waashk/dblp huggingface dataset:
detection and description of buildings from multiple aerial images. a method for detection and description of rectangular buildings from two or more registered aerial intensity images is proposed. the output is a 3d description of the buildings, with an associated confidence measure for each building. hierarchical perceptual grouping and matching across views is employed to increase the robustness of the system. verification of selected building hypotheses is done using shadow and wall evidence of the buildings. the system is largely feature-based. grouping and matching are performed in a hierarchical manner, utilizing primitives of increasing complexity, starting with line segments and junctions, and proceeding to higher level features. binocular and trinocular epipolar constraints are used to reduce the search space for matching features.
recovering affine motion and defocus blur simultaneously. motion in depth and/or zooming cause defocus blur. we show how the defocus blur in an image can be recovered simultaneously with affine motion. we introduce the theory, develop a solution method and demonstrate the validity of the theory and the solution by conducting experiments with real scenery.
global models with parametric offsets as applied to cardiac motion recovery. we introduce a new solid shape model formulation that includes built-in offsets from a base global component (e.g. an ellipsoid) which are functions of the global component's parameters. the offsets provide two features. first, they help to form an expected model shape which facilitates appropriate model data correspondences. second, they scale with the base global model to maintain the expected shape even in the presence of large global deformations. we apply this model formulation to the recovery of 3-d cardiac motion from a volunteer dataset of tagged-mr images. the model instance is a variation of the hybrid volumetric ventriculoid (hvv), a deformable thick-walled ellipsoid model resembling the left ventricle (lv) of the heart. a unique aspect of our implementation is the employment of constant volume constraints when recovering the cardiac motion. in addition, we present a novel geodesic-like prismoidal tessellation of the model which provides for more stable fits.
occlusion detectable stereo -- occlusion patterns in camera matrix. in stereo algorithms with more than two cameras, the improvement of accuracy is often reported since they are robust against noise. however, another important aspect of the polynocular stereo, that is the ability of occlusion detection, has been paid less attention. we intensively analyzed the occlusion in the camera matrix stereo (sea) and developed a simple but effective method to detect the presence of occlusion and to eliminate its effect in the correspondence search. by considering several statistics on the occlusion and the accuracy in the sea, we derived a few base masks which represent occlusion patterns and are effective for the detection of occlusion. several experiments using typical indoor scenes showed quite good performance to obtain dense and accurate depth maps even at the occluding boundaries of objects.
lines in one orthographic and two perspective views. we introduce a linear algorithm to recover the euclidean motion between an orthographic and two perspective cameras from straight line correspondences filling the gap in the analysis of motion estimation from line correspondences for various projection models. the general relationship between lines in three views is described by the trifocal tensor. euclidean structure from motion for three perspective views is a special case in which the relationship is defined by a collection of three matrices. here, we describe the case of two calibrated perspective views and an orthographic view. similar to the other cases, our linear algorithm requires 13 or more line correspondences to recover 27 coefficients of the trifocal tensor.
catadioptric omnidirectional camera. conventional video cameras have limited fields of view that make them restrictive in a variety of vision applications. there are several ways to enhance the field of view of an imaging system. however, the entire imaging system must have a single effective viewpoint to enable the generation of pure perspective images from a sensed image. a new camera with a hemispherical field of view is presented. two such cameras can be placed back-to-back, without violating the single viewpoint constraint, to arrive at a truly omnidirectional sensor. results are presented on the software generation of pure perspective images from an omnidirectional image, given any user-selected viewing direction and magnification. the paper concludes with a discussion on the spatial resolution of the proposed camera.
structure from linear or planar motions. we recently demonstrated a new approach to multiframe structure from motion from point features which, in the appropriate domain, provably reconstructs structure and motion correctly. the algorithm works for general motion and large perspective effects. in this paper, we describe how to adapt our approach to translational motion lying along a line or in a plane, with arbitrary rotations. an analysis of the bas--relief effect for multiple--motion sequences is also presented.
a new structure--from--motion ambiguity. this paper demonstrates the existence of a new, approximate, intrinsic ambiguity in euclidean structure from motion (sfm) which occurs as generically as the bas-relief ambiguity but, unlike it, strengthens for scenes with more depth variation. the ambiguity does not occur in projective sfm, but the reasons for this make projective reconstructions more likely to have large errors. our analysis gives a semiquantitative characterization of the least-squares error surface over a domain complementary to that analyzed by jepson, heeger, and maybank. as part of our analysis, we show that the least-squares error for infinitesimal motion¿the optical-flow error¿gives a good approximation to the least-squares error for moderate finite motions. we propose that many high-error local minima occur for epipoles in or near the image. we also establish the existence of a new local minimum in minimizing over the rotation, given the translation direction.
lafter: lips and face real-time tracker. this paper describes an active-camera real-time system for tracking, shape description, and classification of the human face and mouth using only an sgi indy computer. the system is based on use of 2-d blob features, which are spatially-compact clusters of pixels that are similar in terms of low-level image properties. patterns of behavior (e.g., facial expressions and head movements) can be classified in real-time using hidden markov model (hmm) methods. the system has been tested on hundreds of users and has demonstrated extremely reliable and accurate performance. typical classification accuracies are near 100 %
connectionist networks for feature indexing and object recognition. feature indexing techniques are promising for object recognition because of their ability to eliminate many feature set matches from consideration without much computation. this work exploits another property of such techniques. they have inherently parallel structure and connectionist network formulations are easy to develop. once indexing has been performed, a voting scheme such as geometric hashing [lamdan et al. 1990] can be used to generate object hypotheses in parallel. we give a framework for the connectionist implementation for such indexing and recognition techniques. with sufficient processing elements, recognition can be performed in a small number of time steps. the number of processing elements necessary to achieve peak performance and the fan-in/fan-out required for the processing elements is determined. these techniques have been simulated on a conventional architecture with good results.
affine invariant detection: edges, active contours, and segments. in this paper we undertake a systematic investigation of affine invariant object detection. edge detection is first presented from the point of view of the affine invariant scale-space obtained by curvature based motion of the image level-sets. in this case, affine invariant edges are obtained as a weighted difference of images at different scales. we then introduce the affine gradient as the simplest possible affine invariant differential function which has the same qualitative behavior as the euclidean gradient magnitude. these edge detectors are the basis both to extend the affine invariant scale-space to a complete affine flow for image denoising and simplification, and to define affine invariant active contours for object detection and edge integration. the active contours are obtained as a gradient flow in a conformally euclidean space defined by the image on which the object is to be detected. that is, we show that objects can be segmented in an affine invariant manner by computing a path of minimal weighted affine distance,the weight being given by functions of the affine edge detectors. the geodesic path is computed via an algorithm which allows to simultaneously detect any number of objects independently of the initial curve topology.
pedestrian detection using wavelet templates. this paper presents a trainable object detection architecture that is applied to detecting people in static images of cluttered scenes. this problem poses several challenges. people are highly non-rigid objects with a high degree of variability in size, shape, color, and texture. unlike previous approaches, this system learns from examples and does not rely on any a priori (handcrafted) models or on motion. the detection technique is based on the novel idea of the wavelet template that defines the shape of an object in terms of a subset of the wavelet coeficients of the image. it is invariant to changes in color and texture and can be used to robustly define a rich and complex class of objects such as people. we show how the invariant properties and computational eficiency of the wavelet template make it an effective tool for object detection.
training support vector machines: an application to face detection. we investigate the application of support vector machines (svms) in computer vision. svm is a learning technique developed by v. vapnik and his team (at\&t bell labs.) that can be seen as a new method for training polynomial, neural network, or radial basis functions classifiers. the decision surfaces are found by solving a linearly constrained quadratic programming problem. this optimization problem is challenging because the quadratic form is completely dense and the memory requirements grow with the square of the number of data points.we present a decomposition algorithm that guarantees global optimality, and can be used to train svm's over very large data sets. the main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of optimality conditions which are used both to generate improved iterative values, and also establish the stopping criteria for the algorithm.we present experimental results of our implementation of svm, and demonstrate the feasibility of our approach on a face detection problem that involves a data set of 50,000 data points.
image velocity estimation from trajectory surface in spatiotemporal space. a new framework and method, based on image motion trajectories in spatiotemporal space (x-y-t space), are proposed to estimate image velocity from an image sequence. we focus on the surfaces of the trajectories in the x-y-t space formed by the edges and contours of moving objects and obtain image velocity from the orientation of the intersection line formed by tangent planes on the trajectories. the proposed method includes two hough transforms to detect the most dominant orientation in all possible intersection lines and reliably produces the dominant translational image velocity semi-locally. also, the confidence measure of estimates is defined to decide the optimal size of patch that suppresses the aperture problem. experimental results from several synthetic and real image sequences are presented to verify the effectiveness of the method and to confirm its robustness against noise and occlusion.
non-parametric similarity measures for unsupervised texture segmentation and image retrieval. in this paper we propose and examine non-parametric statistical tests to define similarity and homogeneity measures for textures. the statistical tests are applied to the coefficients of images filtered by a multi-scale gabor filter bank. we will demonstrate that these similarity measures are useful for both, texture based image retrieval and for unsupervised texture segmentation, and hence offer an unified approach to these closely related tasks. we present results on brodatz-like micro-textures and a collection of real-word images.
object detection using hierarchical mrf and map estimation. this paper presents a new scale, position and orientation invariant approach to object detection. the proposed method first chooses attention regions in an image based on the region detection result on the image. within the attention regions, the method then detects targets using a novel object detection algorithm that combines template matching methods with feature-based methods via hierarchical mrf and map estimation. hierarchical mrf and map estimation provide a flexible framework to incorporate various visual clues. the combination of template matching and feature detection helps to achieve robustness against complex backgrounds and partial occlusions in object detection. experimental results are given in the paper.
a fast and flexible statistical method for text extraction in document pages. this paper describes a fast and flexible method for extracting text regions from a document page containing text, graphics, and pictures. such regions can be given as an input to an ocr system. the user fixes two parameters, the minimum width w of the text to be detected, and the precision \epsilon needed (both expressed as a percentage of the image width), according to the implementation needs. the method works by subdividing the page into overlapping columns whose width and inter-shift depend on w and \epsilon, and by performing text lines extraction on each column separately. successively, a statistical analysis of the text line elements found in each column is performed, and they are connected to form complete text lines. finally, related pieces of text are merged into blocks so that a sensible reading order is provided for the ocr system. the algorithm is very fast, is able to work on low-resolution document pages and is robust against skew. the algorithm is also very flexible: no assumptions are made on the layout of the document, the shape of the text regions, and the font size and style; the main assumption is that the background is uniform and the text approximately horizontal. despite the statistical nature of the method, a single line of text of a certain font size is generally sufficient to warrant detection. experimental results are shown which demonstrate the effectiveness of the method on several different kinds of documents.
uncalibrated 1d projective camera and 3d affine reconstruction of lines. we describe a linear algorithm to recover 3d affine shape/motion from line correspondences over three views with uncalibrated affine cameras. the key idea is the introduction of a one-dimensional projective camera. this converts the 3d affine reconstruction of "lines" into 2d projective reconstruction of "points". using the full tensorial representation of three uncalibrated 1d views, we prove that the 3d affine reconstruction of lines from minimal data is unique up to a re-ordering of the views. 3d affine line reconstruction can be performed by properly rescaling image coordinates instead of using projection matrices. the algorithm is validated on both simulated and real image sequences.
a factorization method for affine structure from line correspondences. a family of structure from motion algorithms called the factorization method has been recently developed from the orthographic projection model to the affine camera model. all these algorithms are limited to handling only point features of the image stream. we propose in this paper an algorithm for the recovery of shape and motion from line correspondences by the factorization method with the affine camera. instead of one step factorization for points, a multi-step factorization method is developed for lines based on the decomposition of the whole shape and motion into three separate substructures. each of these substructures can then be linearly solved by factorizing the appropriate measurement matrices. it is also established that affine shape and motion with uncalibrated affine cameras can be achieved with at least seven lines over three views, which extends the previous results of koenderink and van doorn for points to lines.
panoramic mosaics by manifold projection. as the field of view of a picture is much smaller than our own visual field of view, it is common to paste together several pictures to create a panoramic mosaic having a larger field of view. images with a wider field of view can be generated by using fish-eye lens, or panoramic mosaics can be created by special devices which rotate around the camera's optical center (quicktime vr, surround video), or by aligning, and pasting, frames in a video sequence to a single reference frame. existing mosaicing methods have strong limitations on imaging conditions, and distortions are common. manifold projection enables the creation of panoramic mosaics from video sequences under more general conditions, and in particular the unrestricted motion of a hand-held camera. the panoramic mosaic is a projection of the scene into a virtual manifold whose structure depends on the camera's motion. this manifold is more general than the customary projections onto a single image plane or onto a cylinder. in addition to being more general than traditional mosaics, manifold projection is also computationally efficient, as the only image deformations used are image plane translations and rotations. real-time, software only, implementation on a pentium-pc, proves the superior quality and speed of this approach.
closed-loop object recognition using reinforcement learning. current computer vision systems whose basic methodology is open-loop or filter type typically use image segmentation followed by object recognition algorithms. these systems are not robust for most real-world applications. in contrast, the system presented here achieves robust performance by using reinforcement learning to induce a mapping from input images to corresponding segmentation parameters. this is accomplished by using the confidence level of model matching as a reinforcement signal for a team of learning automata to search for segmentation parameters during training. the use of the recognition algorithm as part of the evaluation function for image segmentation gives rise to significant improvement of the system performance by automatic generation of recognition strategies. the system is verified through experiments on sequences of indoor and outdoor color images with varying external conditions.
randomness and geometric features in computer vision. it is often necessary to handle randomness and geometry in computer vision, for instance to match and fuse together noisy geometric features such as points, lines or 3d frames, or to estimate a geometric transformation from a set of matched features. however, the proper handling of these geometric features is far more difficult than for points, and a number of paradoxes can arise. we analyse in this article three basic problems: (1) what is a uniform random distribution of features, (2) how to define a distance between features, and (3) what is the ``mean feature'' of a number of feature measurements, and we propose generic methods to solve them.
orientation diffusions. diffusions provide a convenient way of smoothing noisy brightness images, of analyzing images at multiple scales, and of enhancing discontinuities. some quantities of interest in computer vision are defined on curved manifolds; typical examples are orientation and hue that are defined on the circle. generalizing diffusions to orientation is not straightforward, especially in the case where a discrete implementation is sought. an example of what may go wrong is presented. a method is proposed to define diffusions of orientation-like quantities. first a definition in the continuum is discussed, then a discrete orientation diffusion is proposed. the behavior of such diffusions is explored both analytically and experimentally. it is shown how such orientation diffusions contain a nonlinearity that is reminiscent of edge-process and anisotropic diffusion. a number of open questions are proposed at the end.
optimal selection of camera parameters for recovery of depth from defocused images. in the depth from defocus (dfd) method, two defocused images of a scene are obtained by capturing the scene with different sets of camera parameters. an arbitrary selection of the camera settings can result in observed images whose relative blurring is insufficient to yield a good estimate of the depth. in this paper, we study the effect of the degree of relative blurring on the accuracy of the estimate of the depth by addressing the dfd problem in a maximum likelihood-based framework. we propose a criterion for optimal selection of camera parameters to obtain an improved estimate of the depth. the optimality criterion is based on the cramer-rao bound of the variance of the error in the estimate of blur. simulations as well as experimental results on real images are presented for validation.
the feret evaluation methodology for face-recognition algorithms. two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. the face recognition technology (feret) program has addressed both issues through the feret database of facial images and the establishment of the feret tests. to date, 14,126 images from 1199 individuals are included in the feret database, which is divided into development and sequestered portions. in september 1996, the feret program administered the third in a series of feret face-recognition tests. the primary objectives of the third test were to (1) assess the state of the art, (2) identify future areas of research, and (3) measure algorithm performance on large databases.
a direct method for stereo correspondence based on singular value decomposition. this paper proposes a new algorithm for matching point features across pairs of images. despite the well-known combinatorial complexity of the problem, this work shows that an acceptably good solution can be obtained directly by singular value decomposition of an appropriate correspondence strength matrix. the approach draws from the method proposed previously but, besides suggesting its usefulness for stereo matching, in this work a correlation-weighted proximity function is used as correspondence strength to specifically cater for real images.
a stratified approach to metric self-calibration. camera calibration is essential to many computer vision applications. in practice this often requires cumbersome calibration procedures to be carried out regularly. in the last few years a lot of work has been done on self-calibration of cameras, ranging from weak calibration to metric calibration. it has been shown that a metric calibration of the camera setup (up to scale) was possible based on the rigidity of the scene only. in this paper a stratified approach is proposed which gradually retrieves the metric calibration of the camera setup. starting from an uncalibrated image sequence the projective calibration is retrieved first. in projective space the plane at infinity is then identified yielding the affine calibration. this is achieved using a constraint which can be formulated between any two arbitrary images of the sequence. once the affine calibration is known the upgrade to metric is easily obtained through linear equations.