waashk/dblp huggingface dataset:
polymorphic grouping for image segmentation. the paper describes a new approach to image segmentation. it accepts the inherent deficiencies occuring when extracting low-level features and when dealing with the complexity of real scenes. image segmentation therefore is understood as deriving a rich symbolic description useful for tasks such as stereo or object recognition in outdoor scenes. the approach is based on a polymorphic scheme for simultaneously extracting points, lines and segments in a topologically consistent manner, together with their mutual relations derived from the feature adjacency graph (fag) thereby performing several grouping steps which gradually use more and more specific domain knowledge to achieve an optimal image description. the heart of the approach is (1) a detailed analysis of the fag and (2) a robust estimation for validating the found geometric hypotheses. the analysis of the fag, derived from the exoskeleton of the features, allows to detect inconsistencies of the extracted features with the ideal image model, a cell-complex. the fag is used for finding hypotheses about incidence relations and geometric hypotheses, such as collinearity or parallelity, also between non-neighbored points and lines. the m-type robust estimation is used for simultaneously eliminating wrong hypotheses on geometric relationships. it uses a new argument for the weighting function.
combining color and geometry for the active, visual recognition of shadows. shadows are a frequent occurrence, but they cannot be infallibly recognized until a scene's geometry and lighting are known. we present a number of cues which together strongly suggest the identification of a shadow and which can be examined with low cost. the techniques are: a color image segmentation method that recovers single material surfaces as single image regions irregardless of the surface partially in shadow, a method to recover the penumbra and umbra of shadow; a method for determining whether some object could be obstructing a light source. the last cue requires the examination of well understood shadows in the scene. our observer is equipped with an extendable probe for casting its own shadows. actively obtained shadows allow the observer to experimentally determine the location of the light sources in the scene. the system has been tested both indoors and out.
texture segmentation by multiscale aggregation of filter responses and shape elements. texture segmentation is a difficult problem, as is apparentfrom camouflage pictures. a textured region can containtexture elements of various sizes, each of which can itselfbe textured. we approach this problem using a bottom-upaggregation framework that combines structural characteristicsof texture elements with filter responses. our processadaptively identifies the shape of texture elements and characterizethem by their size, aspect ratio, orientation, brightness,etc., and then uses various statistics of these propertiesto distinguish between different textures. at the sametime our process uses the statistics of filter responses tocharacterize textures. in our process the shape measuresand the filter responses crosstalk extensively. in addition,a top-down cleaning process is applied to avoid mixing thestatistics of neighboring segments. we tested our algorithmon real images and demonstrate that it can accurately segmentregions that contain challenging textures.
3d reconstruction with projective octrees and epipolar geometry. in this paper, the problem of generating a 3d octree-like structure with the help of epipolar geometry within a projective framework is addressed. after a brief introduction on the basics of octrees and epipolar geometry, the new concept called "projective octree" is introduced together with an algorithm for building this projective structure. finally, some results of the implementations are presented in the last section together with the conclusions and future work.
surface orientation and curvature from differential texture distortion. a unified differential geometric framework for estimation of local surface shape and orientation from projective texture distortion is proposed, based on a differential version of the texture stationarity assumption introduced by malik and rosenholtz. this framework allows the information content of the gradient of any texture descriptor defined in a local coordinate frame to be characterized in a very compact form. the analysis encompasses both full affine texture descriptors and the classical "texture gradients". for estimation of local surface orientation and curvature from uncertain observations of affine texture distortion, the proposed framework allows the dimensionality of the search space to be reduced from five to one.
when does a camera see rain? rain produces sharp intensity fluctuations in images and videos, which degrade the performance of outdoor vision systems. these intensity fluctuations depend on various factors, such as the camera parameters, the properties of rain, and the brightness of the scene. we show that the properties of rain ¿ its small drop size, high velocity, and low density ¿ make its visibility strongly dependent on camera parameters such as exposure time and depth of field. we show that these parameters can be selected so as to reduce or even remove the effects of rain without altering the appearance of the scene. conversely, the parameters of a camera can also be set to enhance the visual effects of rain. this can be used to develop an inexpensive and portable camera-based rain gauge that provides instantaneous rain rate measurements. the proposed methods serve to make vision algorithms more robust to rain without any necessity for post-processing. in addition, they can be used to control the visual effects of rain during the filming of movies.
accurate motion flow estimation with discontinuities. we address the problem of motion flow estimation for a scene with multiple moving objects, observed from a possibly moving camera. we take as input a (possibly sparse) noisy velocity field, as obtained from local matching, produce a set of motion boundaries, and identify pixels with different velocities in overlapping layers. for a fixed observer, these overlapping layers capture occlusion information. for a moving observer, further processing is required to segment independent objects and infer structure. unlike previous approaches, which generate layers by iteratively fitting data to a set of predefined parameters, we instead find boundaries first, then infer regions and address occlusion overlap relationships. all computational steps use a common framework of tensors to represent velocity information, together with saliency (confidence), and uncertainty. communication between sites is performed by convolution-like tensor voting. the scheme is non-iterative, and the only free parameter is the scale, related to neighborhood size. we illustrate the approach with results obtained from synthetic sequences and from real images. the quantitative results compare favorably with those of other methods, especially in the presence of occlusion
parameterized image varieties: a novel approach to the analysis and synthesis of image sequences. this paper addresses the problem of characterizing the space formed by all images of a rigid set of n points observed by a weak perspective or paraperspective camera. by taking explicitly into account the euclidean constraints associated with calibrated cameras, we showthat this space is a six-dimensional variety embedded in ir2n, and parameterize it using the image positions of three reference points. this parameterization is constructed vialinear least squares from point correspondences established across a sequence of images, and it is used to synthesize new pictures without any explicit three-dimensional model. degenerate scene and camera configurations are analyzed, and experiments with real image sequences are presented.
mean shift based clustering in high dimensions: a texture classification example. feature space analysis is the main module in many computer vision tasks. the most popular technique, k-means clustering, however, has two inherent limitations: the clusters are constrained to be spherically symmetric and their number has to be known a priori. in nonparametric clustering methods, like the one based on mean shift, these limitations are eliminated but the amount of computation becomes prohibitively large as the dimension of the space increases. we exploit a recently proposed approximation technique, locality-sensitive hashing (lsh), to reduce the computational complexity of adaptive mean shift. in our implementation of lsh the optimal parameters of the data structure are determined by a pilot learning procedure, and the partitions are data driven. as an application, the performance of mode and k-means based textons are compared in a texture classification study.
incorporating the torrance and sparrow model of reflectance in uncalibrated photometric stereo. under the lambertian reflectance model, uncalibrated photometricstereo with unknown light sources is inherentlyambiguous. in this paper, we consider the use of a moregeneral reflectance model, namely the torrance and sparrowmodel, in uncalibrated photometric stereo. we demonstratethat this can not only resolve the ambiguity when thelight sources are unknown, but can also result in more accuratesurface reconstructions and can capture the reflectanceproperties of a large number of non-lambertian surfaces.our method uses single light source images with unknownlighting and no knowledge about the parameters of the reflectancemodel. it can recover the 3-d shape of surfaces(up to the binary convex/concave ambiguity) together withtheir reflectance properties. we have successfully tested ouralgorithm on a variety of non-lambertian surfaces demonstratingthe effectiveness of our approach. in the case ofhuman faces, the estimated skin reflectance has been shownto closely resemble the measured skin reflectance reportedin the literature. we also demonstrate improved recognitionresults on 4050 images of 10 faces with variable lightingand viewpoint when the synthetic image-based representationsof the faces are generated using the surface reconstructionsand reflectance properties recovered while assumingthe extended reflectance model.
modeling bayesian estimation for deformable contours. a novel trainable snake model, called eigensnake, is presented in the bayesian framework. in the eigensnake, prior knowledge of a specific object shape, such as that of face outlines and facial features, is derived from a training set of the shape and incorporated into a bayesian snake model in the form of the prior distribution. further, a "shape space", which is constructed on the basis of a set of eigenvectors obtained from principle component analysis, is used to restrict and stabilize the search for the optimal solution. the effectiveness is demonstrated by experiments, which shows that the eigensnake produces more reliable and accurate results than existing models.
fragmentation in the vision of scenes. natural images are highly structured in their spatial configuration. where one would expect a different spatial distribution for every image, as each image has a different spatiallay out, we show that the spatial statistics of recorded images can be explained by a single process of sequential fragmentation. the observation by a resolution limited sensory system turns out to have a profound influence on the observed statistics of natural images. the power-law and normal distribution represent the extreme cases of sequential fragmentation. between these two extremes, spatial detail statistics deform from power-law to normal through the weibull type distribution as receptive field size increases relative to image detail size.
reflectance-based classification of color edges. we aim at using color information to classify the physicalnature of edges in video. to achieve physics-based edgeclassification, we first propose a novel approach to coloredge detection by automatic noise-adaptive thresholdingderived from sensor noise analysis. then, we present a taxonomyon color edge types. as a result, a parameter-freeedge classifier is obtained labeling color transitions intoone of the following types: (1) shadow-geometry, (2) highlightedges, (3) material edges. the proposed method isempirically verified on images showing complex real worldscenes.
image indexing using composite color and shape invariant features. new sets of color models are proposed for object recognition invariant to a change in view point, object geometry and illumination. further, computational methods are presented to combine color and shape invariants to produce a high-dimensional invariant feature set for discriminatory object recognition.experiments on a database of 500 images show that object recognition based on composite color and shape invariant features provides excellent recognition accuracy. furthermore, object recognition based on color invariants provides very high recognition accuracy whereas object recognition based entirely on shape invariants yields very poor discriminative power.the image database and the performance of the recognition scheme can be experienced within pictoseek on-line as part of the zomax system at: http://www.wins.uva.nl/research/isis/zomax/.
mirrors in motion: epipolar geometry and motion estimation. in this paper we consider the images taken from pairs ofparabolic catadioptric cameras separated by discrete motions.despite the nonlinearity of the projection model, theepipolar geometry arising from such a system, like the perspectivecase, can be encoded in a bilinear form, the catadioptricfundamental matrix. we show that all such matriceshave equal lorentzian singular values, and they definea nine-dimensional manifold in the space of 4 × 4 matrices.furthermore, this manifold can be identified with a quotientof two lie groups. we present a method to estimate a matrixin this space, so as to obtain an estimate of the motion.we show that the estimation procedures are robust to modestdeviations from the ideal assumptions.
optical flow and deformable objects. when a plane undergoes a deformation that can be represented by a planar linear vector field, the projected vector field on the image plane of an optical device is at most quadratic. this 2d motion field has one singular point, with eigenvalues identical to those of the singular point describing the deformation. as a consequence, the nature of the singular point of the deformation is a projective invariant. when the plane moves and experiences a linear deformation at the same time, the associated 2d motion field is still quadratic with at most 3 singular points. in the case of a normal rototranslation, i.e. when the angular velocity is normal to the plane, and of a linear deformation, the 2d motion field has at most one singular point and substantial information on the rigid motion and on the deformation can be recovered from it. experiments with simulated deformations and real deformable objects show that the proposed analysis can provide accurate results and information on more general 3d deformations.
on the local form and transitions of symmetry sets, medial axes, and shocks. in this paper we explore the local geometry of the medial axis (ma) and shocks (sh), and their structural changes under deformations, by viewing these symmetries as subsets of the symmetry set (ss) and present two results. first, we establish that the local form of the medial axis must generically be one of three cases, which we denote by the a notation explained below (here, it merely serves as a reference to sections of the paper): endpoints (a3), interior points (a12), and junctions (a13). the local form of shocks is a sub-classification of these points into six types. second, we address the (classical) instabilities of the ma, i.e., abrupt changes in the representation with a slight changes in shape, as when a new branch appears with slight protrusion. the identification of these &lsquo;transitions&rsquo; is clearly crucial in robust object recognition. we show that for the medial axis only two such instabilities are generically possible: (i) when four branches come together (a14), and (ii) when a new branch grows out of an existing one (a1a3). similarly, there are six cases of shock instabilities, derived as sub-classifications of the ma instabilities. we give an explicit example of a dent forming in an ellipse where many of the transitions described in the paper can be seen to appear.
affine invariant medial axis and skew symmetry. affine invariant medial axes and symmetry sets of planar shapes are intr oduced and studied in this paper. two different approaches are presented. the first one is based on affine invariant distances, and defines the symmetry set, a set containing the medial axis, as the closure of the locus of points on (at least) two affine normals and affine-equidistant from the corresponding points on the curve. the second approach is based on a affine bitangent conics. in this case the symmetry set is defined as the closure of the locus of centers of conics with (at least) three-point contact with two or more distinct points on the curve. this is equivalent to conic and curve having, at those points, the same affine tangent, or the same euclidean tangent and curvature. although the two analogous definitions for the classical euclidean symmetry set (medial axis) are equivalent, this is not the case for the affine group. we then show how to use the symmetry set to detect affine skew symmetry, proving that the contact based symmetry set is a straight line if and only if the given shape is the affine transformation of a symmetric object.
on the use of marginal statistics of subband images. a commonly used representation of a visual pattern is the set of marginal probability distributions of the output of a bank of filters (gaussian, laplacian, gabor etc...). this representation has been used effectively for a variety of vision tasks including texture classification, texture synthesis, object detection and image retrieval. this paper examines the ability of this representation to discriminate between an arbitrary pair of visual stimuli. examples of patterns are derived that provably possess the same marginal statistical properties, yet are "visually distinct." these results suggest the need for either employing a large and diverse filter bank or incorporating joint statistics in order to represent a large class of visual patterns.
ego-motion and omnidirectional cameras. recent research in image sensors has produced cameras with very large fields of view. an area of computer vision research which will benefit from this technology is the computation of camera motion (ego-motion) from a sequence of images. traditional cameras suffer from the problem that the direction of translation may lie outside of the field of view, making the computation of camera motion sensitive to noise. in this paper, we present a method for the recovery of ego-motion using omnidirectional cameras. noting the relationship between spherical projection and wide-angle imaging devices, we propose mapping the image velocity vectors to a sphere, using the jacobian of the transformation between the projection model of the camera and spherical projection. once the velocity vectors are mapped to a sphere, we show how existing ego-motion algorithms can be applied and present some experimental results. these results demonstrate the ability to compute ego-motion with omnidirectional cameras.
registration of multiple point sets using the em algorithm. in this paper we address the problem of global registration between multiple dimensional point patterns with a given correspondence. the actual overlapping is not necessarily between pairs. instead, it can be between any number of patterns. it is assumed that each pattern is a portion of an image of an unobserved object under a distinct rigid transformation. we derive an iterative solution for the problem of global registration of the patterns in order to reconstruct the original object.our solution is based on the em algorithm and it generalizes the well known solutions for the two-pattern case. we also suggest a very efficient method to implement the proposed algorithm. experimental results demonstrate the improved performance of the proposed method.
an efficient image similarity measure based on approximations of kl-divergence between two gaussian mixtures. in this work we present two new methods for approximating the kullback-liebler (kl) divergence between two mixtures of gaussians. the first method is based on matching between the gaussian elements of the two gaussian mixture densities. the second method is based on the unscented transform. the proposed methods are utilized for image retrieval tasks. continuous probabilistic image modeling based on mixtures of gaussians together with kl measure for image similarity, can be used for image retrieval tasks with remarkable performance. the efficiency and the performance of the kl approximation methods proposed are demonstrated on both simulated data and real image datasets. the experimental results indicate that our proposed approximations outperform previously suggested methods.
vignette and exposure calibration and compensation. we discuss calibration and removal of "vignetting" (radial falloff) and exposure (gain) variations from sequences of images. unique solutions for vignetting, exposure and scene radiances are possible when the response curve is known. when the response curve is unknown, an exponential ambiguity prevents us from recovering these parameters uniquely. however, the vignetting and exposure variations can nonetheless be removed from the images without resolving this ambiguity. applications include panoramic image mosaics, photometry for material reconstruction, image-based rendering, and preprocessing for correlation-based vision algorithms.
shape and spatially-varying brdfs from photometric stereo. this paper describes a photometric stereo method designed for surfaces with spatially-varying brdfs, including surfaces with both varying diffuse and specular properties. our method builds on the observation that most objects are composed of a small number of fundamental materials. this approach recovers not only the shape but also material brdfs and weight maps, yielding compelling results for a wide variety of objects. we also show examples of interactive lighting and editing operations made possible by our method.
a semi-supervised framework for mapping data to the intrinsic manifold. this paper presents a novel scheme for manifold learning. different from the previous work reducing data to euclidean space which cannot handle the looped manifold well, we map the scattered data to its intrinsic parameter manifold by semi-supervised learning. given a set of partially labeled points, the map to a specified parameter manifold is computed by an iterative neighborhood average method called anchor points diffusion procedure (apd). we explore this idea on the most frequently used close-formed manifolds, stiefel manifolds whose special cases include hyper sphere and orthogonal group. the experiments show that apd can recover the underlying intrinsic parameters of points on scattered data manifold successfully.