waashk/dblp huggingface dataset:
detection of concentric circles for camera calibration. the geometry of plane-based calibration methods is well understood, but some user interaction is often needed in practice for feature detection. this paper presents a fully automatic calibration system that uses patterns of pairs of concentric circles. the key observation is to introduce a geometric method that constructs a sequence of points strictly convergent to the image of the circle center from an arbitrary point. the method automatically detects the points of the pattern features by the construction method, and identify them by invariants. it then takes advantage of homological constraints to consistently and optimally estimate the features in the image. the experiments demonstrate the robustness and the accuracy of the new method.
circular motion geometry by minimal 2 points in 4 images. this paper describes a new and simple method of recovering the geometry of uncalibrated circular motion or single axis motion using a minimal data set of 2 points in 4 images. this problem has been solved using non-minimal data either by computing the fundamental matrix and trifocal tensor in 3 images, or by fitting conics to tracked points in 5 images. our new method first computes a planar homography from a minimum of 2 points in 4 images. it is shown that two eigenvectors of this homography are the images of the circular points. then, other fixed image entities and rotation angles can be straightforwardly computed. the crux of the method lies in relating this planar homography from two different points to a homology naturally induced by corresponding points on different conic loci from a circular motion. the experiments on real image sequences demonstrate the simplicity, accuracy and robustness of the new method.
epitomic analysis of appearance and shape. we present novel simple appearance and shape models that we call epitomes. the epitome of an image is its miniature, condensed version containing the essence of the textural and shape properties of the image. as opposed to previously used simple image models, such as templates or basis functions, the size of the epitome is considerably smaller than the size of the image or object it represents, but the epitome still contains most constitute elements needed to reconstruct the image (fig. 1). a collection of images often shares an epitome, e.g., when images are a few consecutive frames from a video sequence, or when they are photographs of similar objects.a particular image in a collection is defined by its epitome and a smooth mapping from the epitome to the image pixels. when the epitomic representation is used within a hierarchical generative model, appropriate inference algorithms can be derived to extract the epitome from a single image or a collection of images and at the same time perform various inference tasks, such as image segmentation, motion estimation, object removal and super-resolution.
model-based matching of line drawings by linear combinations of prototypes. we describe a technique for finding pixelwise correspondences between two images by using models of objects of the same class to guide the search. the object models are "learned" from example images (also called prototypes) of an object class. the models consist of a linear combination of prototypes. the flow fields giving pixelwise correspondences between a base prototype and each of the other prototypes must be given. a novel image of an object of the same class is matched to a model by minimizing an error between the novel image and the current guess for the closest model image. currently, the algorithm applies to line drawings of objects. an extension to real grey level images is discussed.
multidimensional morphable models. we describe a flexible model for representing images of objects of a certain class, known a priori, such as faces, and introduce a new algorithm for matching it to a novel image and thereby performing image analysis. we call this model a multidimensional morphable model or just a morphable model. the morphable model is learned from example images (calledprototypes) of objects of a class. in this paper we introduce an effective stochastic gradient descent algorithm that automatically matches a model to a novel image by finding the parameters that minimize the error between the image generated by the model and the novelimage. two examples demonstrate the robustness and the broad range of applicability of the matching algorithm and the underlying morphable model. our approach can provide novel solutions to several vision tasks, including the computation of image correspondence, object verification, image synthesis and image compression.
layered active appearance models. active appearance models (aams) provide a framework for modeling the joint shape and texture of an image. an aam is a compact representation of both factors in a conditionally linear model. however, the standard aam framework does not handle images which have missing features, or allow modification of certain structures in the image while leaving neighboring ones undeformed. we introduce the layered active appearance model (laam), which allows for missing features, occlusion, substantial spatial rearrangement of features, and which provides a more general representation that extends the applicability of the active appearance model.
structure and motion estimation from dynamic silhouettes under perspective projection. addresses the problem of estimating the structure and motion of a smooth curved object from its silhouettes observed over time by a trinocular stereo rig under perspective projection. we first construct a model for the local structure along the silhouette for each frame in the temporal sequence. successive local models are then integrated into a global surface description by estimating the motion between successive time instants. the algorithm tracks certain surface features (parabolic points) and image features (silhouette inflections and frontier points) which are used to bootstrap the motion estimation process. the entire silhouette along with the reconstructed local structure are then used to refine the initial motion estimate. we have implemented the proposed approach and report results on real images.
high resolution terrain mapping using low altitude aerial stereo imagery. this paper presents an approach to build high resolutiondigital elevation maps from a sequence of unregistered lowaltitude stereovision image pairs. the approach first uses avisual motion estimation algorithm that determines the 3dmotions of the cameras between consecutive acquisitions,on the basis of visually detected and matched environmentfeatures. an extended kalman filter then estimates both the6 position parameters and the 3d positions of the memorizedfeatures as images are acquired. details are given onthe filter implementation and on the estimation of the uncertaintieson the feature observations and motion estimations.experimental results show that the precision of the methodenables to build spatially consistent very large maps.
creating efficient codebooks for visual recognition. visual codebook based quantization of robust appearance descriptors extracted from local image patches is an effective means of capturing image statistics for texture analysis and scene classification. codebooks are usually constructed by using a method such as k-means to cluster the descriptor vectors of patches sampled either densely (¿textons¿) or sparsely (¿bags of features¿ based on key-points or salience measures) from a set of training images. this works well for texture analysis in homogeneous images, but the images that arise in natural object recognition tasks have far less uniform statistics. we show that for dense sampling, k-means over-adapts to this, clustering centres almost exclusively around the densest few regions in descriptor space and thus failing to code other informative regions. this gives suboptimal codes that are no better than using randomly selected centres. we describe a scalable acceptance-radius based clusterer that generates better codebooks and study its performance on several image classification tasks. we also show that dense representations outperform equivalent keypoint based ones on these tasks and that svm or mutual information based feature selection starting from a dense codebook further improves the performance.
unsupervised non-parametric region segmentation using level sets. we present a novel non-parametric unsupervised segmentationalgorithm based on region competition [21];but implemented within a level sets framework [11]. thekey novelty of the algorithm is that it can solve n ¿ 2 classsegmentation problems using just one embedded surface;this is achieved by controlling the merging and splitting behaviourof the level sets according to a minimum descriptionlength (mdl) [6, 14] cost function. this is in contrastto n class region-based level set segmentation methods todate which operate by evolving multiple coupled embeddedsurfaces in parallel [3, 13, 20]. furthermore, it operates inan unsupervised manner; it is necessary neither to specifythe value of n nor the class models a-priori.we argue that the level sets methodology provides amore convenient framework for the implementation of theregion competition algorithm, which is conventionally implementedusing region membership arrays due to the lackof a intrinsic curve representation. finally, we generalisethe gaussian region model used in standard region competitionto the non-parametric case. the region boundary motionand merge equations become simple expressions containingcross-entropy and entropy terms.
multiple view geometry and the l-norm. this paper presents a new framework for solving geometric structure and motion problems based on l_¿ -norm. instead of using the common sum-of-squares cost-function, that is, thel_¿ -norm, the model-fitting errors are measured using the l_¿ -norm. unlike traditional methods based on l_¿, our framework allows for efficient computation of global estimates. we show that a variety of structure and motion problems, for example, triangulation, camera resectioning and homography estimation can be recast as a quasi-convex optimization problem within this framework. these problems can be efficiently solved using second order cone programming (socp) which is a standard technique in convex optimization. the proposed solutions have been validated on real data in different settings with small and large dimensions and with excellent performance.
multiview reconstruction of space curves. is the real problem in resolving correspondence using currentstereo algorithms the lack of the "right" matching criterion?in studying the related task of reconstructing three-dimensionalspace curves from their projections in multipleviews, we suggest that the problem is more basic: matchingand reconstruction are coupled, and so reconstruction algorithmsshould exploit this rather than assuming that matchingcan be successfully performed before reconstruction. torealize this coupling, a generative model of curves is introducedwhich has two key components: (i) a prior distributionof general space curves and (ii) an image formation modelwhich describes how 3d curves are projected onto the imageplane. a novel aspect of the image formation model is that ituses an exact description of the gradient field of a piecewiseconstant image. based on this forward model, a fully automaticalgorithm for solving the inverse problem is developedfor an arbitrary number of views. the resulting algorithmis robust to partial occlusion, deficiencies in image curveextraction and it does not rely on photometric information.the relative motion of the cameras is assumed to be given.several experiments are carried out on various realistic scenarios.in particular, we focus on scenes where traditionalcorrelation-based methods would fail.
globally optimal estimates for geometric reconstruction problems. we introduce a framework for computing statistically optimal estimates of geometric reconstruction problems. while traditional algorithms often suffer from either local minima or non-optimality - or a combination of both - we pursue the goal of achieving global solutions of the statistically optimal cost-function. our approach is based on a hierarchy of convex relaxations to solve non-convex optimization problems with polynomials. these convex relaxations generate a monotone sequence of lower bounds and we show how one can detect whether the global optimum is attained at a given relaxation. the technique is applied to a number of classical vision problems: triangulation, camera pose, homography estimation and last, but not least, epipolar geometry estimation. experimental validation on both synthetic and real data is provided. in practice, only a few relaxations are needed for attaining the global optimum.
using conic correspondence in two images to estimate the epipolar geometry. in this paper it is shown how corresponding conics in two images can be used to estimate the epipolar geometry in terms of the fundamental/essential matrix. the corresponding conics can be images of either planar conics or silhouettes of quadrics. it is shown that one conic correspondence gives two independent constraints on the fundamental matrix and a method to estimate the fundamental matrix from at least four corresponding conics is presented. furthermore, a new type of fundamental matrix for describing conic correspondences is introduced. finally, it is shown that the problem of estimating the fundamental matrix from 5 point correspondences and 1 conic correspondence in general has 10 different solutions. a method to calculate these solutions is also given together with an experimental validation.
3d human body model acquisition from multiple views. we present a novel motion-based approach for the part determination and shape estimation of a human's body parts. the novelty of the technique is that neither a prior model of the human body is employed nor prior body part segmentation is assumed. we present a human body part identification strategy (hbpis) that recovers all the body parts of a moving human based on the spatiotemporal analysis of its deforming silhouette. we formalize the process of simultaneous part determination and 2d shape estimation by employing the supervisory control theory of discrete event systems. in addition, in order to acquire the 3d shape of the body parts, we present a new algorithm which selectively integrates the (segmented by the hbpis) apparent contours, from three mutually orthogonal views. the effectiveness of the approach is demonstrated through a series of experiments, where a subject performs a set of movements according to a protocol that reveals the structure of the human body.
a robot system that observes and replicates grasping tasks. to alleviate the problem of overwhelming complexity in grasp synthesis and path planning associated with robot task planning, we adopt the approach of teaching the robot by demonstrating in front of it. the system has four components: the observation system, the grasping task recognition module, the task translator and the robot system. the observation system comprises an active multibaseline stereo system and a dataglove. the data stream recorded is then used to track object motion; this paper illustrates how complimentary sensory data can be used for this purpose. the data stream is also interpreted by the grasping task recognition module, which produces higher levels of abstraction to describe both the motion and actions taken in the task. the resulting information are provided to the task translator which creates commands for the robot system to replicate the observed task. in this paper we describe how these components work with special emphasis on the observation system. the robot system that we use to perform the grasping tasks comprises the puma 560 arm and the utah/mit hand.
a multibaseline stereo system with active illumination and real-time image acquisition. we describe our implementation of a parallel depth recovery scheme for a four-camera multibaseline stereo in a convergent configuration. our system is capable of image capture at video rate. this is critical in applications that require three-dimensional tracking. we obtain dense stereo depth data by projecting a light pattern of frequency modulated sinusoidally varying intensity onto the scene, thus increasing the local discriminability at each pixel and facilitating matches. in addition, we make most of the camera view areas by converging them at a volume of interest. results show that we are able to extract stereo depth data that are, on the average, less than 1 mm in error at distances between 1.5 to 3.5 m away from the cameras.
unsupervised parallel image classificiation using a hierarchical markovian model. the paper deals with the problem of unsupervised classification of images modeled by markov random fields (mrf). if the model parameters are known then we have various methods to solve the segmentation problem (simulated annealing, icm, etc...). however, when they are not known, the problem becomes more difficult. one has to estimate the hidden label field parameters from the only observable image. our approach consists of extending a recent iterative method of estimation, called iterative conditional estimation (ice) to a hierarchical markovian model. the idea resembles the estimation-maximization (em) algorithm as we recursively look at the maximum a posteriori (map) estimate of the label field given the estimated parameters then we look at the maximum likelihood (ml) estimate of the parameters given a tentative labeling obtained at the previous step. we propose unsupervised image classification algorithms using a hierarchical model. the only parameter supposed to be known is the number of regions, all the other parameters are estimated. the presented algorithms have been implemented on a connection machine cm200. comparative tests have been done on noisy synthetic and real images (remote sensing).
reliable recovery of piled box-like objects via parabolically deformable superquadrics. automatic unloading of piled box-like objects is undoubtedlyof great importance to the industry. in this contributiona system addressing this problem is described: weemploy a laser range finder for data acquisition, and globallydeformable superquadrics [2], [22] for object modeling.our technique is based on a hypothesis generation andrefinement scheme. the vertices of the piled objects are extractedand superquadric seeds are aligned at these vertices.the model parameter recovery task is decomposedinto two subproblems, each dealing with a subset of themodel's parameter set. both region and boundary based informationsources are used for parameter estimation. comparedto a widespread strategy for superquadric recovery[11], our method shows advantages in terms of robustnessand computational efficiency. in addition, our system exhibitsversatility with regard to existing industrial systems,since it can effectively deal with both neatly placed and jumbledconfigurations of objects
accurate, real-time, unadorned lip tracking. human speech is inherently multi-modal, consisting of both audio and visual components. recently researchers have shown that the incorporation of information about the position of the lips into acoustic speech recognisers enables robust recognition of noisy speech. in the case of hidden markov model-recognition, we show that his happens because the visual signal stabilises the alignment of states. it is also shown that unadorned lips, both the inner and outer contours, can be robustly tracked in real time on general-purpose workstations. to accomplish this, efficient algorithms are employed which contain three key components: shape models, motion models, and focused colour feature detectors ¿ all of which are learnt from examples.
consistent surface color for texturing large objects in outdoor scenes. color appearance of an object is significantly influenced by the color of the illumination. when the illumination color changes, the color appearance of the object will change accordingly, causing its appearance to be inconsistent. to arrive at color constancy, we have developed a physics-based method of estimating and removing the illumination color. in this paper, we focus on the use of this method to deal with outdoor scenes, since very few physics-based methods have successfully handled outdoor color constancy. our method is principally based on shadowed and non-shadowed regions. previously researchers have discovered that shadowed regions are illuminated by sky light, while non-shadowed regions are illuminated by a combination of sky light and sunlight. based on this difference of illumination, we estimate the illumination colors (both the sunlight and the sky light) and then remove them. to reliably estimate the illumination colors in outdoor scenes, we include the analysis of noise, since the presence of noise is inevitable in natural images. as a result, compared to existing methods, the proposed method is more effective and robust in handling outdoor scenes. in addition, the proposed method requires only a single input image, making it useful for many applications of computer vision.
recovering epipolar geometry by reactive tabu search. in this paper we propose a new approach to recovering epipolar geometry from a pair of uncalibrated images. we first detect the feature points. by minimizing a proposed cost function, we match the feature points, discard the outliers and recover the epipolar geometry in one step. experiments on real images show that this approach is effective and fast.
united snakes. since their debut in 1987, snakes (active contour models) have become a standard image analysis technique with several variants now in common use. we present a portable, reusable, software package called "united snakes". the package unites the most popular snake variants, including finite difference, b-spline, and hermite polynomial snakes within the mathematical framework of a general finite element formulation with a choice of shape functions. the package furthermore incorporates a recently proposed snake-like technique known as "livewire". we integrate snakes and livewire by introducing an effective method for imposing hard constraints on snakes. our experiments demonstrate that snakes and livewire have complementary strengths and that their union offers a more powerful tool for interactive image analysis, especially for medical imaging applications. united snakes is implemented in java as a javabean so that it can easily be integrated in end-user application systems.
annular symmetry operators: a method for locating and describing objects. we present a machine vision system in which segmentation is computed in conjunction with a structural description of objects in the scene. it is assumed that contrast edges capture all relevant object information. the principles which dictate how edge features are grouped to infer objects are based upon detecting symmetrical enclosing edge configurations. these are detected using annular operators applied at multiple scales to edge data which have been extracted at multiple scales from a gray level image. the subsequent grouping of symmetry points results in a set of parts which make it possible to identify the location of objects within an image. these parts are used as a basis for constructing coarse graph-based descriptors for the perceptually significant objects found in the scene. results are presented to illustrate the method's performance on several images.