waashk/dblp huggingface dataset:
optic flow calculation using robust statistics. a method for calculating optic flow, using robust statistics, is developed. the method generally out-performs all competing methods in terms of accuracy. one of the key features in the success of this method, is that we use least median of squares, which is known to be robust to outliers. the computational cost is kept very low by using an approximate solution to the least median of squares only in a first stage that detects outliers. the essential ingredients of our method should be applicable in a wide range of other computer vision problems.
limits on super-resolution and how to break them. nearly all super-resolution algorithms are based on the fundamental constraints that the super-resolution image should generate the low resolution input images when appropriately warped and down-sampled to model the image formation process. (these reconstruction constraints are normally combined with some form of smoothness prior to regularize their solution.) in the first part of this paper, we derive a sequence of analytical results which show that the reconstruction constraints provide less and less useful information as the magnification factor increases. we also validate these results empirically and show that, for large enough magnification factors, any smoothness prior leads to overly smooth results with very little high-frequency content (however, many low resolution input images are used). in the second part of this paper, we propose a super-resolution algorithm that uses a different kind of constraint, in addition to the reconstruction constraints. the algorithm attempts to recognize local features in the low-resolution images and then enhances their resolution in an appropriate manner. we call such a super-resolution algorithm a hallucination or recogstruction algorithm. we tried our hallucination algorithm on two different data sets, frontal images of faces and printed roman text. we obtained significantly better results than existing reconstruction-based algorithms, both qualitatively and in terms of rms pixel error.
pattern rejection. the efficiency of pattern recognition is particularly crucial in two scenarios; whenever there are a large number of classes to discriminate, and, whenever recognition must be performed a large number of times. we propose a single technique, namely, pattern rejection, that greatly enhances efficiency in both cases. a rejector is a generalization of a classifier, that quickly eliminates a large fraction of the candidate classes or inputs. this allows a recognition algorithm to dedicate its efforts to a much smaller number of possibilities. importantly, a collection of rejectors may be combined to form a composite rejector, which is shown to be far more effective than any of its individual components. a simple algorithm is proposed for the construction of each of the component rejectors. its generality is established through close relationships with the karhunen-loéve expansion and fisher's discriminant analysis. composite rejectors were constructed for two representative applications, namely, appearance matching based object recognition and local feature detection. the results demonstrate substantial efficiency improvements over existing approaches, most notably fisher's discriminant analysis.
3d reconstruction of the human jaw from a sequence of images. a novel approach is proposed to obtain a record of the patient's occlusion using computer vision. data acquisition is obtained using intra-oral video cameras. the technique utilizes shape from shading to extract 3d information from 2d views of the jaw, and a novel technique for 3d data registration using genetic algorithms. the resulting 3d model can be used for diagnosis, treatment planning, and implant purposes. the overall purpose of this research is to develop a model-based vision system for orthodontics to replace traditional approaches. this system will be flexible, accurate, and will reduce the cost of orthodontic treatments.
modelling of single mode distributions of colour data using directional statistics. three different statistical models of colour data for use in segmentation or tracking algorithms are proposed. results of a performance comparison of a tracking algorithm, applied to two separate applications, using each of the three different types of underlying model of the data are presented. from these a comparison of the performance of the statistical colour models themselves is obtained.
recursive estimation of motion and planar structure. abstract a specialized formulation of azarbayejani and pentland''s framework for recursive recovery of motion, structure and focal length from feature correspondences tracked through an image sequence is presented. the specialized formulation addresses the case where all tracked points lie on a plane. this planarity constraint reduces the dimension of the original state vector, and consequently the number of feature points needed to estimate the state. experiments with synthetic data and real imagery illustrate the system performance. the experiments confirm that the specialized formulation provides improved accuracy, stability to observation noise, and rate of convergence in estimation for the case where the tracked points lie on a plane.
extracting salient curves from images: an analysis of the saliency network. the saliency network proposed by shashua and ullman is a well-known approach to the problem of extracting salient curves from images while performing gap completion. this paper analyzes the saliency network. the saliency network is attractive for several reasons. first, the network generally prefers long and smooth curves over short or wiggly ones. while computing saliencies, the network also fills in gaps with smooth completions and tolerates noise. finally, the network is locally connected, and its size is proportional to the size of the image. nevertheless, our analysis reveals certain weaknesses with the method. in particular, we show cases in which the most salient element does not lie on the perceptually most salient curve. furthermore, in some cases the saliency measure changes its preferences when curves are scaled uniformly. also, we show that for certain fragmented curves the measure prefers large gaps over a few small gaps of the same total size. in addition, we analyze the time complexity required by the method. we show that the number of steps required for convergence in serial implementations is quadratic in the size of the network, and in parallel implementations is linear in the size of the network. we discuss problems due to coarse sampling of the range of possible orientations. we show that with proper sampling the complexity of the network becomes at least cubic in the size of the network. finally, we consider the possibility of using the saliency network for grouping. we show that the saliency network recovers the most salient curve efficiently, but it has problems with identifying any salient curve other than the most salient one.
verifying model-based alignments in the presence of uncertainty. this paper introduces a unified approach to the problem of verifying alignment hypotheses in the presence of substantial amounts of uncertainty in the predicted locations of projected model features. our approach is independent of whether the uncertainty is distributed or bounded, and, moreover, incorporates information about the domain in a formally correct manner. information which can be incorporated includes the error model, the distribution of background features, and the positions of the data features near each predicted model feature. experiments are described that demonstrate the improvement over previously used methods. furthermore, our method is efficient in that the number of operations is on the order of the number of image features that lie nearby the predicted model features.
model-based approach to accurate and consistent 3--d modeling of drainage and surrounding terrain. we propose an automated approach to modeling drainage channels---and, more generally, linear features that lie on the terrain---from multiple images, which results not only in high-resolution, accurate and consistent models of the features, but also of the surrounding terrain.in our specific case, we have chosen to exploit the fact that rivers flow downhill and lie at the bottom of local depressions in the terrain, valley floors tend to be ``u'' shaped, and the drainage pattern appears as a network of linear features that can be visually detected in single gray-level images.different approaches have explored individual facets of this problem. ours unifies these elements in a common framework. we accurately model terrain and features as 3--dimensional objects from several information sources that may be in error and inconsistent with one another. this approach allows us to generate models that are faithful to sensor data, internally consistent and consistent with physical constraints. the acm portal is published by the association for computing machinery. copyright © 2010 acm, inc. terms of usage privacy policy code of ethics contact us useful downloads: adobe acrobat quicktime windows media player real player
photometric computation of the sign of gaussian curvature using a curve-orientation invariant. we compute the sign of gaussian curvature using a purely geometric definition. consider a point p on a smooth surface s and a closed curve g on s which encloses p. the image of g on the unit normal gaussian sphere is a new curve b. the gaussian curvature at p is defined as the ratio of the area enclosed by g over the area enclosed by b as g contracts to p. the sign of gaussian curvature at p is determined by the relative orientations of the closed curves g and b. we directly compute the relative orientation of two such curves from intensity data. we employ three unknown illumination conditions to create a photometric scatter plot. this plot is in one-to-one correspondence with the subset of the unit gaussian sphere containing the mutually illuminated surface normals. this permits direct computation of the sign of gaussian curvature without the recovery of surface normals. our method is albedo invariant. we assume diffuse reflectance, but the nature of the diffuse reflectance can be general and unknown. simulations, as well as empirical results, demonstrate the accuracy of our technique.
real-time extraction of connected components in 3-d sonar range images. this paper describes an efficient algorithm for the segmentation of echo clusters within a dynamic 3-d sonar image. the sensor centered image is an echo management framework grouping sonar returns into spherical cells, and allowing real-time organisation of 3-d range data using inexpensive equipment. each cell acts as a spatial key to the features related to this location. the spherical representation is effectively exploited for segmentation using an approach motivated from connected components analysis in binary video images. a fast algorithm (linear in time complexity) based on cell connectivity between sonar beams is presented, including methods for coping with sparse data.
a curvature based descriptor invariant to pose and albedo derived from photometric data. gaussian curvature is an invariant local descriptor of smooth surfaces. we present an object signature which is a condensed representation of the distribution of gaussian curvature information at visible object points. an invariant related to gaussian curvature at a point is derived from the covariance matrix of the photometric values in a neighborhood about that point. in addition, we introduce an albedo-normalization method that is capable of cancelling albedo on lambertian surfaces. we use three illumination conditions, two of which are unknown. the three-tuple of intensity values at a point is related via a one-to-one mapping to the surface normal at that point. the determinant of the covariance matrix of the local three-tuples is invariant to albedo, rotation and translation. the collection of determinants over mutually illuminated object points is combined into a signature distribution which is albedo, rotation, translation, and scale invariant. an object recognition methodology using these signatures is proposed.
stereo-motion that complements stereo and motion analyses. recovering three-dimensional (3d) information of a scene from its images is a fundamental problem in computer vision. there exists two major multi-ocular cues for it, namely the motion cue and the stereo cue. this paper presents a new approach of integrating the two cues when two cameras that move through the scene while taking pictures repeatedly are available. the approach is based on the singular value decomposition (svd) technique, with which the 3d structure of the scene, the image projection parameters, the motion parameters, and the stereo geometry are all separated. the approach offers the advantages of both cues: simple correspondence as well as accurate reconstruction. it can also work with relatively short image sequences.
finding corresponding points based on bayesian triangulation. in this paper, we consider the problems of finding corresponding points from multiple perspective projection images (the correspondence problem), and estimating the 3-d point from which these points have arisen (the triangulation problem). we pose the triangulation problem as that of finding the bayesian maximum a posteriori estimate of the 3-d point, given its projections in n images, assuming a gaussian error model for the image point co-ordinates and the camera parameters. we solve this by an iterative steepest descent method. we then consider the correspondence problem as a statistical hypothesis verification problem. given a set of 2-d points, under the hypothesis that the points are in correspondence, the map estimate of the 3-d point is computed. based on the map estimate, we derive a statistical test for verifying this hypothesis. to find sets of corresponding points when multiple points in each of n images are given, we propose a method that does the bayesian triangulation and hypothesis verification on each n-tuple of points, selecting those that pass the hypothesis test. we characterize the performance of the bayesian triangulation in terms of the average distance of the triangulated 3-d point from the true 3-d point, and of the point correspondence method in terms of its misdetection and false alarm rates.
shape indexing using approximate nearest-neighbour search in high-dimensional spaces. shape indexing is a way of making rapid associations between features detected in an image and object models that could have produced them. when model databases are large, the use of high-dimensional features is critical, due to the improved level of discrimination they can provide. unfortunately, finding the nearest neighbour to a query point rapidly becomes inefficient as the dimensionality of the feature space increases. past indexing methods have used hash tables for hypothesis recovery, but only in low-dimensional situations. in this paper, we show that a new variant of the k-d tree search algorithm makes indexing in higher-dimensional spaces practical. this best bin first, or bbf, search is an approximate algorithm which finds the nearest neighbour for a large fraction of the queries, and a very close neighbour in the remaining cases. the technique has been integrated into a fully developed recognition system, which is able to detect complex objects in real, cluttered scenes in just a few seconds.
what is the set of images of an object under all possible lighting conditions? the appearance of a particular object depends on both the viewpoint from which it is observed and the light sources by which it is illuminated. if the appearance of two objects is never identical for any pose or lighting conditions, then-in theory - the objects can always be distinguished or recognized. the question arises: what is the set of images of an object under all lighting conditions and pose? in this paper, we consider only the set of images of an object under variable illumination (including multiple, extended light sources and attached shadows). we prove that the set of n-pixel images of a convex object with a lambertian reflectance function, illuminated by an arbitrary number of point light sources at infinity, forms a convex polyhedral cone in ir/sup n/ and that the dimension of this illumination cone equals the number of distinct surface normals. furthermore, we show that the cone for a particular object can be constructed from three properly chosen images. finally, we prove that the set of n-pixel images of an object of any shape and with an arbitrary reflectance function, seen under all possible illumination conditions, still forms a convex cone in ir/sup n/. these results immediately suggest certain approaches to object recognition. throughout this paper, we offer results demonstrating the empirical validity of the illumination cone representation.
independent 3d motion detection based on depth elimination in normal flow fields. this paper considers a specific problem of visual perception of motion, namely the problem of visual detection of independent 3d motion. most of the existing techniques for solving this problem rely on restrictive assumptions about the environment, the observer's motion, or both. moreover, they are based on the computation of optical flow, which amounts to solving the ill-posed correspondence problem. in this work, independent motion detection is formulated as robust parameter estimation applied to the visual input acquired by a binocular, rigidly moving observer. depth and motion measurements are combined in a linear model. the parameters of this model are related to the parameters of self-motion (egomotion) and the parameters of the stereoscopic configuration of the observer. the robust estimation of this model leads to a segmentation of the scene based on 3d motion. the method avoids the correspondence problem by employing only normal flow fields. experimental results demonstrate the effectiveness of this method in detecting independent motion in scenes with large depth variations, without any constraints imposed on observer motion.
the bas-relief ambiguity. when an unknown object with lambertian reflectance is viewed orthographically, there is an implicit ambiguity in determining its 3-d structure: we show that the object&lsquo;s visible surface f(x, y) is indistinguishable from a &ldquo;generalized bas-relief&rdquo; transformation of the object&lsquo;s geometry, \bar f (x, y) &equals; &lambda;f(x, y) + &mu;x + &nu;y, and a corresponding transformation on the object&lsquo;s albedo. for each image of the object illuminated by an arbitrary number of distant light sources, there exists an identical image of the transformed object illuminated by similarly transformed light sources. this result holds both for the illuminated regions of the object as well as those in cast and attached shadows. furthermore, neither small motion of the object, nor of the viewer will resolve the ambiguity in determining the flattening (or scaling) &lambda; of the object&lsquo;s surface. implications of this ambiguity on structure recovery and shape representation are discussed.
interpreting and representing tabular documents. this paper describes a methodology to interpret the information from telephone company dsx assignment table drawings. horizontal lines are found using an efficient algorithm that works over the run-length encoded representation of the image. for vertical lines, the image is transposed using an efficient method we developed, and the algorithm for horizontal lines is applied again. using the information about the lines, the tabular structures are extracted by finding biconnected components on the graph formed by the lines and their intersections. a methodology has also been developed for the representation of end access to the entries inside the tables.
representation of objects in a volumetric frequency domain with application to face recognition. a novel method for representing 3-d objects that unifies viewer and model centered object representations is presented. a unified 3-d frequency-domain representation (called volumetric/iconic spectral signatures - v/iss) encapsulates both the spatial structure of the object and a continuum of its views in the same data structure. the frequency-domain image of an object viewed from any direction can be directly extracted employing an extension of the projection slice theorem, where each fourier-transformed view is a planar slice of the volumetric frequency representation. the v/iss representation can be employed for pose-invariant recognition of complex objects such as faces. the recognition and pose estimation is based on an efficient matching algorithm in a four dimensional fourier space. experimental examples of pose estimation and recognition of faces are also presented.
multilinear constraints in the infinitesimal-time cas. in this paper we study the infinitesimal-time case of the so called multilinear constraints that exist for each subsequence in a sequence of images. these constraints link the infinitesimal motion of the image points with the infinitesimal viewer motion. the analysis is done both for calibrated and uncalibrated cameras. two simplifications are also presented for the uncalibrated camera case. one simplification is made using affine reduction and kinetic depth. the second simplification is based upon a projective reduction with respect to the image of a planar patch.
resolving occlusion in augmented reality : a contour based approach without 3d reconstruction. we present a new approach for resolving occlusions in augmented reality. the main interest is that it does not require 3d reconstruction of the considered scene. our idea is to use a contour based approach and to label each contour point as being behind or in front of, depending on whether it is in front of or behind the virtual object. this labeling step only requires that the contours can be tracked from frame to frame. a proximity graph is then built in order to group the contours that belong to the same occluding object. finally, we use some kind of active contours to accurately recover the mask of the occluding object.
deformable multi template matching with application to portal images. the exact positioning of patients during radiotherapy is essentialfor high precision treatment. the registration of portal image sequences can help to control the patient position. the particular problem of such megavoltage x-ray imagery is its extremely low contrast, rendering accurate feature extraction a difficult task. to circumvent the step offeature extraction, the algorithm presented in this paper relies on anarea-bused matching of the image signal using deformable templates. this strategy contrasts with most state of the art registration algorithms for portal imagery. the paper includes the mathematical formalism of the least squares template matching method, as well as the framework for automated quality control, together yielding a fast, robust and very accurate image matching procedure. tests on i7 portal image series with more than 100 images in total have shown very satisfying results. artificially rotated and shifted images demonstrate the performance of the method with respect to a ground truth.
shadows and shading flow fields. many presume that parsing the shadows out of an image is a high-level task, because of the global nature of the shadow formation process. but shape-from-shading algorithms are low-level, in the sense that they seek solutions (surface normals or depth values) directly from image intensities. a dilemma arises: since shape-from-shading involves an illumination term, shadows must first be identified. we show that a structure intermediate between intensities and surfaces-the shading flow field-provides a solution to this dilemma. our analysis is based on the observation that the geometric information that can be derived from images supports different inferences than the photometric information, and our specific goal will be to articulate this geometric structure and to show how shading flow fields can be reliably computed.