waashk/dblp huggingface dataset:
sparse representations for image decomposition with occlusions. we study the problem of how to detect "interesting objects'' appeared in a given image, i. our approach is to treat it as a function approximation problem based on an over-redundant basis, and also account for occlusions, where the basis superposition principle is no longer valid. since the basis (a library of image templates) is over-redundant, there are infinitely many ways to decompose i. we are motivated to select a sparse/compact representation of i, and to account for occlusions and noise. we then study a greedy and iterative "weighted lp matching pursuit" strategy, with 0
an mimd computing platform for a hierarchical foveal machine vision system. a multiple instruction multiple data (mimd) parallel computing platform built upon a network of tms320c40/44s (c40/c44) for real-time image processing of a hierarchical foveal machine vision (hfmv) system is described in this paper. the architecture of the system, the parallel algorithm development environment, and strategies to map tasks into the computing platform are described. the platform supports both static and dynamic computing resource allocation. the performance of the computing platform is illustrated by examples.
appearance matching of occluded objects using coarse-to-fine adaptive masks. in this paper, we discuss an appearance matching technique for the interpretation of color scenes containing occluded objects. dealing with occlusions is very difficult, and we have explored the use of an iterative, coarse-to-fine correlation-based method that uses hypothesized occlusion events to modify the scene-to-template similarity measure at run-time. specifically, a binary mask is used to adaptively exclude regions of the template image from the correlation computation. at each iteration, these masks are adjusted based on higher resolution scene data and the occluding interactions between multiple object hypotheses. we present results which demonstrate the technique is reasonably robust over a large database of color test scenes containing objects at a variety of scales, and tolerates minor object rotations and global illumination variations.
detecting binocular half-occlusions: empirical comparisons of four approaches. binocular half-occlusion points are those that are visible in one of the two views provided by a binocular imaging system. due to their importance in binocular matching as well as, subsequent interpretation tasks, a number of approaches have been developed for dealing with such points. in the current paper, we consider five methods that explicitly detect half-occlusions and report on a more uniform comparison than has previously been performed. taking a disparity image and its associated match goodness image as input, we generate images that show the half-occluded points in the underlying scene. we quantitatively and qualitatively compare these methods under a variety of conditions.
image editing in the contour domain. image editing systems are essentially pixel-based. in this paper, we propose a novel method for image editing in which the primitive working unit is not a pixel but an edge. the feasibility of this proposal is suggested by recent work showing that a gray-scale image can be accurately represented by its edge map if a suitable edge model and scale selection method are employed [1]. in particular, an efficient algorithm has been reported to invert such an edge representation to yield a high-fidelity reconstruction of the original image [2], [3]. we have combined these algorithms together with an efficient method for contour grouping and an intuitive user interface to allow users to perform image editing operations (crop, paste, delete) directly in the contour domain. experimental results suggest that this novel combination of vision algorithms may increase the efficiency of certain classes of image editing operations.
a robust and convergent iterative approach for determining the dominant plane from two views without correspondence and calibration. a robust, iterative approach is introduced for finding the dominant plane in a scene using binocular vision. neither camera calibration nor stereo correspondence is required. recently cohen formalized a framework guaranteeing (local) convergence of iterative two-step methods. in this paper, the framework is adopted, with a global step using tentative matches to estimate the planar projectivity, and a local step attempting to solve the stereo correspondence. a detected point in the first image is matched to an auxiliary point in the second image, on the line joining the transformed first image point, and its closest detected second image point. convergence is assured, while achieving robustness to both mismatching and non-coplanar points.
space scale localization, blur, and contour-based image coding. we have recently proposed a scale-adaptive algorithm for reliable edge detection and blur estimation. the algorithm produces a contour code which consists of estimates of position, brightness, contrast and blur for each edge point in the image. here we address two questions: 1. can scale adaptation be used to achieve precise localization of blurred edges? 2. how much of the perceptual content of an image is carried by the 1-d contour code? we report an efficient algorithm for subpixel localization, and show that local scale control allows excellent precision even for highly blurred edges. we further show how local scale control can quantitatively account for human visual acuity of blurred edge stimuli. to address the question of perceptual content, we report an algorithm for inverting the contour code to reconstruct an estimate of the original image. while reconstruction based on edge brightness and contrast alone introduces significant artifact, restitution of the local blur signal is shown to produce perceptually accurate reconstructions.
body plans. this paper describes a representation for people and animals, called a body plan, which is adapted to segmentation and to recognition in complex environments. the representation is an organized collection of grouping hints obtained from a combination of constraints on color and texture and constraints on geometric properties such as the structure of individual parts and the relationships between parts. body plans can be learned from image data, using established statistical learning techniques. the approach is illustrated with two examples of programs that successfully use body plans for recognition: one example involves determining whether a picture contains a scantily clad human, using a body plan built by hand; the other involves determining whether a picture contains a horse, using a body plan learned from image data. in both cases, the system demonstrates excellent performance on large, uncontrolled test sets and very large and diverse control sets.
learning bilinear models for two-factor problems in vision. in many vision problems, we want to infer two (or more) hidden factors which interact to produce our observations. we may want to disentangle illuminant and object colors in color constancy; rendering conditions from surface shape in shape-from-shading; face identity and head pose in face recognition; or font and letter class in character recognition. we refer to these two factors generically as ``style'' and ``content''.bilinear models offer a powerful framework for extracting the two-factor structure of a set of observations, and are familiar in computational vision from several well-known lines of research. this paper shows how bilinear models can be used to learn the style-content structure of a pattern analysis or synthesis problem, which can then be generalized to solve related tasks using different styles and/or content. we focus on three tasks: extrapolating the style of data to unseen content classes, classifying data with known content under a novel style, and translating data from novel content classes and style to a known style or content. we show examples from color constancy, face pose estimation, shape-from-shading, typography and speech.
a robust clustering algorithm based on competitive agglomeration and soft rejection of outliers. we present a new clustering algorithm that addresses two major issues associated with conventional partitional clustering: the difficulty in determining the number of clusters, and the sensitivity to noise and outliers. the proposed algorithm determines the number of clusters by a process of competitive agglomeration. noise immunity is achieved by integrating concepts from robust statistics into the algorithm. the proposed approach can incorporate different distance measures in the objective function to find an unknown number of clusters of various types including lines, planes and surfaces.
the use of hybrid models to recover cardiac wall motion in tagged mr images. we present a new algorithm for the automatic recovery of tag grid-line intersections in tagged mr images of the left ventricle of the heart. our method uses an active spring mesh to capture local properties of the motion and a global motion model to capture the global coherence of the motion. we recover the global component of the motion using robust estimation. different motion models have been developed for short and long axis views of the heart. the algorithm has been tested on healthy and pathological data.
efficient stereo with multiple windowing. we present a new, efficient stereo algorithm addressing robust disparity estimation in the presence of occlusions. the algorithm is an adaptive, multi-window scheme using left-right consistency to compute disparity and its associated uncertainty. we demonstrate and discuss performances with both synthetic and real stereo pairs, and show how our results improve on those of closely related techniques for both robustness and efficiency.
efficient regularity-based grouping. this paper summarizes a novel logic-based approach to grouping and perceptual organization (presented more thoroughly in \cite{feldman_ci}), and presents novel efficient methods for computing interpretations in this framework. grouping interpretations are first defined as logical structures, built out of atomic premises (``regularities'') that are derived from considerations of non-accidentalness. these interpretations can then be partially ordered by their degree of regularity or constraint (measured numerically by their {\it codimension\/}). the genericity constraint---the principle that interpretations should minimize coincidences in the observed configuration---dictates that the preferred interpretation will be the minimum in this partial order, i.e. the interpretation with {\it maximum codimension.\/} the preferred interpretation, called the {\it qualitative parse\/}, corresponds neatly to the interpretation intuitively preferred by human observers. as a side-effect, the ``most salient'' or most structured part of the scene can be identified, as the highest-codimension subtree of the qualitative parse. an efficient ($o(n^2)$) method for computing the maximum codimension interpretation is presented, along with examples.
adaptive b-splines and boundary estimation. this paper describes a boundary estimation scheme based on a new adaptive approach to b-spline curve fitting. the number of control points of the spline, their locations, and the observation parameters, are all considered unknown. the optimal number of control points is estimated via a new minimum description length (mdl) type criterion. the result is an adaptive parametrically deformable contour which also estimates the observation model parameters. experiments on synthetic and real (medical) images confirm the adequacy and good performance of the approach.
the confounding of translation and rotation in reconstruction from multiple views. if 3d rigid motion is estimated with some error a distorted version of the scene structure will in turn be computed. of computational interest are these regions in space where the distortions are such that the depths become negative, because in order to be visible the scene has to lie in front of the image. the stability analysis for the structure-from-motion problem presented in this paper investigates the optimal relationship between the errors in the estimated translational and rotational parameters of a rigid motion, that results in the estimation of a minimum number of negative depth values. the input used is the value of the flow along some direction, which is more general than optic flow or correspondence. for a planar retina it is shown that the optimal configuration is achieved when the projections of the translational and rotational errors on the image plane are perpendicular. furthermore, the projection of the actual and the estimated translation lie on a line passing through the image center. for a spherical retina given a rotational error, the optimal translation is the correct one, while given a translational error. the optimal rotational error is normal to the translational one at an equal distance from the real and estimated translations. the proofs, besides illuminating the confounding of translation and rotation in structure from motion, have an important application to ecological optics, explaining differences of planar and spherical eye or camera designs in motion and shape estimation.
efficient approximation of range images through data-dependent adaptive triangulations. this paper presents an efficient algorithm for generating adaptive triangular meshes from dense range images. the proposed technique consists of two stages. first, a quadrilateral mesh is generated from the given range image. the points of this mesh adapt to the surface shapes represented in the range image by grouping in areas of high curvature and dispersing in low-variation regions. the second stage splits each quadrilateral cell obtained before into two triangles. between the two possible flips, it is chosen the one whose diagonal' s direction is closest to the orientation of the discontinuities present in that cell. both stages avoid costly iterative optimization techniques. results with real range images are presented. they show low cpu times and accurate triangular approximations of the given images.
3-d model-based tracking of humans in action: a multi-view approach. we present a vision system for the 3-d model-based tracking of unconstrained human movement. using image sequences acquired simultaneously from multiple views, we recover the 3-d body pose at each time instant without the use of markers. the pose-recovery problem is formulated as a search problem and entails finding the pose parameters of a graphical human model whose synthesized appearance is most similar to the actual appearance of the real human in the multi-view images. the models used for this purpose are acquired from the images. we use a decomposition approach and a best-first technique to search through the high dimensional pose parameter space. a robust variant of chamfer matching is used as a fast similarity measure between synthesized and real edge images. we present initial tracking results from a large new humans-in-action (hia) database containing more than 2500 frames in each of four orthogonal views. they contain subjects involved in a variety of activities, of various degrees of complexity, ranging from the more simple one-person hand waving to the challenging two-person close interaction in the argentine tango.
color-based tracking of heads and other mobile objects at video frame rates. we develop a simple and very fast method for object tracking based exclusively on color information in digitized video images. running on a silicon graphics r4600 indy system with an indycam, our algorithm is capable of simultaneously tracking objects at full frame size ($640\times480$ pixels) and video frame rate (30 fps). robustness with respect to occlusion is achieved via an explicit hypothesis-tree model of the occlusion process. we demonstrate the efficacy of our technique in the challenging task of tracking people, especially tracking human heads and hands.
visual organization for figure/ground separation. a common factor in all illusory contour figures is the perception of a surface occluding part of a background. in our previous work we have shown that by detecting junctions and assigning a proper set of hypothesis at each junction, we could diffuse this information and obtain surface reconstructions where the surface boundaries represented illusory contours. amodal completions emerge at the overlapping surfaces. we here address the problem of selecting the best image organization (set of hypothesis). we propose two optimization criteria, one based on a coherence measure between pairs of junctions (correlation between the diffusion of each pair) and another one based on an entropy measure (sharpness of the reconstruction). we show their similarity and a statistical physics approach to select the best organization. the experiments suggest that despite the large number of possible organizations our approach may take a few steps to select the best organization (starting from random organizations).
a region-level graph labeling approach to motion-based segmentation. this paper deals with the problem of motion-based segmentation of image sequences. such partitions are multiple-purpose in dynamic scene analysis. we first extract a texture-based partition using an unsupervised mrf approach. the regions obtained are then grouped according to a motion-based criterion. this grouping process relies on two motion estimation techniques and exploits contextual information between regions. in contrast with clustering techniques, region grouping is formalized as a motion-based graph labeling process, within a markovian framework. results on real-world image sequences are shown and validate the proposed method.
combining region splitting and edge detection through guided delaunay image subdivision. in this paper, an adaptive split-and-merge segmentation method is proposed. the splitting phase of the algorithm employs the incremental delaunay triangulation competent of forming grid edges of arbitrary orientation and position. the tessellation grid, defined by the delaunay triangulation, is adjusted to the semantics of the image data by combining similarity and difference information among pixels. experimental results on synthetic images show that the method is robust to different object edge orientations, partially weak object edges and very noisy homogeneous regions. experiments on a real image indicate that the method yields good segmentation results even when there is a quadratic sloping of intensities particularly suited for segmenting natural scenes of man-made objects.
target detection in foveal atr systems. automatic target recognition (atr) applications require simultaneously a wide field of view (fov) for better detection and situation awareness, high resolution for target recognition and threat assessment, and high frame rate for detecting brief events and disambiguating frame-to-frame correlation. uniformly sampling the entire fov at recognition resolution is simply wasteful in atr scenarios with localized regions of interest (rois). foveal data acquisition with space-variant sampling and context- sensitive sensor articulation is highly optimized for active atr applications. we propose a multiscale local zernike filter-based front end target detection technique for a commercially feasible foveal sensor topology with piecewise constant resolution profile. anisotropic heat diffusion is employed for preprocessing of the foveal data. expansion template matching is used to derive a detection filter that optimizes the discriminant signal-to-noise ratio (snr). results are presented with simulated foveal imagery derived from real uniform acuity flir data.
a formal classification of 3d medial axis points and their local geometry. abstract--this paper proposes a novel hypergraph skeletal representation for 3d shape based on a formal derivation ofthe generic structure of its medial axis. by classifying each skeletal point by its order of contact, we show that, generically, the medial axis consists of five types of points, which are then organized into sheets, curves, and points: 1) sheets (manifolds with boundary) which are the locus of bitangent spheres with regular tangency a_1^2 (a_k^n notation means n distinct k{\hbox{-}}{\rm{fold}} tangencies of the sphere of contact, as explained in the text); two types of curves, 2) the intersection curve of three sheets and the locus of centers of tritangent spheres, a_1^3, and 3) the boundary of sheets, which are the locus of centers of spheres whose radius equals the larger principal curvature, i.e., higher order contact a_3 points; and two types of points, 4) centers of quad-tangent spheres, a_1^4, and 5) centers of spheres with one regular tangency and one higher order tangency, a_1a_3. the geometry of the 3d medial axis thus consists of sheets (a_1^2) bounded by one type of curve (a_3) on their free end, which corresponds to ridges on the surface, and attached to two other sheets at another type of curve (a_1^3), which support a generalized cylinder description. the a_3 curves can only end in a_1a_3 points where they must meet an a_1^3 curve. the a_1^3 curves meet together in fours at an a_1^4 point. this formal result leads to a compact representation for 3d shape, referred to as the medial axis hypergraph representation consisting of nodes (a_1^4 and a_1 a_3 points), links between pairs of nodes (a_1^3 and a_3 curves) and hyperlinks between groups of links (a_1^2 sheets). the description of the local geometry at nodes by itself is sufficient to capture qualitative aspects of shapes, in analogy to 2d. we derive a pointwise reconstruction formula to reconstruct a surface from this medial axis hypergraph together with the radius function. thus, this information completely characterizes 3d shape and lays the theoretical foundation for its use in recognition, morphing, design, and manipulation of shapes.
on the intrinsic reconstruction of shape from its symmetries. the main question we address is: what is the minimal information required to generate closed, nonintersecting planar boundaries? for this paper, we restrict ¿shape¿ to this meaning. more precisely, we examine whether the medial axis, together with dynamics, can serve as a language to design shapes and to effect shape changes, e.g., for modeling, to generate a morph sequence, etc. we represent the medial axis together with a direction of flow along the axis as the shock graph and examine the reconstruction of shape along each of the three types of medial axis points, (labeled a_1^2, a_1^3, a_3; see below for the a notation) and the associated six types of shock points. first, we show that the tangent and curvature of the medial axis and the speed and acceleration of the shock with respect to time of propagation, i.e., first and second order geometric and dynamic properties, are sufficient to determine the boundary tangent and curvature at corresponding points of the boundary. this implies that a rather coarse sampling of the symmetry axis, its tangent, curvature, speed, and acceleration is sufficient to regenerate accurately a local neighborhood of shape at regular axis points (a_1^2). we also show how higher order differential properties of the axis can be related to the higher-order differential properties of the boundary of the same order. second, we examine the reconstruction of shape at branch points (a_1^3) where three regular branches are joined. we show that the three pairs of geometry (that is, curvature) and dynamics (that is, acceleration) must satisfy certain constraints. finally, we derive similar results for the end points of shock branches (a_3 points). these formulas completely specify the local reconstruction of a shape from its shock-graph or medial axis and the conditions required to form a coherent shape from the medial axis.