localization and homing using combinations of model views abstract navigation involves recognizing the environment identifying the current position within the environment and reaching particular positions we present a method for localization the act of recognizing the environment positioning the act of computing the exact coordinates of a robot in the environment and homing the act of returning to a previously visited position from visual input the method is based on representing the scene as a set of 2d views and predicting the appearances of novel views by linear combinations of the model views the method accurately approximates the appearance of scenes under weak perspective projection analysis of this projection as well as experimental results demonstrate that in many cases this approximation is sufficient to accurately describe the scene when weak perspective approximation is invalid either a larger number of models can be acquired or an iterative solution to account for the perspective distortions can be employed the method has several advantages over other approaches it uses relatively rich representations the representations are 2d rather than 3d and localization can be done from only a single 2d view without calibration the same principal method is applied for both the localization and positioning problems and a simple qualitative algorithm for homing is derived from this method

model based object pose in 25 lines of code in this paper we describe a method for finding the pose of an object from a single image we assume that we can detect and match in the image four or more noncoplanar feature points of the object and that we know their relative geometry on the object the method combines two algorithms the first algorithm pos pose from orthography and scaling approximates the perspective projection with a scaled orthographic projection and finds the rotation matrix and the translation vector of the object by solving a linear system the second algorithm posit pos with iterations uses in its iteration loop the approximate pose found by pos in order to compute better scaled orthographic projections of the feature points then applies pos to these projections instead of the original image projections posit converges to accurate pose measurements in a few iterations posit can be used with many feature points at once for added insensitivity to measurement errors and image noise compared to classic approaches making use of newton s method posit does not require starting from an initial guess and computes the pose using an order of magnitude fewer floating point operations it may therefore be a useful alternative for real time operation when speed is not an issue posit can be written in 25 lines or less in mathematica the code is provided in an appendix

perceptual grouping for generic recognition we address the problem of recognition of generic objects from a single intensity image this precludes the use of purely geometric methods which assume that models are geometrically and precisely designed instead we propose to use descriptions in terms of features and their qualitative geometric relationships to succeed it is clear that these features need to be high level rather than points or lines we propose to detect groups using perceptual organization criteria such as proximity symmetry parallelism and closure the detection of these features is performed in an efficient way using proximity indexing since many groups are created we also perform selection of relevant groups by organizing them into sets of similar perceptual content finally we present an implementation of a recognition system using these sets as primitives it is an efficient colored graph matching algorithm using the adjacency matrix representation of a graph using indexing we retrieve matching hypotheses which are verified against each other with respect to topological constraints groups of consistent hypotheses represent detected model instances in a scene the complete system is illustrated on real images we also discuss further extensions

recognition using region correspondences recognition systems attempt to recover information about the identity of observed objects and their location in the environment a fundamental problem in recognition is pose estimation this is the problem of using a correspondence between some portions of an object model and some portions of an image to determine whether the image contains an instance of the object and in case it does to determine the transformation that relates the model to the image the current approaches to this problem are divided into methods that use global properties of the object e g centroid and moments of inertia and methods that use local properties of the object e g corners and line segments global properties are sensitive to occlusion and specifically to self occlusion local properties are difficult to locate reliably and their matching involves intensive computation we present a novel method for recognition that uses region information in our approach the model and the image are divided into regions given a match between subsets of regions without any explicit correspondence between different pieces of the regions the alignment transformation is computed the method applies to planar objects under similarity affine and projective transformations and to projections of 3 d objects undergoing affine and projective transformations the new approach combines many of the advantages of the previous two approaches while avoiding some of their pitfalls like the global methods our approach makes use of region information that reflects the true shape of the object but like local methods our approach can handle occlusion

cross weighted moments and affine invariants for image registration and matching a framework for deriving a class of new global affine invariants for both object matching and positioning based on a novel concept of cross weighted moments with fractional weights is presented the fractional weight factor allows for a more flexible range to balance between the capability to discriminate between objects that differ only in small shape details and the sensitivity of small shape details to the presence of the noise moreover it makes it possible to arrive at low order zero order affine invariants that are more robust than those derived from higher order regular moments the affine transformation parameters are recovered from the zero and the first order cross weighted moments without requiring any feature point correspondence information the equations used to find the affine transformation parameters are linear algebraic the sensitivity of the cross weighted moment invariants to noise missing data and perspective effects is shown on real images

model based recognition of 3d objects from single images in this work we treat major problems of object recognition which have received relatively little attention lately among them are the loss of depth information in the projection from a 3d object to a single 2d image and the complexity of finding feature correspondences between images we use geometric invariants to reduce the complexity of these problems there are no geometric invariants of a projection from 3d to 2d however given certain modeling assumptions about the 3d object such invariants can be found the modeling assumptions can be either a particular model or a generic assumption about a class of models here we use such assumptions for single view recognition we find algebraic relations between the invariants of a 3d model and those of its 2d image under general projective projection these relations can be described geometrically as invariant models in a 3d invariant space illuminated by invariant light rays and projected onto an invariant version of the given image we apply the method to real images

c ondensation conditional density propagation forvisual tracking the problem of tracking curves in dense visual clutter is challenging kalman filtering is inadequate because it is based on gaussian densities which being unimo dal cannot represent simultaneous alternative hypotheses the condensation algorithm uses factored sampling previously applied to the interpretation of static images in which the probability distribution of possible interpretations is represented by a randomly generated set condensation uses learned dynamical models together with visual observations to propagate the random set over time the result is highly robust tracking of agile motion notwithstanding the use of stochastic methods the algorithm runs in near real time

automatic localization of craniofacial landmarks for assisted cephalometry in this paper we propose a system for localization of cephalometric landmarks the process of localization is carried out in two steps deriving a smaller expectation window for each landmark using a trained neuro fuzzy system nfs then applying a template matching algorithm to pin point the exact location of the landmark four points are located on each image using edge detection the four points are used to extract more features such as distances shifts and rotation angles of the skull limited numbers of representative groups that will be used for training are selected based on k means clustering the most effective features are selected based on a fisher discriminant for each feature set using fuzzy linguistics if then rules membership degree is assigned to each of the selected features and fed to the fns the fns is trained utilizing gradient descent to learn the relation between the sizes rotations and translations of landmarks and their locations the data for training is obtained manually from one image from each cluster images whose features are located closer to the center of their cluster are used for extracting data for the training set the expected locations on target images can then be predicted using the trained fns for each landmark a parametric template space is constructed from a set of templates extracted from several images based on the clarity of the landmark in that image the template is matched to the search windows to find the exact location of the landmark decomposition of landmark shapes is used to desensitize the algorithm to size differences the system is trained to locate 20 landmarks on a database of 565 images preliminary results show a recognition rate of more than 90

motion and structure from orthographic projections s ullman s classical 1979 results on motion structure from orthographic views are revisited specifically two results are presented 1 two orthographic views allow an uncountably infinite number of solutions to motion structure of a rigid body and 2 a linear algorithm for solving motion structure from four point correspondences over three views

morphable surface models we describe a novel automatic technique for finding a dense correspondence between a pair of n dimensional surfaces with arbitrary topologies this method employs a different formulation than previous correspondence algorithms such as optical flow and includes images as a special case we use this correspondence algorithm to build morphable surface models an extension of morphable models from examples we present a method for matching the model to new surfaces and demonstrate their use for analysis synthesis and clustering

learning affine transformations under the assumption of weak perspective two views of the same planar object are related through an affine transformation in this paper we consider the problem of training a simple neural network to learn to predict the parameters of the affine transformation although the proposed scheme has similarities with other neural network schemes its practical advantages are more profound first of all the views used to train the neural network are not obtained by taking pictures of the object from different viewpoints instead the training views are obtained by sampling the space of affine transformed views of the object this space is constructed using a single view of the object fundamental to this procedure is a methodology based on singular value decomposition svd and interval arithmetic ia for estimating the ranges of values that the parameters of affine transformation can assume second the accuracy of the proposed scheme is very close to that of a traditional least squares approach with slightly better space and time requirements a front end stage to the neural network based on principal components analysis pca shows to increase its noise tolerance dramatically and also to guides us in deciding how many training views are necessary in order for the network to learn a good noise tolerant mapping the proposed approach has been tested using both artificial and real data

when is it possible to identify 3d objects from single images using class constraints one approach to recognizing objects seen from arbitrary viewpoint is by extracting invariant properties of the objects from single images such properties are found in images of 3d objects only when the objects are constrained to belong to certain classes e g bilaterally symmetric objects existing studies that follow this approach propose how to compute invariant representations for a handful of classes of objects a fundamental question regarding the invariance approach is whether it can be applied to a wide range of classes to answer this question it is essential to study the set of classes for which invariance exists this paper introduces a new method for determining the existence of invariant functions for classes of objects together with the set of images from which these invariants can be computed we develop algebraic tests that determine whether the objects in a given class can be identified from single images these tests apply to classes of objects undergoing affine projection in addition these tests allow us to determine the set of views of the objects which are degenerate we apply these tests to several classes of objects and determine which of them is identifiable and which of their views are degenerate

brief a set theoretic approach for time to contact estimation in dynamic vision this paper deals with the estimation of the time to contact in dynamic vision it is well known that differential invariants of the image velocity field can be used to characterize the shape changes of objects in the scene due to relative motion between the observer and the scene under the hypothesis of constant velocity along the optical axis the time to contact turns out to be a function of the area enclosed by the object contour and its time derivative in the paper a novel approach based on set membership estimation theory is proposed to estimate the variables involved in the computation of the time to contact both errors in the motion model and image measurement noise are described as unknown but bounded disturbances without requiring any statistical assumption the proposed technique allows for the computation of guaranteed bounds on the time to contact estimates in finite time a crucial issue in all problems where a robust evaluation of the time to contact is in order

lambertian reflectance and linear subspaces we prove that the set of all lambertian reflectance functions the mapping from surface normals to intensities obtained with arbitrary distant light sources lies close to a 9d linear subspace this implies that in general the set of images of a convex lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low dimensional linear subspace explaining prior empirical results we also provide a simple analytic characterization of this linear space we obtain these results by representing lighting using spherical harmonics and describing the effects of lambertian materials as the analog of a convolution these results allow us to construct algorithms for object recognition based on linear methods as well as algorithms that use convex optimization to enforce nonnegative lighting functions we also show a simple way to enforce nonnegative lighting when the images of an object lie near a 4d linear space we apply these algorithms to perform face recognition by finding the 3d model that best matches a 2d query image

morphable models for the analysis and synthesis of complex motion patterns the linear combination of prototypical views provides a powerful approach for the recognition and the synthesis of images of stationary three dimensional objects in this article we present initial results that demonstrate that similar ideas can be developed for the recognition and synthesis of complex motion patterns we present a technique that permits to represent complex motion or action patterns by linear combinations of a small number of prototypical image sequences we demonstrate the applicability of this new approach for the synthesis and analysis of biological motion using simulated and real video data from different locomotion patterns our results show that complex motion patterns are embedded in pattern spaces with a defined topological structure which can be uncovered with our methods the underlying pattern space seems to have locally but not globally the properties of a linear vector space we show how the knowledge about the topology of the pattern space can be exploited during pattern recognition our method may provide a new interesting approach for the analysis and synthesis of video sequences and complex movements

generalization to novel views universal class based andmodel based processing a major problem in object recognition is that a novel image of a given object can be different from all previously seen images images can vary considerably due to changes in viewing conditions such as viewing position and illumination in this paper we distinguish between three types of recognition schemes by the level at which generalization to novel images takes place universal class and model based the first is applicable equally to all objects the second to a class of objects and the third uses known properties of individual objects we derive theoretical limitations on each of the three generalization levels for the universal level previous results have shown that no invariance can be obtained here we show that this limitation holds even when the assumptions made on the objects and the recognition functions are relaxed we also extend the results to changes of illumination direction for the class level previous studies presented specific examples of classes of objects for which functions invariant to viewpoint exist here we distinguish between classes that admit such invariance and classes that do not we demonstrate that there is a tradeoff between the set of objects that can be discriminated by a given recognition function and the set of images from which the recognition function can recognize these objects furthermore we demonstrate that although functions that are invariant to illumination direction do not exist at the universal level when the objects are restricted to belong to a given class an invariant function to illumination direction can be defined a general conclusion of this study is that class based processing that has not been used extensively in the past is often advantageous for dealing with variations due to viewpoint and illuminant changes

a novel rotation scale invariant template matching algorithm using weighted adaptive lifting scheme transform this paper presents a novel algorithm for detecting user selected objects in given test images based on a new adaptive lifting scheme transform given an object as a template we first select a set of coefficients as object features in the wavelet transform domain and then build an adaptive transform based on the selected features the goal of the new adaptive transform is to vanish the selected features in the transform domain after applying both non adaptive and adaptive transforms to a given test image the corresponding transform domain coefficients are compared for detecting the object of interest in addition the proposed detection algorithm is combined with the proper log polar mapping model in the parametric template space to attain rotation scale invariance property finally we have verified the properties of our proposed algorithm with experimental results

matching 3 d models to 2 d images we consider the problem of analytically characterizing the set of all 2 d images that a group of 3 d features may produce and demonstrate that this is a useful thing to do our results apply for simple point features and point features with associated orientation vectors when we model projection as a 3 d to 2 d affine transformation we show how to represent the set of images that a group of 3 d points produces with two lines 1 d subspaces one in each of two orthogonal high dimensional spaces where a single image group corresponds to one point in each space the images of groups of oriented point features can be represented by a 2 d hyperbolic surface in a single high dimensional space the problem of matching an image to models is essentially reduced to the problem of matching a point to simple geometric structures moreover we show that these are the simplest and lowest dimensional representations possible for these cases we demonstrate the value of this way of approaching matching by applying our results to a variety of vision problems in particular we use this result to build a space efficient indexing system that performs 3 d to 2 d matching by table lookup this system is analytically built and accessed accounts for the effects of sensing error and is tested on real images we also derive new results concerning the existence of invariants and non accidental properties in this domain finally we show that oriented points present unexpected difficulties indexing requires fundamentally more space with oriented than with simple points we must use more images in a motion sequence to determine the affine structure of oriented points and the linear combinations result does not hold for oriented points

object recognition by combining paraperspective images this paper provides a study on object recognition under paraperspective projection discussed is the problem of determining whether or not a given image was obtained from a 3 d object to be recognized first it is clarified that paraperspective projection is the first order approximation of perspective projection then it is shown that if we represent an object as a set of its feature points and the object undergoes a rigid transformation or an affine transformation any paraperspective image can be expressed as a linear combination of several appropriate paraperspective images we need at least three images for rigid transformations whereas we need at least two images for affine transformations particularly in the case of a rigid transformation the coefficients of the combination have to satisfy two conditions orthogonality and norm equality a simple algorithm to solve the above problem based on these properties is presented a linear single shot algorithm some experimental results with synthetic images and real images are also given

palindromic companion forms for matrix polynomials of odd degree the standard way to solve polynomial eigenvalue problems p x 0 is to convert the matrix polynomial p into a matrix pencil that preserves its spectral information a process known as linearization when p is palindromic the eigenvalues elementary divisors and minimal indices of p have certain symmetries that can be lost when using the classical first and second frobenius companion linearizations for numerical computations since these linearizations do not preserve the palindromic structure recently new families of pencils have been introduced with the goal of finding linearizations that retain whatever structure the original p might possess with particular attention to the preservation of palindromic structure however no general construction of palindromic linearizations valid for all palindromic polynomials has as yet been achieved in this paper we present a family of linearizations for odd degree polynomials p which are palindromic whenever p is and which are valid for all palindromic polynomials of odd degree we illustrate our construction with several examples in addition we establish a simple way to recover the minimal indices of the polynomial from those of the linearizations in the new family

a curve fitting problem and its application in modeling objects in monocular image sequences presents a solution to a particular curve surface fitting problem and demonstrate its application in modeling objects from monocular image sequences the curve fitting algorithm is based on a modified nonparametric regression method which forms the core contribution of this work this method is far more effective compared to standard estimation techniques such as the maximum likelihood estimation method and can take into account the discontinuities present in the curve next the theoretical results of this 1d curve estimation technique ate extended significantly for an object modeling problem the input to the algorithm is a monocular image sequence of an object undergoing rigid motion by using the affine camera projection geometry and a given choice of an image frame pair in the sequence we adopt the kvd koenderink and van doorn 1991 model to express the depth at each point on the object as a function of the unknown out of plane rotation and some measurable quantities computed directly from the optical flow this is repeated for multiple image pairs keeping one fixed image frame which we formally call the base image and choosing another frame from the sequence the depth map is next estimated from these equations using the modified nonparametric regression analysis we conducted experiments on various image sequences to verify the effectiveness of the technique the results obtained using our curve fitting technique can be refined further by hierarchical techniques as well as by nonlinear optimization techniques in structure from motion

paraperspective affine it is shown that the set of all paraperspective images with arbitrary reference point and the set of affine images of a 3 d object are identical consequently all uncalibrated paraperspective images of an object can be constructed from a 3 d model of the object by applying an affine transformation to the model and every affine image of the object represents some uncalibrated paraperspective image of the object it follows that the paraperspective images of an object can be expressed as linear combinations of any two non degenerate images of the object when the image position of the reference point is given the parameters of the affine transformation and likewise the coefficients of the linear combinations satisfy two quadratic constraints conversely when the values of parameters are given the image position of the reference point is determined by solving a bi quadratic equation

integration of local and global geometrical cues for 3d face recognition we present a unified feature representation of 2 5d pointclouds and apply it to face recognition the representation integrates local and global geometrical cues in a single compact representation which makes matching a probe to a large database computationally efficient the global cues provide geometrical coherence for the local cues resulting in better descriptiveness of the unified representation multiple rank 0 tensors scalar features are computed at each point from its local neighborhood and from the global structure of the 2 5d pointcloud forming multiple rank 0 tensor fields the pointcloud is then represented by the multiple rank 0 tensor fields which are invariant to rigid transformations each local tensor field is integrated with every global field in a 2d histogram which is indexed by a local field in one dimension and a global field in the other dimension finally pca coefficients of the 2d histograms are concatenated into a single feature vector the representation was tested on frgc v2 0 data set and achieved 93 78 identification and 95 37 verification rate at 0 1 far

on deformable models for visual pattern recognition this paper reviews model based methods for non rigid shape recognition these methods model match and classify non rigid shapes which are generally problematic for conventational algorithms using rigid models issues including model representation optimization criteria formulation model matching and classification are examined in detail with the objective to provide interested researchers a roadmap for exploring the field this paper emphasizes on 2d deformable models their potential applications and future research directions particularly on deformable pattern classification are discussed

multifractal cross correlation spectra analysis on chinese stock markets abstract in this paper the long range cross correlation of chinese stock indices is systematically studied the multifractal detrended cross correlation analysis mf dxa appears to be one of the most effective methods in detecting long range cross correlation of two non stationary variables the legendre spectrum and the large deviations spectrum are extended to the cross correlation case so as to present multifractal structure of stock return and volatility series it is characterized of the multifractality in chinese stock markets partly due to clusters of local detrended covariance with large and small magnitudes

scale invariant image recognition based on higher order autocorrelation features we propose a framework and a complete implementation of a translation and scale invariant image recognition system for natural indoor scenes the system employs higher order autocorrelation features of scale space data which permit linear classification an optimal linear classification method is presented which is able to cope with a large number of classes represented by many as well as very few samples in the course of the analysis of our system we examine which numerical methods for feature transformation and classification show sufficient stability to fulfill these demands the implementation has been extensively tested we present the results of our own application and several classification benchmarks

least squares matching problems abstract a large class of matching problems can be thought of in the following way one is given a group g which acts on a riemannian manifold and one is given two points x 1 and x 2 in the manifold the problem is to find g g such that d g x 1 x 2 is as small as possible if the group has finite cardinality this is a combinatorial optimization problem whereas if g is a lie group one can use the calculus to obtain necessary conditions our main results establish circumstances under which one can relax the combinatorial problem to an appropriate lie group problem and then solve it by the method of steepest descent an interesting class of applications involves embedding the permutation group on n letters in the orthogonal group o n with the latter acting on r n n 1 2 via a symmetricized tensor product the convergence properties of the descent equation which arises in this way are analyzed in some detail

uncertainty propagation in model based recognition robust recognition systems require a careful understanding of the effects of error in sensed features in model based recognition matches between model features and sensed image features typically are used to compute a model pose and then project the unmatched model features into the image the error in the image features results in uncertainty in the projected model features we first show how error propagates when poses are based on three pairs of 3d model and 2d image points in particular we show how to simply and efficiently compute the distributed region in the image where an unmatched model point might appear for both gaussian and bounded error in the detection of image points and for both scaled orthographic and perspective projection models next we provide geometric and experimental analyses to indicate when this linear approximation will succeed and when it will fail then based on the linear approximation we show how we can utilize linear programming to compute bounded propagated error regions for any number of initial matches finally we use these results to extend from two dimensional to three dimensional objects robust implementations of alignment interpretation tree search and transformation clustering

on photometric issues in 3d visual recognition from asingle 2d image we describe the problem of recognition under changing illumination conditions and changing viewing positions from a computational and human vision perspective on the computational side we focus on the mathematical problems of creating an equivalence class for images of the same 3d object undergoing certain groups of transformations mostly those due to changing illumination and briefly discuss those due to changing viewing positions the computational treatment culminates in proposing a simple scheme for recognizing via alignment an image of a familiar object taken from a novel viewing position and a novel illumination condition on the human vision aspect the paper is motivated by empirical evidence inspired by mooney images of faces that suggest a relatively high level of visual processing is involved in compensating for photometric sources of variability and furthermore that certain limitations on the admissible representations of image information may exist the psychophysical observations and the computational results that follow agree in several important respects such as the same apparent limitations on image representations

recognition by linear combinations of models an approach to visual object recognition in which a 3d object is represented by the linear combination of 2d images of the object is proposed it is shown that for objects with sharp edges as well as with smooth bounding contours the set of possible images of a given object is embedded in a linear space spanned by a small number of views for objects with sharp edges the linear combination representation is exact for objects with smooth boundaries it is an approximation that often holds over a wide range of viewing angles rigid transformations with or without scaling can be distinguished from more general linear transformations of the object by testing certain constraints placed on the coefficients of the linear combinations three alternative methods of determining the transformation that matches a model to a given image are proposed

representation and detection of deformable shapes we describe some techniques that can be used to represent and detect deformable shapes in images the main difficulty with deformable template models is the very large or infinite number of possible nonrigid transformations of the templates this makes the problem of finding an optimal match of a deformable template to an image incredibly hard using a new representation for deformable shapes we show how to efficiently find a global optimal solution to the nonrigid matching problem the representation is based on the description of objects using triangulated polygons our matching algorithm can minimize a large class of energy functions making it applicable to a wide range of problems we present experimental results of detecting shapes in medical images and images of natural scenes we also consider the problem of learning a nonrigid shape model for a class of objects from examples we show how to learn good models while constraining them to be in the form required by the matching algorithm

image registration and object recognition using affine invariants and convex hulls this paper is concerned with the problem of feature point registration and scene recognition from images under weak perspective transformations which are well approximated by affine transformations and under possible occlusion and or appearance of new objects it presents a set of local absolute affine invariants derived from the convex hull of scattered feature points e g fiducial or marking points corner points inflection points etc extracted from the image the affine invariants are constructed from the areas of the triangles formed by connecting three vertices among a set of four consecutive vertices quadruplets of the convex hull and hence do make direct use of the area invariance property associated with the affine transformation because they are locally constructed they are very well suited to handle the occlusion and or appearance of new objects these invariants are used to establish the correspondences between the convex hull vertices of a test image with a reference image in order to undo the affine transformation between them a point matching approach for recognition follows this the time complexity for registering l feature points on the test image with n feature points of the reference image is of order o n spl times l the method has been tested on real indoor and outdoor images and performs well

visual recognition and categorization on the basis of similarities to multiple class prototypes one of the difficulties of object recognition stems from the need to overcome the variability in object appearance caused by pose and other factors such as illumination the influence of these factors can be countered by learning to interpolate between stored views of the target object taken under representative combinations of viewing conditions difficulties of another kind arise in daily life situations that require categorization rather than recognition of objects although categorization cannot rely on interpolation between stored examples we show that knowledge of several representative members or prototypes of each of the categories of interest can provide the necessary computational substrate for the categorization of new instances we describe a system that represents input shapes by their similarities to several prototypical objects and show that it can recognize new views of the familiar objects discriminate among views of previously unseen shapes and attribute the latter to familiar categories

algebraic functions for recognition in the general case a trilinear relationship between three perspective views is shown to exist the trilinearity result is shown to be of much practical use in visual recognition by alignment yielding a direct reprojection method that cuts through the computations of camera transformation scene structure and epipolar geometry moreover the direct method is linear and sets a new lower theoretical bound on the minimal number of points that are required for a linear solution for the task of reprojection the proof of the central result may be of further interest as it demonstrates certain regularities across homographics of the plane and introduces new view invariants experiments on simulated and real image data were conducted including a comparative analysis with epipolar intersection and the linear combination methods with results indicating a greater degree of robustness in practice and a higher level of performance in reprojection tasks

gabor wavelet representation for 3 d object recognition this paper presents a model based object recognition approach that uses a gabor wavelet representation the key idea is to use magnitude phase and frequency measures of the gabor wavelet representation in an innovative flexible matching approach that can provide robust recognition the gabor grid a topology preserving map efficiently encodes both signal energy and structural information of an object in a sparse multiresolution representation the gabor grid subsamples the gabor wavelet decomposition of an object model and is deformed to allow the indexed object model match with similar representation obtained using image data flexible matching between the model and the image minimizes a cost function based on local similarity and geometric distortion of the gabor grid grid erosion and repairing is performed whenever a collapsed grid due to object occlusion is detected the results on infrared imagery are presented where objects undergo rotation translation scale occlusion and aspect variations under changing environmental conditions

coarse view synthesis using shape from shading this paper investigates the use of shape from shading for coarse view synthesis the aim of our study is to determine whether needle maps delivered by a new shape from shading sfs algorithm can be used as a compact object representation for the purposes of efficiently generating appearance manifolds specifically we aim to show that the needle maps can be used to generate novel object views under changing light source and viewer directions to this end we conduct two sets of experiments firstly we use the recovered needle maps to re illuminate objects under varying lighting directions here we show that a single input image can be used to construct relatively faithful re illuminations under radical illumination changes secondly we investigate how the needle map can be used to generate new object poses here we show that needle maps can be used for both view interpolation and view extrapolation

3 d pose from 3 points using weak perspective this correspondence discusses computing the pose of a model from three matching point pairs under weak perspective projection a new approach to the problem that is motivated geometrically is described like previous methods the method here involves solving a biquadratic equation but here the biquadratic s solutions comprised of an actual and a false solution are interpreted graphically the final equations take a new form which leads to a simple expression for the image position of any unmatched model point

continuous generalized procrustes analysis two dimensional shape models have been successfully applied to solve many problems in computer vision such as object tracking recognition and segmentation typically 2d shape models are learned from a discrete set of image landmarks corresponding to projection of 3d points of an object after applying generalized procustes analysis gpa to remove 2d rigid transformations however the standard gpa process suffers from three main limitations firstly the 2d training samples do not necessarily cover a uniform sampling of all the 3d transformations of an object this can bias the estimate of the shape model secondly it can be computationally expensive to learn the shape model by sampling 3d transformations thirdly standard gpa methods use only one reference shape which can might be insufficient to capture large structural variability of some objects to address these drawbacks this paper proposes continuous generalized procrustes analysis cgpa cgpa uses a continuous formulation that avoids the need to generate 2d projections from all the rigid 3d transformations it builds an efficient in space and time non biased 2d shape model from a set of 3d model of objects a major challenge in cgpa is the need to integrate over the space of 3d rotations especially when the rotations are parameterized with euler angles to address this problem we introduce the use of the haar measure finally we extended cgpa to incorporate several reference shapes experimental results on synthetic and real experiments show the benefits of cgpa over gpa highlightscontinuous formulation of the generalized procrustes analysis cgpa avoids the need to generate 2d projections from all 3d rigid transformations cgpa builds an efficient non biased 2d shape model from 3d objects cgpa uses the haar measure to integrate over the space of 3d rotations experimental results building 2d shape models for different public datasets

face recognition the problem of compensating for changes in illumination direction a face recognition system must recognize a face from a novel image despite the variations between images of the same face a common approach to overcoming image variations because of changes in the illumination conditions is to use image representations that are relatively insensitive to these variations examples of such representations are edge maps image intensity derivatives and images convolved with 2d gabor like filters here we present an empirical study that evaluates the sensitivity of these representations to changes in illumination as well as viewpoint and facial expression our findings indicated that none of the representations considered is sufficient by itself to overcome image variations because of a change in the direction of illumination similar results were obtained for changes due to viewpoint and expression image representations that emphasized the horizontal features were found to be less sensitive to changes in the direction of illumination however systems based only on such representations failed to recognize up to 20 percent of the faces in our database humans performed considerably better under the same conditions we discuss possible reasons for this superiority and alternative methods for overcoming illumination effects in recognition

learning to track the visual motion of contours abstract a development of a method for tracking visual contours is described given an untrained tracker a training motion of an object can be observed over some extended time and stored as an image sequence the image sequence is used to learn parameters in a stochastic differential equation model these are used in turn to build a tracker whose predictor imitates the motion in the training set tests show that the resulting trackers can be markedly tuned to desired curve shapes and classes of motions

fourier domain representation of planar curves for recognition in multiple views recognition of planar shapes is an important problem in computer vision and pattern recognition the same planar object contour imaged from different cameras or from different viewpoints looks different and their recognition is non trivial traditional shape recognition deals with views of the shapes that differ only by simple rotations translations and scaling however shapes suffer more serious deformation between two general views and hence recognition approaches designed to handle translations rotations and or scaling would prove to be insufficient many algebraic relations between matching primitives in multiple views have been identified recently in this paper we explore how shape properties and multiview relations can be combined to recognize planar shapes across multiple views we propose novel recognition constraints that a planar shape boundary must satisfy in multiple views the constraints are on the rank of a fourier domain measurement matrix computed from the points on the shape boundary our method can additionally compute the correspondence between the curve points after a match is established we demonstrate the applications of these constraints experimentally on a number of synthetic and real images

similarity connectionism and the problem of representation in vision a representational scheme under which the ranking between represented similarities is isomorphic to the ranking between the corresponding shape similarities can support perfectly correct shape classification because it preserves the clustering of shapes according to the natural kinds prevailing in the external world this article discusses the computational requirements of representation that preserves similarity ranks and points out the relative straightforwardness of its connectionist implementation

polynomial time geometric matching for object recognition this paper considers the task of recognition and position determination by computer of a 2d or 3d object where the input is a single 2d brightness image and a model of the object is known a priori the primary contribution of this paper is a novel formulation and methods for local geometric feature matching this formulation is based on analyzing geometric constraints on transformations of the model features which geometrically align it with a substantial subset of image features specifically the formulation and algorithms for geometric feature matching presented here provide a guaranteed method for finding all feasible interpretations of the data in terms of the model this method is robust to measurement uncertainty in the data features and to the presence of spurious scene features and its time and space requirements are only polynomial in the size of the feature sets this formulation provides insight into the fundamental nature of the matching problem and the algorithms commonly used in computer vision for solving it

on the geometry of visual correspondence image displacement fields optical flow fields stereo disparity fields normal flow fields due to rigid motion possess a global geometric structure which is independent of the scene in view motion vectors of certain lengths and directions are constrained to lie on the imaging surface at particular loci whose location and form depends solely on the 3d motion parameters if optical flow fields or stereo disparity fields are considered then equal vectors are shown to lie on conic sections similarly for normal motion fields equal vectors lie within regions whose boundaries also constitute conics by studying various properties of these curves and regions and their relationships a characterization of the structure of rigid motion fields is given the goal of this paper is to introduce a concept underlying the global structure of image displacement fields this concept gives rise to various constraints that could form the basis of algorithms for the recovery of visual information from multiple views