waashk/dblp huggingface dataset:
critical motion sequences for monocular self-calibration and uncalibrated euclidean reconstruction. in this paper, sequences of camera motions that lead to inherent ambiguities in uncalibrated euclidean reconstruction or self-calibration are studied. our main contribution is a complete, detailed classification of these critical motion sequences (cms). the practically important classes are identified and their degrees of ambiguity are derived. we also discuss some practical issues, especially concerning the reduction of the ambiguity of a reconstruction.
runway obstacle detection by controlled spatiotemporal image flow disparity. this paper proposes a method for detecting obstacles on a runway by controlling their expected disparities. by approximating the runway by a planar surface, the initial model flow field (mff) corresponding to an obstacle-free runway is described by the data from on-board sensors (obs). the error variance of the initial mff is computed and used to estimate the mff. obstacles are detected by comparing the expected residual flow disparities with the residual flow field (rff) estimated after warping (or stabilizing) an image using the mff. expected temporal and spatial disparities are obtained from the use of the obs. this allows us to control the residual disparities by increasing the temporal baseline and/or by utilizing the spatial baseline if distant objects cannot be detected for a given temporal baseline. experimental results for two real flight image sequences are presented.
are textureless scenes recoverable? it is widely accepted that textureless surfaces cannot be recovered using passive sensing techniques. the problem is approached by viewing image formation as a fully three-dimensional mapping. it is shown that the lens encodes structural information of the scene within a compact three-dimensional space behind it. after analyzing the information content of this space and by using its properties we derive necessary and sufficient conditions for the recovery of textureless scenes. based on these conditions, a simple procedure for recovering textureless scenes is described. we experimentally demonstrate the recovery of three textureless surfaces, namely, a line, a plane, and a paraboloid. since textureless surfaces represent the worst case recovery scenario, all the results and the recovery procedure are naturally applicable to scenes with texture.
efficient image gradient-based object localisation and recognitio. this paper reports novel algorithms for the efficient localization and recognition of vehicles in traffic scenes, which eliminate the need for explicit symbolic feature extraction and matching. the algorithms make use of two a priori sources of knowledge about the scene and the objects: (i) the ground-plane constraint, and (ii) the fact that road vehicles are strongly rectilinear. the algorithms are demonstrated and tested using routine outdoor traffic images. success with a variety of vehicles demonstrates the efficiency and robustness of context-based computer vision in road traffic scenes. the limitations of the algorithms are also addressed in the paper.
shocks from images: propagation of orientation elements. the extraction of figure symmetry from image contours faces a number of fundamental difficulties: object symmetries are distorted due to (i) gaps in the bounding contour of a shape due to figure-ground blending, weak contrast edges, highlights, noise, etc.; (ii) an introduction of parts and occluders, and (iii) spurious edge elements due to surface markings, texture, etc. a framework for extracting such symmetries from real images is proposed based on the propagation of contour orientation information and the detection of four types of singularities (shocks) arising from the collision of propagating elements. in this paper, we show that an additional labeling of shocks based on whether the colliding wavefronts carry true orientation information (regular vs. rarefaction waves) allows a division of shocks into three sets: regular shocks are the partial shocks of partial contours as they remain invariant to the completion of the contour; semi-degenerate and degenerate shocks depict potential parts and gaps. finally, shocks altered due to spurious edges, occlusion, and gaps are recovered via a simulation of inter-penetrating waves generated at select shock groups which with the aid of the above shock labels leads to second and further generations of shocks.
a computational approach to steerable functions. we present a computational, group-theoretic approach to steerable functions. the approach is group-theoretic in that the treatment involves continuous transformation groups for which elementary lie group theory may be applied. the approach is computational in that the theory is constructive and leads directly to a procedural implementation. for functions that are steerable with $n$ finite number of basis functions under a $k$-parameter group, the procedure is efficient and is guaranteed to return the minimum number of basis functions. if the function is not steerable, a numerical implementation of the procedure could also be used to compute basis functions that approximately steer the function over a range of transformation parameters. examples of both applications are demonstrated.
non-rigid matching using demons. we present the concept of non-rigid matching based on demons, by reference to maxwell's demons. we contrast this concept with the more conventional viewpoint of attraction. we show that demons and attractive points are clearly distinct for large deformations, but also that they become similar for small displacements, encompassing techniques close to optical flow. we describe a general iterative matching method based on demons, and derive from it three different non-rigid matching algorithms, one using all the image intensities, one using only contours, and one for already segmented images. at last, we present results with synthesized and real deformations, with applications to computer vision and medical image processing.
comparison of approaches to egomotion computation. we evaluated six algorithms for computing egomotion from image velocities. we established benchmarks for quantifying bias and sensitivity to noise, and for quantifying the convergence properties of those algorithms that require numerical search. our simulation results reveal some interesting and surprising results. first, it is often written in the literature that the egomotion problem is difficult because translation (e.g., along the x-axis) and rotation (e.g., about the y-axis) produce similar image velocities. we found, to the contrary, that the bias and sensitivity of our six algorithms are totally invariant with respect to the axis of rotation. second, it is also believed by some that fixating helps to make the egomotion problem easier. we found, to the contrary, that fixating does not help when the noise is independent of the image velocities. fixation does help if the noise is proportional to speed, but this is only for the trivial reason that the speeds are slower under fixation. third, it is widely believed that increasing the field of view will yield better performance. we found, to the contrary, that this is not necessarily true.
boosting image retrieval. we present an approach for image retrieval using a very large number of highly selective features and efficient learning of queries. our approach is predicated on the assumption that each image is generated by a sparse set of visual &ldquo;causes&rdquo; and that images which are visually similar share causes. we propose a mechanism for computing a very large number of highly selective features which capture some aspects of this causal structure (in our implementation there are over 46,000 highly selective features). at query time a user selects a few example images, and the adaboost algorithm is used to learn a classification function which depends on a small number of the most appropriate features. this yields a highly efficient classification function. in addition we show that the adaboost framework provides a natural mechanism for the incorporation of relevance feedback. finally we show results on a wide variety of image queries.
an assessment of information criteria for motion model selection. rigid motion imposes constraints on the motion of image points between the two images. the matched points must conform to one of several possible constraints, such as that given by the fundamental matrix or image-image homography, and it is essential to know which model to fit to the data before recovery of structure, matching or segmentation can be performed successfully. this paper compares several model selection methods with a particular emphasis on providing a method that will work fully automatically on real imagery.
factorization methods for projective structure and motion. this paper describes a family of factorization-based algorithms that recover 3d projective structure and motion from multiple uncalibrated perspective images of 3d points and lines. they can be viewed as generalizations of the tomasi-kanade algorithm from affine to fully perspective cameras, and from points to lines. they make no restrictive assumptions about scene or camera geometry, and unlike most existing reconstruction methods they do not rely on `privileged' points or images. all of the available image data is used, and each feature in each image is treated uniformly. the key to projective factorization is the recovery of a consistent set of projective depths (scale factors) for the image points: this is done using fundamental matrices and epipoles estimated from the image data. we compare the performance of the new techniques with several existing ones, and also describe an approximate factorization method that gives similar results to svd-based factorization, but runs much more quickly for large problems.
autocalibration and the absolute quadric. the author describes a new method for camera autocalibration and scaled euclidean structure and motion, from three or more views taken by a moving camera with fixed but unknown intrinsic parameters. the motion constancy of these is used to rectify an initial projective reconstruction. euclidean scene structure is formulated in terms of the absolute quadric-the singular dual 3d quadric (4/spl times/4 rank 3 matrix) giving the euclidean dot-product between plane normals. this is equivalent to the traditional absolute conic but simpler to use. it encodes both affine and euclidean structure, and projects very simply to the dual absolute image conic which encodes camera calibration. requiring the projection to be constant gives a bilinear constraint between the absolute quadric and image conic, from which both can be recovered nonlinearly from m/spl ges/3 images, or quasi-linearly from m/spl ges/4. calibration and euclidean structure follow easily. the nonlinear method is stabler, faster, more accurate and more general than the quasi-linear one. it is based on a general constrained optimization technique-sequential quadratic programming-that may well be useful in other vision problems.
ego-motion estimation using optical flow fields observed from multiple cameras. in this paper, we consider a multi-camera vision system mounted on a moving object in a static three-dimensional environment. by using the motion flow fields seen by all of the cameras, an algorithm which does not need to solve the point-correspondence problem among the cameras is proposed to estimate the 3d ego-motion parameters of the moving object. our experiments have shown that using multiple optical flow fields obtained from different cameras can be very helpful for ego-motion estimation.
nonrigid motion analysis based on dynamic refinement of finite element models. in this paper, we propose new algorithms for accurate nonrigid motion tracking. given an initial model representing general knowledge of the object, a set of sparse correspondences, and incomplete or missing information about geometry or material properties, we can recover dense motion vectors using finite element models. the method is based on the iterative analysis of the differences between the actual and predicted behavior. large differences indicate that an object's properties are not captured properly by the model describing it. feedback from the images during the motion allows the refinement of the model by minimizing the error between the expected and true position of the object's points. these errors are due to flaws in the model parameter estimation such as geometry and material properties. unknown parameters are recovered using an iterative descent search for the best nonlinear finite element model that approximates nonrigid motion of the given object. during this search process, we not only estimate material properties, but also infer dense point correspondences from our initial set of sparse correspondences. thus, during tracking, the model is refined which, in turn, improves tracking quality. as a result, we obtain a more precise description of nonrigid motion. experimental results demonstrate the success of the proposed algorithm. the method was applied to man-made elastic materials and human skin to recover unknown elasticity, to complex 3d objects to find details of their geometry, and to a hand motion analysis application. our work demonstrates the possibility of accurate quantitative analysis of nonrigid motion in range image sequences with objects consisting of multiple materials and 3d volumes.
feature tracking from an image sequence using geometric invariants. in this paper two new feature tracking algorithms are proposed. in the first algorithm, a perspective camera model is used. making use of the projective invariant of barrett, and assuming the image feature points corresponding to 8 general points in space are tracked by a conventional method in the image sequence, the other feature points in the sequence can be tracked using a hough technique. correspondence between two reference images as required by the original barrett's invariant is not necessary. in the second algorithm, an affine camera model is assumed and the image feature points corresponding to 4 non-coplanar points in space are assumed tracked in the image sequence using a conventional method. these image points form the basis of affine coordinates in each image. after the correspondence of a fifth point is established between the first two images, the affine coordinates of all image points in the first images can be computed. as far as we know, this is the only algorithm which can transfer a point knowing only a single image. experiments showed that both algorithms gave highly accurate tracking results.
a framework for recognizing a facial image from a police sketch. we present a theory and practical computations for automatically matching a police artist sketch to a set of true photographs. we locate facial features in both the sketch as well as the set of photograph images. then, the sketch is photometrically standardized to facilitate comparison with a photo and then both the sketch and the photos are geometrically standardized. finally, for matching, eigenanalysis is employed. results using real police sketches and arrest photos are presented.
empirical bayesian em-based motion segmentation. a recent trend in motion-based segmentation has been to rely on statistical procedures derived from expectation-maximization (em) principles. em-based approaches have various attractives for segmentation, such as proceeding by taking non-greedy soft decisions with regards to the assignment of pixels to regions, or allowing the use of sophisticated priors capable of imposing spatial coherence on the segmentation. a practical difficulty with such priors is, however, the determination of appropriate values for their parameters. in this work, we exploit the fact that the em framework is itself suited for empirical bayesian data analysis to develop an algorithm that finds the estimates of the prior parameters which best explain the observed data. such an approach maintains the bayesian appeal of incorporating prior beliefs, but requires only a qualitative description of the prior, avoiding the requirement of a quantitative specification of its parameters. this eliminates the need for trial-and-error strategies for parameter determination and leads to better segmentations in less iterations.
a bootstrapping algorithm for learning linear models of object classes. flexible models of object classes, based on linear combinations of prototypical images, are capable of matching novel images of the same class and have been shown to be a powerful tool to solve several fundamental vision tasks such as recognition, synthesis and correspondence. the key problem in creating a specific flexible model is the computation of pixelwise correspondence between the prototypes, a task done until now in a semiautomatic way. in this paper we describe an algorithm that automatically bootstraps the correspondence between the prototypes. the algorithm - which can be used for 2d images as well as for 3d models - is shown to synthesize successfully a flexible model of frontal face images and a flexible model of handwritten digits.
structure and motion of curved 3d objects from monocular silhouettes. the silhouette of a smooth 3d object observed by a moving camera changes over time. past work has shown how surface geometry can be recovered using the deformation of the silhouette when the camera motion is known. this paper addresses the problem of estimating both the full euclidean surface structure and the camera motion from a dense set of silhouettes captured under orthographic or scaled orthographic projection. the approach relies on a viewpoint-invariant representation of curves swept by viewpoint-dependent features such as bitangents, inflections and contour points with parallel tangents. feature points, which form stereo frontier points between non-consecutive images, are matched using this representation. the camera's angular velocity is computed from constraints derived from this correspondence along with the image velocity of these features. from the angular velocity, the epipolar geometry is ascertained, and infinitesimal motion frontier points can be detected. in turn, the motion of these frontier points constrains the translation component of camera motion. finally, the surface is reconstructed using established techniques once the camera motion has been estimated.
blurring strategies for image segmentation using a multiscale linking model. multiscale approaches are an invaluable tool for image segmentation. a vast amount of research has been devoted to the construction of different multiscale representations of an image. in this paper we use the `hyperstack'--a multiscale linking model for image segmentation--for an in-depth comparison of four different scale space generators with respect to segmentation results. we consider the linear (gaussian) scale space both in the spatial and the fourier domain, the variable conductance diffusion according to the perona & malik equation, and the euclidean shortening flow. we have done experiments on mr images of the brain, for which a gold standard is available. the hyperstack proofs to be rather insensitive to the underlying scale space generator.
determining correspondences and rigid motion of 3-d point sets with missing data. this paper addresses the general 3-d rigid motion problem, where the point correspondences and the motion parameters between two sets of 3-d points are to be recovered. the existence of missing points in the two sets is the most difficult problem. we first show a mathematical symmetry in the solutions of rotation parameters and point correspondences. a closed-form solution based on the correlation matrix eigenstructure decomposition is proposed for correspondence recovery with no missing points. using a heuristic measure of point pair affinity derived from the eigenstructure, a weighted bipartite matching algorithm is developed to determine the correspondences in general cases where missing points occur. the use of the affinity heuristic also leads to a fast outlier removal algorithm, which can be run iteratively to refine the correspondence recovery. simulation results and experiments on real images are shown in both ideal and general cases.
illumination and geometry invariant recognition of texture in color images. we develop a method for recognizing color texture independent of rotation, scale, and illumination. color texture is modeled using spatial correlation functions defined within and between sensor bands. using a linear model for surface spectral reflectance with the same number of parameters as the number of sensor classes, we show that illumination and geometry changes in the scene correspond to a linear transformation of the correlation functions and a linear transformation of their coordinates. a several step algorithm which includes scale estimation and correlation moment computation is used to achieve the invariance. the key to the method is the new result that illumination and geometry changes in the scene correspond to a specific transformation of correlation function zernike moment matrices. these matrices can be estimated from a color image. this relationship is used to derive an efficient algorithm for recognition. the algorithm is substantiated using classification results on over 200 images of color textures obtained under various illumination conditions and geometric configurations.
extracting surface textures and microstructures from multiple aerial images. we present a surface texture and microstructure extraction system to provide added realism in visualization and virtual reality applications. the system uses multiple images, 3-d models and camera information, in addition to knowledge about man-made structures, to cope with problems such as perspective distortion, data deficiency, and corruption caused by shadows and occlusions. combined with the ascender site modeling system~\cite{collins_iuw} and scene rendering algorithms, the system is typically useful for urban site model refinement and visualization.
minimal operator set for passive depth from defocus. a fundamental problem in depth from defocus is the measurement of relative defocus between images. we propose a class of broadband operators that, when used together, provide invariance to scene texture and produce accurate and dense depth maps. since the operators are broadband, a small number of them are sufficient for depth estimation of scenes with complex textural properties. experiments are conducted on both synthetic and real scenes to evaluate the performance of the proposed operators. the depth detection gain error is less than 1%, irrespective of texture frequency. depth accuracy is found to be 0.5/spl sim/1.2% of the distance of the object from the imaging optics.
smoothness in layers: motion segmentation using nonparametric mixture estimation. grouping based on common motion, or ``common fate'' provides a powerful cue for segmenting image sequences. recently a number of algorithms have been developed that successfully perform motion segmentation by assuming that the motion of each group can be described by a low dimensional parametric model (e.g. affine). typically the assumption is that motion segments correspond to planar patches in 3d undergoing rigid motion. here we develop an alternative approach, where the motion of each group is described by a smooth dense flow field and the stability of the estimation is ensured by means of a prior distribution on the class of flow fields. we present a variant of the em algorithm that can segment image sequences by fitting multiple smooth flow fields to the spatiotemporal data. using the method of green's functions, we show how the estimation of a single smooth flow field can be performed in closed form, thus making the multiple model estimation computationally feasible. furthermore, the number of models is estimated automatically using similar methods to those used in the parametric approach. we illustrate the algorithm's performance on synthetic and real image sequences.