the main way to attack the problem as with several other problems in science and engineering is redundancy in the data up to now all the algorithms developed either used two frames or depended on assumptions about the motion or the shape of the scene we present in this paper an algorithm based on multiple frames that employs only the rigidity assumption is simple and mathematically elegant and experimentally proves to be a major improvement over the two frame algorithms the algorithm does minimization of the squared error which we prove equivalent to an eigenvalue minimization problem one of the side effects of this mean square method is that the algorithm can have a very descriptive physical interpretation in terms of the loaded spring model

heteroscedastic regression in computer vision problems with bilinear constraint we present an algorithm to estimate the parameters of a linear model in the presence of heteroscedastic noise i e each data point having a different covariance matrix the algorithm is motivated by the recovery of bilinear forms one of the fundamental problems in computer vision which appears whenever the epipolar constraint is imposed or a conic is fit to noisy data points we employ the errors in variables eiv model and show why already at moderate noise levels most available methods fail to provide a satisfactory solution the improved behavior of the new algorithm is due to two factors taking into account the heteroscedastic nature of the errors arising from the linearization of the bilinear form and the use of generalized singular value decomposition gsvd in the computations the performance of the algorithm is compared with several methods proposed in the literature for ellipse fitting and estimation of the fundamental matrix it is shown that the algorithm achieves the accuracy of nonlinear optimization techniques at much less computational cost

robust adaptive segmentation of range images we propose a novel image segmentation technique using the robust adaptive least kth order squares alks estimator which minimizes the kth order statistics of the squares of residuals the optimal value of k is determined from the data and the procedure detects the homogeneous surface patch representing the relative majority of the pixels the alks shows a better tolerance to structured outliers than other recently proposed similar techniques the performance of the new fully autonomous range image segmentation algorithm is compared to several other methods

estimation of planar curves surfaces and nonplanar space curves defined by implicit equations with applications to edge and range image segmentation the author addresses the problem of parametric representation and estimation of complex planar curves in 2 d surfaces in 3 d and nonplanar space curves in 3 d curves and surfaces can be defined either parametrically or implicitly with the latter representation used here a planar curve is the set of zeros of a smooth function of two variables x y a surface is the set of zeros of a smooth function of three variables x y z and a space curve is the intersection of two surfaces which are the set of zeros of two linearly independent smooth functions of three variables x y z for example the surface of a complex object in 3 d can be represented as a subset of a single implicit surface with similar results for planar and space curves it is shown how this unified representation can be used for object recognition object position estimation and segmentation of objects into meaningful subobjects that is the detection of interest regions that are more complex than high curvature regions and hence more useful as features for object recognition

robust regression methods for computer vision a review regression analysis fitting a model to noisy data is a basic technique in computer vision robust regression methods that remain reliable in the presence of various types of noise are therefore of considerable importance we review several robust estimation techniques and describe in detail the least median of squares lmeds method the method yields the correct result even when half of the data is severely corrupted its efficiency in the presence of gaussian noise can be improved by complementing it with a weighted least squares based procedure the high time complexity of the lmeds algorithm can be reduced by a monte carlo type speed up technique we discuss the relationship of lmeds with the ransac paradigm and its limitations in the presence of noise corrupting all the data and we compare its performance with the class of robust m estimators references to published applications of robust techniques in computer vision are also given

a review on egomotion by means of differential epipolar geometry applied to the movement of a mobile robot the estimation of camera egomotion is an old problem in computer vision since the 1980s many approaches based on both the discrete and the differential epipolar constraint have been proposed the discrete case is used mainly in self calibrated stereoscopic systems whereas the differential case deals with a single moving camera this article surveys several methods for 3d motion estimation unifying the mathematics convention which are then adapted to the common case of a mobile robot moving on a plane experimental results are given on synthetic data covering more than 0 5 million estimations these surveyed algorithms have been programmed and are available on the internet

inferring segmented dense motion layers using 5d tensor voting we present a novel local spatiotemporal approach to produce motion segmentation and dense temporal trajectories from an image sequence a common representation of image sequences is a 3d spatiotemporal volume x y t and its corresponding mathematical formalism is the fiber bundle however directly enforcing the spatiotemporal smoothness constraint is difficult in the fiber bundle representation thus we convert the representation into a new 5d space x y t vx vy with an additional velocity domain where each moving object produces a separate 3d smooth layer the smoothness constraint is now enforced by extracting 3d layers using the tensor voting framework in a single step that solves both correspondence and segmentation simultaneously motion segmentation is achieved by identifying those layers and the dense temporal trajectories are obtained by converting the layers back into the fiber bundle representation we proceed to address three applications tracking mosaic and 3d reconstruction that are hard to solve from the video stream directly because of the segmentation and dense matching steps but become straightforward with our framework the approach does not make restrictive assumptions about the observed scene or camera motion and is therefore generally applicable we present results on a number of data sets

hop diffusion monte carlo for epipolar geometry estimation between very wide baseline images we present a monte carlo approach for epipolar geometry estimation that efficiently searches for minimal sets of inlier correspondences in the presence of many outliers in the putative correspondence set a condition that is prevalent when we have wide baselines significant scale changes rotations in depth occlusion and repeated patterns the proposed monte carlo algorithm uses balanced local and global search blogs to find the best minimal set of correspondences the local search is a diffusion process using joint feature distributions that captures the dependencies among the correspondences and the global search is a hopping search process across the minimal set space controlled by photometric properties using a novel experimental protocol that involves computing errors for manually marked ground truth points and images with outlier rates as high as 90 percent we find that blogs is better than related approaches such as mapsac 1 napsac 2 and beem 3 blogs results are of similar quality as other approaches but blogs generate them in 10 times fewer iterations the time per iteration for blogs is also the lowest among the ones we studied

implementing a multi model estimation method we revisit the problem of parameter estimation in computer vision reconsidering and implementing what may be called the kanatani s estimation method presented here as a simple optimisation problem so a without any direct reference to a probabilistic framework but b considering i non linear implicit measurement equations and parameter constraints plus ii robust estimation in the presence of outliers and iii multi model comparisons here a a projection algorithm based on generalisations of square root decompositions allows an efficient and numerically stable local resolution of a set of non linear equations on the other hand b a robust estimation module of a hierarchy of non linear models has been designed and validated a step ahead c the software architecture of the estimation module is discussed with the goal of being integrated in reactive software environments or within applications with time constraints while an experimentation considering the parameterisation of retinal displacements between two views is proposed as an illustration of the estimation module