waashk/dblp huggingface dataset:
reconstruction of 3d-curves from 2d-images using affine shape methods for curves. in this paper, we propose an algorithm for doing reconstruction of general sd-curves from a number of 2d-images taken by uncalibrated cameras. no point correspondences between the images are assumed. the curve and the view points are uniquely reconstructed, module common projective transformations and the point correspondence problem is solved. furthermore, the algorithm is independent of the choice of coordinates, as it is based on orthogonal projections and aligning subspaces. the algorithm is based on an extension of afine shape of finite point configurations to more general objects.
recursive structure and motion from image sequences using shape and depth spaces. in this paper a novel recursive method for estimating structure and motion from image sequences is presented. the novelty lies in the fact that the output of the algorithm is independent of the chosen coordinate systems in the images as well as the ordering of the points. it relies on subspace methods and is derived from both ordinary coordinate representations and camera matrices and from a so called depth and shape analysis. furthermore, no initial phase is needed to start up the algorithm. it starts directly with the first two images and incorporates new images as soon as new corresponding points are obtained. the performance of the algorithm is shown on simulated data.moreover, the two different approaches, one using camera matrices and the other using the concepts of affine shape and depth, are unified into a general theory of structure and motion from image sequences.
calibration of a foveated wide-angle lens on an active vision head. inspired by the properties of the humam visual system, a new active vision system called escher (etl stereo compact head for robot vision) has been recently implemented with foveated wide-angle lenses. the lenses exhibit a wide field of view along with a space-varying resolution for facilitating both detection and close observation. however, to handle such optical properties and achieve basic eye movement functions, new calibration methods are needed. therefore, two novel and online techniques are presented that in one case perform a global identification of the optical process through artificial neural techniques and in the other case compute the physical parameters by using environmental feature-tracking and controlled rotations of the cameras. self-alignment of the cameras is also achieved using a similar technique.
feature correspondence by interleaving shape and texture computations. the correspondence problem in computer vision is basically a matching task between two or more sets of features. in this paper, we introduce a vectorized image representation, which is a feature-based representation where correspondence has been established with respect to a reference image. the representation consists of two image measurements made at the feature points: shape and texture. feature geometry, or shape, is represented using the (x, y) locations of features relative to the some standard reference shape. image grey levels, or texture, are represented by mapping image grey levels onto the standard reference shape. computing this representation is essentially a correspondence task, and in this paper we explore an automatic technique for "vectorizing" face images. our face vectorizer alternates back and forth between computation steps for shape and texture, and a key idea is to structure the two computations so that each one uses the output of the other. in addition to describing the vectorizer, an application to the problem of facial feature detection will be presented.
ordinal measures for visual correspondence. we present ordinal measures for establishing image correspondence. linear correspondence measures like correlation and the sum of squared differences are known to be fragile. ordinal measures, which are based on relative ordering of intensity values in windows, have demonstrable robustness to depth discontinuities, occlusion and noise. the relative ordering of intensity values in each window is represented by a rank permutation which is obtained by sorting the corresponding intensity data. by using a novel distance metric between the rank permutations, we arrive at ordinal correlation coefficients. these coefficients are independent of absolute intensity scale, i.e they are normalized measures. further, since rank permutations are invariant to monotone transformations of the intensity values, the coefficients are unaffected by nonlinear effects like gamma variation between images. we have developed a simple algorithm for their efficient implementation. experiments suggest the superiority of ordinal measures over existing techniques under non-ideal conditions. though we present ordinal measures in the context of stereo, they serve as a general tool for image matching that is applicable to other vision problems such as motion estimation and image registration.
motion estimation using ordinal measures. we present a method for motion estimation using ordinal measures. ordinal measures are based on relative ordering of intensity values in an image region called rank permutation. while popular measures like the sum-of-squared-difference (ssd) and normalized correlation (ncc) rely on linearity between corresponding intensity values, ordinal measures only require them to be monotonically related so that rank permutations between corresponding regions are presented. this property turns out to be useful for motion estimation in tagged magnetic resonance images. we study the imaging equation involved in two methods of tagging and observe temporal monotonicity in intensity under certain conditions though the tags themselves fade. we compare our method to ssd and ncc in a rotating ring phantom image sequence. we present an experiment on a real heart image sequence which suggests the suitability of our method.
deterioration detection for digital film restoration. this paper presents a robust technique to detect local deteriorations of old cinematographic films. this method relies on spatio-temporal information and combines two different detectors: a morphological detector which uses spatial properties of deteriorations, and a dynamic detector based on motion estimation techniques. our deterioration detector has been validated on several film sequences and turned out to be a powerful tool for digital film restoration.
recognition of planar object classes. we present a new framework for recognizing planar object classes, which is based on local feature detectors and a probabilistic model of the spatial arrangement of the features. the allowed object deformations are represented through shape statistics, which are learned from examples. instances of an object in an image are detected by finding the appropriate features in the correct spatial configuration. the algorithm is robust with respect to partial occlusion, detector false alarms, and missed features. a 94\% success rate was achieved for the problem of locating quasi-frontal views of faces in cluttered scenes.
on a spectral attentional mechanism. this paper describes an attentional mechanism based on the interpretation of spectral signatures for detecting regular object configurations in areas of an image delineated using context information. the proposed global operator relies on the spectral analyse's of edge structure and exploits spatial as well as frequency domain constraints derived from known geometrical models of monitored objects. a decision theoretic method for learning decision regions is presented. applications of this mechanism are demonstrated for several aerial image interpretation tasks. specific examples are described for detecting vehicle formations (such as convoys), qualifying the geometry of detected formations, or monitoring the occupancy of regions of interest (such as parking areas, roads, or open areas). experiments and sensitivity analysis results are reported.
recognition via consensus of local moments of brightness and orientation. this study combines two useful methods in recognition: consensus or voting-based approaches and moment-based representations. matches between image patches are generated using a gaussian-weighted moment encoding of the patches and a feature indexing process. each match implies an object 3d position and orientation (pose) and generates a vote for this pose. recognition is accomplished by detecting significant clusters of votes in pose space. this combined method is an improvement over voting and moment methods in isolation. using image brightness moments, the idea is successfully demonstrated on examples of human faces undergoing full 3d pose change, as well as changes in features such as talking and blinking. the idea is then extended to moments of local texture orientation and successfully demonstrated under large variations in lighting nature and geometry.
learning parameterized models of image motion. a framework for learning parameterized models of optical flow from image sequences is presented. a class of motions is represented by a set of orthogonal basis flow fields that are computed from a training set using principal component analysis. many complex image motions can be represented by a linear combination of a small number of these basis flows. the learned motion models may be used for optical flow estimation and for model-based recognition. for optical flow estimation we describe a robust, multi-resolution scheme for directly computing the parameters of the learned flow models from image derivatives. as examples we consider learning motion discontinuities, non-rigid motion of human mouths, and articulated human motion.
autonomous recognition: driven by ambiguit. recognition ambiguity, due to noisy measurements and uncertain object models, can be quantified and actively used by an autonomous agent to efficiently gather new data and improve its information about the environment. in this work an information-based utility measure is used to derive from a learned classification of shape models an efficient data collection strategy, specifically aimed at increasing classification confidence when recognizing uncertain shapes. promising simulation results are presented and discussed.
controlling view-based algorithms using approximate world models and action information. most view-based vision algorithms are based on strong assumptions about the disposition of the objects in the image. to safely apply those algorithms in real world image sequences, we propose that a vision system should be divided into two components. the first component contains an approximate world model of the scene-a low accuracy, coarse description of the objects and actions in the world. approximate world models are constructed and updated by simple vision routines and by the use of action information provided by an external source. the second component employs view-based algorithms to perform required perceptual tasks; the selection and control of the view-based methods are determined by the information provided by the approximate world model. we demonstrate the approximate world model approach in a project to control cameras in a tv studio where the external context is provided by a script.
lie groups, space-variant fourier analysis and the exponential chirp transform. the use of visual representations in which retinal neurons receptive fields are not constant over the visual field is universal in the visual systems of higher vertebrates, and is coming to play an important role in active vision applications. the breaking of translation symmetry that is unavoidably associated with non-uniform sampling presents a major algorithmic complication for image processing. in this paper we use a lie group approach to derive a kernel which provides a quasi-shift (i.e. approximate shift) invariant template matching capability, under normal convolution in the distorted (range) coor- dinates of the non-uniform mapping. we work out the special case of the log- polar mapping, which is of great interest in vision; in this case, we call the associated linear integral transform the ``exponential chirp transform'' (ect). the method is, however, general for other forms of mapping, or warp, function.
experimental performance evaluation of feature grouping modules. we present five performance measures to evaluate grouping modules in the context of constrained search and indexing based object recognition. using these measures, we demonstrate a sound experimental framework based on statistical anova tests to compare and contrast three edge based organization modules, namely those of etemadi et al., jacobs, and sarkar-boyer in the domain of aerial objects using 50 images. with adapted parameters, the jacobs module is overall the best choice for constraint based recognition. for fixed parameters, the sarkar-boyer module is the best in terms of recognition accuracy and indexing speedup. etemadi et al.'s module performs equally well with fixed and adapted parameters while the jacobs module is most sensitive to fixed and adapted parameter choices. the overall performance ranking of the modules is jacobs, sarkar-boyer, and etemadi et al..
global training of document processing systems using graph transformer networks. we propose a new machine learning paradigm called graph transformer networks that extends the applicability of gradient-based learning algorithms to systems composed of modules that take graphs as inputs and produce graphs as output. training is performed by computing gradients of a global objective function with respect to all the parameters in the system using a kind of back-propagation procedure.a complete check reading system based on these concepts is described. the system uses convolutional neural network character recognizers, combined with global training techniques to provide record accuracy on business and personal checks. it is presently deployed commercially and reads million of checks per month.
corner detection with covariance propagation. this paper presents a statistical approach for detecting corners from chain encoded digital arcs. an arc point is declared as a corner if the estimated parameters of the two fitted lines of the two arc segments immediately to the right and left of the arc point are statistically significantly different. the corner detection algorithm consists of two steps: corner detection and optimization. while corner detection involves statistically identifying the most likely corner points along an arc sequence, corner optimization deals with improving the locational errors of the detected corners.the major contributions of this research include developing a method for analytically estimating the covariance matrix of the fitted line parameters and developing a hypothesis test statistic to statistically test the difference between the parameters of two fitted lines. this paper discusses the theory and performance characterization of the proposed corner detector.
a hierarchical approach to high resolution edge contour reconstruction. efficient edge detection algorithms such as canny's (1986) fail near curve singularities. moreover, the standard linking algorithms used on top of these detectors often fail because of instabilities in the tracking process (due to multiple responses to the same edge and interference of nearby edges). we propose a hierarchical approach to edge detection based on a graph stabilization method that allows bifurcation resolution in stages. curve singularities are recovered at the last stage by using "top-down" feedback to select the best curve connections.
head tracking via robust registration in texture map images. abstract a novel method for 3d head tracking in the presence of large head rotations and facial expression changes is described. tracking is formulated in terms of color image registration in the texture map of a 3d surface model. model appearance is recursively updated via image mosaicking in the texture map as the head orientation varies. the resulting dynamic texture map provides a stabilized view of the face that can be used as input to many existing 2d techniques for face recognition, facial expressions analysis, lip reading, and eye tracking. parameters are estimated via a robust minimization procedure; this provides robustness to occlusions, wrinkles, shadows, and specular highlights. the system was tested on a variety of sequences taken with low quality, uncalibrated video cameras. experimental results are reported.
a new bayesian framework for object recognition. we describe a new approach to feature-based object recognition, using maximum a posteriori (map) estimation under a markov random field (mrf) model. the main advantage of this approach is that it allows explicit modeling of dependencies between individual features of an object. for instance, we use the approach to model the fact that mismatched features due to partial occlusions tend to form spatially coherent groups rather than being independent. efficient computation of the map estimate in our framework can be accomplished by finding a minimum cut on an appropriately defined graph. an even more efficient approximation, that does not use graph cuts, is also presented. this approximation technique, which we call spatially coherent matching (scm), is closely related to generalized hausdorff matching. we report some monte carlo experiments showing that the scm technique improves substantially on the tradeoff between correct detection and false alarms compared with previous feature matching methods such as the hausdorff distance.
fast, reliable head tracking under varying illumination. abstract an improved technique for 3d head tracking under varying illumination conditions is proposed. the head is modeled as a texture mapped cylinder. tracking is formulated as an image registration problem in the cylinder''s texture map image. to solve the registration problem in the presence of lighting variation and head motion, the residual error of registration is modeled as a linear combination of texture warping templates and orthogonal illumination templates. fast and stable on-line tracking is then achieved via regularized, weighted least squares minimization of the registration error. the regularization term tends to limit potential ambiguities that arise in the warping and illumination templates. it enables stable tracking over extended sequences. tracking does not require a precise initial fit of the model; the system is initialized automatically using a simple 2-d face detector. the only assumption is that the target is facing the camera in the first frame of the sequence. the warping templates are computed at the first frame of the sequence. illumination templates are precomputed off-line over a training set of face images collected under varying lighting conditions. experiments in tracking are reported.
disparity component matching for visual correspondence. we present a method for computing dense visual correspondence based on general assumptions about scene geometry. our algorithm does not rely on correlation, and uses a variable region of support. we assume that images consist of a number of connected sets of pixels with the same disparity, which we call {\em disparity components}. at each pixel we compute a small set of plausible disparities, each of which is more likely to be the pixel's true disparity than not. a pixel is assigned a disparity $d$ based on connected components of pixels, where each pixel in a component considers $d$ to be plausible. our implementation chooses the largest plausible disparity component; however, global contextual constraints can also be applied. while the algorithm was originally designed for visual correspondence, it can also be used for other early vision problems such as image restoration. it runs in a few seconds on traditional benchmark images with standard parameter settings, and gives quite promising results.
markov random fields with efficient approximations. markov random fields (mrf''s) can be used for a wide variety of vision problems. in this paper we address the estimation of first-order mrf''s with a particular clique potential that resembles a well. we show that the maximum {\em a posteriori} estimate of such an mrf can be obtained by solving a multiway cut problem on a graph. this allows the application of near linear-time algorithms for computing provably good approximations. we formulate the visual correspondence problem as an mrf in our framework, and show that this yields quite promising results on real data with ground truth.
model-based multi-objective analysis of ultrasound image sequences in prenatal diagnosis. in this study, ultrasound image sequences of fetus head are examined for the bi-parietal diameter measurement. there are five parts of the fetus head that have to be seen clearly before such a measurement can be made. our approach is based on model-based multi-objective analysis. for each part, an objective function encoding both data and anatomic constraints is formulated. the resulting multi-objective problem is then transformed into a problem of coupled differential equations and is then solved using numerical integration. throughout the process, continuity principle is used between the frames so that the results of analysis on one frame can be used on the next frame-whenever possible.
stereo coupled active contours. we consider how tracking in stereo may be enhanced by coupling pairs of active contours in different views via affine epipolar geometry and various subsets of planar affine transformations, as well as by implementing temporal constraints imposed by curve rigidity. 3d curve tracking is achieved using a submanifold model, where it is shown how the coupling mechanisms can be decomposed to cater for fixed and variable epipolar geometries. in the case of tracking planar curves, the canonical frame model is developed such that the various geometrical constraints needed in different situations may be efficiently selected. the results show that coupled active contours add consistency and robustness to tracking in stereo.
coupled hidden markov models for complex action recognition. we present algorithms for coupling and training hidden markov models (hmms) to model interacting processes, and demonstrate their superiority to conventional hmms in a vision task classifying two-handed actions. hmms are perhaps the most successful framework in perceptual computing for modeling and classifying dynamic behaviors, popular because they offer dynamic time warping, a training algorithm and a clear bayesian semantics. however the markovian framework makes strong restrictive assumptions about the system generating the signal-that it is a single process having a small number of states and an extremely limited state memory. the single-process model is often inappropriate for vision (and speech) applications, resulting in low ceilings on model performance. coupled hmms provide an efficient way to resolve many of these problems, and offer superior training speeds, model likelihoods, and robustness to initial conditions.
learning and recognizing human dynamics in video sequences. this paper describes a probabilistic decomposition of human dynamics at multiple abstractions, and shows how to propagate hypotheses across space, time, and abstraction levels. recognition in this framework is the succession of very general low level grouping mechanisms to increased specific and learned model based grouping techniques at higher levels. hard decision thresholds are delayed and resolved by higher level statistical models and temporal context. low-level primitives are areas of coherent motion found by em clustering, mid-level categories are simple movements represented by dynamical systems, and high-level complex gestures are represented by hidden markov models as successive phases of simple movements. we show how such a representation can be learned from training data, and apply it to the example of human gait recognition.