waashk/dblp huggingface dataset:
a unified mixture framework for motion segmentation: incorporating spatial coherence and estimating the number of models. describing a video sequence in terms of a small number of coherently moving segments is useful for tasks ranging from video compression to event perception. promising approach is to view the motion segmentation problem in a mixture estimation framework. however, existing formulations generally use only the motion data and thus fail to make use of static cues when segmenting the sequence. furthermore, the number of models is either specified in advance or estimated outside the mixture model framework. in this work we address both of these issues. we show how to add spatial constraints to the mixture formulations and present a variant of the em algorithm that makes use of both the form and the motion constraints. moreover this algorithm estimates the number of segments given knowledge about the level of model failure expected in the sequence. the algorithm's performance is illustrated on synthetic and real image sequences.
nonlinear shape restoration for document images. previous researches on nonlinear shape restoration are based on the assumptions that the original shapes and distortions of the images have known formulations under certain conditions. the main contribution of this research is the development of a new restoration algorithm, called multi-steps restoration. the algorithm is based on a liner interpolation theory that is able to detect and restore nonlinear shape distortions in any irregular quadrilateral-shaped patterns. the main idea of the algorithm is the use of two-dimensional spline functions in bicubic, biquadratic, and/or bilinear models to approximate the three-dimensional nonlinear distortion curves. the performance of the approach shown by experiment is promising.
using local 3d structure for segmentation of bone from computer tomography images. in this paper we focus on using local 3d structure for segmentation. a tensor descriptor is estimated for each neighbourhood, i.e. for each voxel in the data set. the tensors are created from a combination of the outputs form a set of 3d quadrature filters. the shape of the tensors describe locally the structure of the neighborhood in terms of how much it is like a plane, a line, and a sphere. we apply this to segmentation of bone from computer tomography data (ct). traditional methods are based purely on gray-level value discrimination and have difficulties in recovering thin bone structures due to so called partial voluming, a problem which is present in all such sampled data. we illuminate the partial voluming problem by showing that thresholding creates complicated artifacts even if the signal is densely enough sampled and can be perfectly reconstructed. the unwanted effects of thresholding can be reduced by a change of the signal basis. we show that by using additional local structure information can significantly reduce the degree of sampling artifacts. evaluation of the method on a clinical case is presented, the segmentation of a human skull from a ct volume. the method shows that many of the thin bone structures which disappear in a pure thresholding can be recovered.
attention control for robot vision. focus of attention mechanisms for robot vision are discussed. a new method for neglecting low level filter responses from already modelled structures is presented. the method is based on a filtering technique termed normalized convolution. in one experiment, the robot is continuously moving its arm in the scene while tracking other objects. it is shown how the arm can be made ``invisible'' so that only the moving object of interest is detected. this makes tracking of objects much simpler. in another experiment, the attention of the system is shifted between objects by simply cancelling the mask of the object to be attended to. with this strategy the low level processes do not need to know the difference between a new object entering the scene and a mask being cancelled, and thus a complex communication structure between high and low levels is avoided.
physically based fluid flow recovery from image sequences. this paper presents an approach to measuring fluid flow from image sequences. the approach centers around a motion recovery algorithm that is based on principles from fluid mechanics: the algorithm is constrained so that recovered flows observe conservation of mass as well as physically motivated boundary conditions. empirical results are presented from application of the algorithm to fluid flows captured via transmittance imagery (i.e., radiographs). in these experiments, fluids seeded with tracers were driven through simple physical systems. the significance of this work is twofold. first, from a theoretical point of view it is shown how information derived from the physical behavior of fluids can be used to motivate a flow recovery algorithm. second, from an applications point of view the developed algorithm can be used to augment the tools that are available for the measurement of fluid dynamics; other imaged flows that observe compatible constraints might benefit in a similar fashion.
hyper-patches for 3d model acquisition and tracking. automatic 3d model acquisition and 3d tracking of simple objects under motion using a single camera is often difficult due to the sparsity of information from which to establish the model. we have developed an automatic scheme that first computes a simple pointalistic euclidean model of the object and then enriches this model using hyper-patches. these hyper-patches contain information on both the orientation and intensity pattern variation of roughly planar patches on an object. this information allows both the spatial and intensity distortions of the projected patch to be modelled accurately under 3d object motion. we show that hyper-patches not only can be computed automatically during model acquisition from a monocular image sequence, but that they are also extremely appropriate for the task of visual tracking.
local parallel computation of stochastic completion fields. we describe a local parallel method for computing the stochastic completion field introduced in an earlier paper\cite{williams}. the stochastic completion field represents the likelihood that a completion joining two contour fragments passes through any given position and orientation in the image plane. it is based upon the assumption that the prior probability distribution of completion shape can be modeled as a random walk in a lattice of discrete positions and orientations. the local parallel method can be interpreted as a stable finite difference scheme for solving the underlying fokker-planck equation identified by mumford\cite{mumford}. the resulting algorithm is significantly faster than the previously employed method which relied on convolution with large-kernel filters computed by monte carlo simulation. the complexity of the new method is o(n3m) while that of the previous algorithm was o(n4m2) (for an n x n image with m discrete orientations). perhaps most significantly, the use of a local method allows us to model the probability distribution of completion shape using stochastic processes which are neither homogenous nor isotropic. for example, it is possible to modulate particle decay rate by a directional function of local image brightnesses (i.e., anisotropic decay). the effect is that illusory contours can be made to respect the local image brightness structure. finally, we note that the new method is more plausible as a neural model since 1) unlike the previous method, it can be computed in a sparse, locally connected network; and 2) the network dynamics are consistent with psychophysical measurements of the time course of illusory contour formation.
rational discrete generalized cylinders and their application to shape recovery in medical images. generalized cylinders (gcs) are a popular representational tool in computer vision. in medical imaging, the curved axis gc is particularly applicable to a number of elongated physical structures such as vasculature, bone and bronchi. in many of these instances, it is necessary to recover curved-axis gcs with arbitrary cross-sections. it is also vital that these structures, once recovered, can be analyzed and visualized with off-the-shelf algorithms and software packages. such tools are usually designed to operate on the domain of polynomial or rational surfaces. unfortunately most extant, suitably versatile gc representations do not admit rational parameterizations. we develop an entirely rational b-spline representation for generalized cylinders with curved axes and arbitrary cross-section functions. we demonstrate how our representation can be used as a deformable model by extracting a rational gc from pre-segmented spinal data using a discrete, dynamic surface fit.
temporal classification of natural gesture and application to video coding. a method for the temporal classification of natural gesture from video imagery is presented. the work is motivated by recent developments in the theory of natural gesture which have identified several key temporal aspects of gesture important to communication. in particular, gesticulation during conversation can be coarsely characterized as periods of bi-phasic or tri-phasic gesture separated by a rest state. we first present an automatic procedure for hypothesizing plausible rest state configurations of a speaker. second, we develop a state-based parsing algorithm used to both select among candidate rest states and to parse an incoming video stream into bi-phasic and tri-phasic gestures. finally, we demonstrate the use of the bi-phasic/tri-phasic labeling to select semantically significant static images for low bandwidth coding of video of story-telling speakers.
gauging relational consistency and correcting structural errors. the aim of this paper is to provide a comparative evaluation of a number of contrasting approaches to relational matching. unique to this study is the way in which we show how a diverse family of algorithms relate to one-another using a common bayesian framework. broadly speaking there are two main aspects to this study. firstly we focus on the issue of how relational inexactness may be quantified. we illustrate that several popular relational distance measures can be recovered as specific limiting cases of the same bayesian consistency measure. the second aspect of our comparison concerns the way in which structural inexactness is controlled. we investigate three different realisations of the matching process which draw on contrasting control models. the main conclusion of our study is that the active process of graph-editing outperforms the alternatives in terms of its ability to effectively control a large population of contaminating clutter.
a minimum-variance adaptive surface mesh. our main contribution in this paper is to describe a new class of the adaptive mesh. the mesh uses both split and merge operations to adapt itself to the structure of volumetric data-points. the adaptive behavior is controlled by the variance of the data-point positions about maximum-likelihood quadric patches. we show that the density of control points on the mesh is regulated by the curvature of the underlying surface. finally, we illustrate the effectiveness of the method on both real-world and simulated data-sets.
polarization phase-based method for material classification and object recognition in computer vision. a robust and accurate polarization phase-based technique for material classification is presented. the novelty of this technique is three-fold in (i) its theoretical development, (ii) its application, and, (iii) its experimental implementation. the concept of phase of polarization of a light wave is introduced to computer vision for discrimination between materials according to their intrinsic electrical conductivity, such as distinguishing conducting metals, and poorly-conducting dielectrics. previous work has used intensity, color and polarization component ratios. this new method is based on the physical principle that metals retard orthogonal components of light upon reflection while dielectrics do not. this method has significant complementary advantages with respect to existing techniques, is computationally efficient, and can be easily implemented with existing imaging technology. experiments for real circuit board inspection, non-conductive and conductive glass, and, outdoor object recognition have been performed to demonstrate its accuracy and potential capabilities.
error analysis of a real-time stereo syste. correlation-based real-time stereo systems have been proven to be effective in applications such as robot navigation, elevation map building etc. this paper provides an in-depth analysis of the major error sources for such a real-time stereo system in the context of cross-country navigation of an autonomous vehicle. three major types of errors: foreshortening error, misalignment error and systematic error, are identified. the combined disparity errors can easily exceed three-tenths of a pixel, which translates to significant range errors. upon understanding these error sources, we demonstrate different approaches to either correct them or model their magnitudes without excessive additional computations. by correcting those errors, we show that the precision of the stereo algorithm can be improved by 50%.
creating image-based vr using a self-calibrating fisheye lens. image-based virtual reality is emerging as a major alternative to the more traditional 3d-based vr. the main advantages of the image-based vr are its photo-quality realism and 3d illusion without any 3d information. unfortunately, creating content for image-based vr is usually a very tedious process. this paper proposes to use a non-perspective fisheye lens to capture the spherical panorama with very few images. unlike most of camera calibration in computer vision, self-calibration of the fisheye lens poses new questions regarding the parameterization of the distortion and wrap-around effects. because of its unique projection model and large field of view (near 180 degrees), most of the ambiguity problems in self-calibrating a traditional lens can be solved trivially. we demonstrate that with four fisheye lens images, we can seamlessly register them to create the spherical panorama, while self-calibrating its distortion and field of view.
determining a polyhedral shape using interreflections. we discuss the uniqueness of 3-d shape recovery of a polyhedron from a single shading image. first, we analytically show that multiple convex (and concave) shape solutions usually exist for a simple polyhedron if interreflections are not considered. then we propose a new approach to uniquely determine the concave shape solution using interreflections as a constraint. a numerical example, in which two convex shapes and two concave shapes exist for a trihedral corner, has been shown by horn. however, it is difficult to prove the uniqueness using constraint equations. we analytically show that multiple convex (and concave) shape solutions usually exist for a pyramid using a reflectance map, if interreflection distribution is not considered. however, if interreflection distribution is used as a constraint that limits the shape solution (for a concave polyhedron), the polyhedral shape can be uniquely determined. interreflections are used as a constraint to determine the shape solution in our approach.
gradient vector flow: a new external force for snakes. snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. problems associated with initialization and poor convergence to concave boundaries, however, have limited their utility. this paper develops a new external force for active contours, largely solving both problems. this external force, which we call gradient vector flow (gvf), is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. the resultant field has a large capture range and forces active contours into concave regions. examples on simulated images and one real image are presented.
temporal multi-scale models for flow and acceleration. a model for computing image flow in image sequences containing a very wide range of instantaneous flows is proposed. this model integrates the spatio-temporal image derivatives from multiple temporal scales to provide both reliable and accurate instantaneous flow estimates. the integration employs robust regression and automatic scale weighting in a generalized brightness constancy framework. in addition to instantaneous flow estimation the model supports recovery of dense estimates of image acceleration and can be readily combined with parameterized flow and acceleration models. a demonstration of performance on image sequences of typical human actions taken with a high frame-rate camera is given.
identification of salient contours in cluttered images. we present a model of contour extraction in which the perceptual salience of contours arises from long-range interactions between orientation-selective filters. it has been previously shown that salient contours may be extracted from noisy images by using a number heuristic features. our algorithm is based on cortical mechanisms, and simulations show close agreement with results from recent anatomical, physiological and psychophysical studies including recent results of d.j. field et al. (1993), i. kovacs et al., and m.k. kapadia et al. (1995). the performance of the algorithm is demonstrated on a range of psychophysical stimuli and real images.
robust occluding contour detection using the hausdorff distance. in this paper, a correlational approach for distinguishing occluding contours from object markings for 3d object modeling is presented. the proposed method is valid under weak perspective projection, does not require to search for correspondences between frames, can handle scaling between consecutive images, thus can estimate the full euclidean surface structure, and does not require camera calibration or camera motion measurement. extensive experimental results show that the method is robust to the occlusion of feature points and image noise unlike previous affine-based approaches. qualitative and quantitative results for the relation between the required minimum viewing angle change for the detection and the surface curvature are also presented.
surface shape from warping. we propose a variant of shape from shading which we call shape from image warping. the idea is that the three-dimensional shape of an object is estimated by determining how much the image of the object is warped with respect to the image of a known prototype shape. we demonstrate that, for a class of reflectance functions, there is a direct relationship between these image warps and geometric warps of the underlying three-dimensional shapes. therefore detecting the image warp relative to a prototype of known shape allows us to reconstruct the shape of the imaged object. we derive properties of these shape warps and illustrate the results by recovering the shapes of faces. this relationship between image and shape warps helps us understand the relationship between image based models of object recognition and approaches based on three dimensional object models.
shape and albedo from multiple images using integrability. previous work has developed an approach for estimating shape and albedo from multiple images assuming lambertian reflectance with single light sources. the main contributions of this paper are: (i) to show how the approach can be generalized to include ambient background illumination, (ii) to demonstrate the use of the integrability constraint for solving this problem, and (iii) an iterative algorithm which is able to improve the analysis by finding shadows and rejecting them.
learning in gibbsian fields: how accurate and how fast can it be? gibbsian fields or markov random fields are widely used in bayesian image analysis, but learning gibbs models is computationally expensive. the computational complexity is pronounced by the recent minimax entropy (frame) models which use large neighborhoods and hundreds of parameters. in this paper, we present a common framework for learning gibbs models. we identify two key factors that determine the accuracy and speed of learning gibbs models: the efficiency of likelihood functions and the variance in approximating partition functions using monte carlo integration. we propose three new algorithms. in particular, we are interested in a maximum satellite likelihood estimator, which makes use of a set of precomputed gibbs models called "satellites" to approximate likelihood functions. this algorithm can approximately estimate the minimax entropy model for textures in seconds in a hp workstation. the performances of various learning algorithms are compared in our experiments.
learning generic prior models for visual computation. this paper presents a novel theory for learning generic prior models from a set of observed natural images based on a minimax entropy theory that the authors studied in texture modeling. we start by studying the statistics of natural images including the scale invariant properties, then generic prior models were learnt to duplicate the observed statistics. the learned gibbs distributions confirm and improve the forms of existing prior models, and more interestingly inverted potentials are found to be necessary, and such potentials produce patterns and enhance preferred image features. the learned model is compared with existing prior models in experiments of image restoration.
fully 3d active surface models with self--inflation and self--deflation forces. in this paper, we propose fully 3d active surface models for image segmentation. our models are capable of fitting a diverse range of region shapes. they have low sensitivity to initial shape and position. we design self--inflation/deflation forces, which cooperate naturally with gradient forces. they permit the active surface to travel a long distance without the aid of any external forces. they are easily controlled in both their directions and magnitude. the models produce accurate segmentation when tested with synthetic and real images. they manifest robustness to image noise and imperfect image data. importantly, they are capable of converging to the correct boundary even if the initial estimate is not close.
frame: filters, random fields, and minimax entropy-- towards a unified theory for texture modeling. in this paper, a minimax entropy principle is studied, based on which a novel theory, called frame (filters, random fields and minimax entropy) is proposed for texture modeling. frame combines attractive aspects of two important themes in texture modeling: multi-channel filtering and markov random field (mrf) modeling. it incorporates the responses of a set of well selected filters into the distribution over a random field, and hence has a much stronger descriptive ability than the traditional mrf models. furthermore, it interprets and clarifies many previous concepts and methods for texture analysis and synthesis from a unified point of view. algorithms are proposed for probability inference, stochastic simulation and filter selection. experiments on a variety of textures are described to illustrate our theory and to show the performance of our algorithms. these experiments demonstrate that many textures previously considered as different categories can be modeled and synthesized in a common framework.