waashk/dblp huggingface dataset:
detection, analysis and matching of hair. we develop computational models for measuring hair appearance for comparing different people. the models and methods developed have applications to person recognition and face image indexing. an automatic hair detection algorithm is described and results reported. a multidimensional representation of hair appearance is presented and computational algorithms are described. results on a dataset of 524 subjects are reported. identification of people using hair attributes is compared to eigenface-based recognition along with a joint, eigenface-hair based identification.
a background layer model for object tracking through occlusion. motion layer estimation has recently emerged as apromising object tracking method. in this paper, we extendprevious research on layer-based tracker by introducingthe concept of background occluding layers and explicitlyinferring depth ordering of foreground layers. thebackground occluding layers lie in front of, behind, and inbetween foreground layers. each pixel in the backgroundregions belongs to one of these layers and occludes all theforeground layers behind it. together with the foregroundordering, the complete information necessary for reliablytracking objects through occlusion is included in ourrepresentation. an map estimation framework isdeveloped to simultaneously update the motion layerparameters, the ordering parameters, and the backgroundoccluding layers. experimental results show that undervarious conditions with occlusion, including situationswith moving objects undergoing complex motions orhaving complex interactions, our tracking algorithm isable to handle many difficult tracking tasks reliably.
learned temporal models of image motion. an approach for learning and estimating temporal-flow models from image sequences is proposed. the temporal-flow models are represented as a set of orthogonal temporal-flow bases that are learned using principal component analysis of instantaneous flow measurements. spatial constraints on the temporal-flow are also developed for modeling the motion of regions in rigid and coordinated motion. the performance of these models is demonstrated on several long image sequences of rigid and articulated bodies in motion.
learning a sparse, corner-based representation for time-varying background modeling. time-varying phenomenon, such as ripples on water, trees waving in the wind and illumination changes, produces false motions, which significantly compromises the performance of an outdoor-surveillance system. in this paper, we propose a corner-based background model to effectively detect moving-objects in challenging dynamic scenes. specifically, the method follows a three-step process. first, we detect feature points using a harris corner detector and represent them as sift-like descriptors. second, we dynamically learn a background model and classify each extracted feature as either a background or a foreground feature. last, a "lucas-kanade" feature tracker is integrated into this framework to differentiate motion-consistent foreground objects from background objects with random or repetitive motion. the key insight of our work is that a collection of sift-like features can effectively represent the environment and account for variations caused by natural effects with dynamic movements. features that do not correspond to the background must therefore correspond to foreground moving objects. our method is computational efficient and works in real-time. experiments on challenging video clips demonstrate that the proposed method achieves a higher accuracy in detecting the foreground objects than the existing methods.
region competition: unifying snakes, region growing, energy/bayes/mdl for multi-band image segmentation. we present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. this algorithm is derived by minimizing a generalized bayes/mdl (minimum description length) criterion using the variational principle. we show that existing techniques in early vision such as, snake/balloon models, region growing, and bayes/mdl are addressing different aspects of the same problem and they can be unified within a common statistical framework which combines their advantages. we analyze how to optimize the precision of the resulting boundary location by studying the statistical properties of the region competition algorithm and discuss what are good initial conditions for the algorithm. our method is generalized to color and texture segmentation and is demonstrated on grey level images, color images and texture images.
free-form surface registration using surface signatures. this paper introduces a new free-form surface representation scheme for the purpose of fast and accurate registration and matching. accurate registration of surfaces is a common task in computer vision. the proposed representation scheme captures the surface curvature information, seen from certain points and produces images, called surface signatures, at these points. matching signatures of different surfaces enables the recovery of the transformation parameters between these surfaces. we propose to use template matching to compare the signature images. to enable partial matching, another criterion, the overlap ratio, is used. this representation scheme can be used as a global representation of the surface as well as a local one and performs near real-time registration. we show that the signature representation can be used to match objects in 3-d scenes in the presence of clutter and occlusion. applications presented include free-form object matching, multimodal medical volumes registration and dental teeth reconstruction from intra-oral images.
grade: gibbs reaction and diffusion equation. recently there have been increasing interest in using nonlinear pdes for applications in computer vision and image processing, in this paper, we propose a general statistical framework for designing a new class of pdes. for a given applications, a markov random field model p(i) is learned according to the minimax entropy principle studied in [25] [26], so that p(i) should characterize the ensemble of images in our application. p(i) is a gibbs distribution whose energy terms can be divided into two categories. subsequently the partial differential equations given by gradient descent on the gibbs potential are essentially reaction-diffusion equations, where the energy terms in one category produce anisotropic diffusion while the inverted energy terms in the second category produce reaction associated with pattern formation. we call this new class of pdes the gibbs reaction and diffusion equations ¿ grade and we demonstrate experiments where grade are used for texture pattern formation, denoising, image enhancements, and clutter removal.
ranking prior likelihood distributions for bayesian shape localization framework. in this paper, we formulate the shape localization problem in the bayesian framework. in the learning stage, we propose the constrained rank boost approach to model the likelihood of local features associated with the keypoints of an object, like face, while preserve the prior ranking order between the ground truth position of a keypoint and its neighbors; in the inferring stage, a simple efficient iterative algorithm is proposed to uncover the map shape by locally modeling the likelihood distribution around each key point via our proposed variational locally weighted learning (vlwl) method. our proposed framework has the following benefits: 1) compared to the classical pca models, the likelihood presented by the ranking prior likelihood model has more discriminating power as to the optimal position and its neighbors, especially in the problem with ambiguity between the optimal positions and their neighbors; 2) the vlwl method guarantees that the posterior probability of the derived shape increases monotonously; and 3) the above two methods are both based on accurate probability formulation, which spontaneously leads to a robust confidence measure for the discovered shape. moreover, we present a theoretical analysis for the convergence of the constrained rank-boost. extensive experiments compared with the active shape models demonstrate the accuracy, robustness, and stability of our proposed framework.
automatically labeling video data using multi-class active learning. labeling video data is an essential prerequisite for many vision applications that depend on training data, such as visual information retrieval, object recognition, and human activity modeling. however, manually creating labels is not only time-consuming but also subject to human errors, and eventually, becomes impossible for a very large amount of data (e.g. 24/7 surveillance video). to minimize the human effort in labeling, we propose a unified multi-class active learning approach for automatically labeling video data. the contributions of this paper include extending active learning from binary classes to multiple classes and evaluating several practical sample selection strategies. the experimental results show that the proposed approach works effectively even with a significantly reduced amount of labeled data. the best sample selection strategy can achieve more than a 50% error reduction over random sample selection.
forms: a flexible object recognition and modelling system. we briefly describe a generic statistical framework for representing the shapes of animate objects using principal component analysis and stochastic shape grammars. such a representation scheme gives a formalism for solving the inverse problem-object recognition. then we show: how these representations can be extracted from 2d silhouettes by a novel method for skeleton extraction and shape segmentation; how a similarity metric can be defined on this shape space; and how we can perform recognition in a bottom up/top down loop. the system is demonstrated to be stable in the presence of noise, the absence of parts, the presence of additional parts, and considerable variations in articulation and viewpoint. successful categorization is demonstrated on a dataset of seventeen categories of animate objects.
fast multiple object tracking via a hierarchical particle filter. a very efficient and robust visual object tracking algorithm based on the particle filter is presented. the method characterizes the tracked objects using color and edge orientation histogram features. while the use of more features and samples can improve the robustness, the computational load required by the particle filter increases. to accelerate the algorithm while retaining robustness we adopt several enhancements in the algorithm. the first is the use of integral images [34] for efficiently computing the color features and edge orientation histograms, which allows a large amount of particles and a better description of the targets. next, the observation likelihood based on multiple features is computed in a coarse-to-fine manner, which allows the computation to quickly focus on the more promising regions. quasi-random sampling of the particles allows the filter to achieve a higher convergence rate. the resulting tracking algorithm maintains multiple hypotheses and offers robustness against clutter or short period occlusions. experimental results demonstrate the efficiency and effectiveness of the algorithm for single and multiple object tracking.
binocular helmholtz stereopsis. helmholtz stereopsis has been introduced recently as a surfacereconstruction technique that does not assume a modelof surface reflectance. in the reported formulation, correspondencewas established using a rank constraint, necessitatingat least three viewpoints and three pairs of images.here, it is revealed that the fundamental helmholtz stereopsisconstraint defines a nonlinear partial differential equation,which can be solved using only two images. it is shownthat, unlike conventional stereo, binocular helmholtz stereopsisis able to establish correspondence (and thereby recoversurface depth) for objects having an arbitrary andunknown brdf and in textureless regions (i.e., regions ofconstant or slowly varying brdf). an implementation andexperimental results validate the method for specular surfaceswith and without texture.
improved fast gauss transform and efficient kernel density estimation. evaluating sums of multivariate gaussians is a common computational task in computer vision and pattern recognition, including in the general and powerful kernel density estimation technique. the quadratic computational complexity of the summation is a significant barrier to the scalability of this algorithm to practical applications. the fast gauss transform (fgt) has successfully accelerated the kernel density estimation to linear running time for low-dimensional problems. unfortunately, the cost of a direct extension of the fgt to higher-dimensional problems grows exponentially with dimension, making it impractical for dimensions above 3. we develop an improved fast gauss transform to efficiently estimate sums of gaussians in higher dimensions, where a new multivariate expansion scheme and an adaptive space subdivision technique dramatically improve the performance. the improved fgt has been applied to the mean shift algorithm achieving linear computational complexity. experimental results demonstrate the efficiency and effectiveness of our algorithm.
passive depth from defocus using a spatial domain approach. this paper presents an algorithm for a dense computation of the difference in blur between two images. the two images are acquired by varying the intrinsic parameters of the camera.the image formation system is assumed to be passive. estimation of depth from the blur difference is straigh-forward. the algorithm is based on a local image decomposition technique using the hermite polynomial basis. we show that any coefficient of the hermite polynomial computed using the more blurred image is a function of the partial derivatives of the other image and the blur difference. hence, the blur difference can be computed by resolving a system of equations. all computations required are local and carried out in the spatial domain.an algorithm is presented for estimation of the blur in 1d and 2d cases and its behavior is studied for constant images, step edges, line edges and junctions. the algorithm is tested using synthetic and real images. the results obtained are very encouraging. the acm portal is published by the association for computing machinery. copyright © 2010 acm, inc. terms of usage privacy policy code of ethics contact us useful downloads: adobe acrobat quicktime windows media player real player
hilbert functions and applications to the estimation of subspace arrangements. this paper develops a new mathematical framework for studying the subspace-segmentation problem. we examine some important algebraic properties of subspace arrangements that are closely related to the subspace-segmentation problem. more specifically, we introduce an important class of invariants given by the hilbert functions. we show that there exist rich relations between subspace arrangements and their corresponding hilbert functions. we propose a new subspace-segmentation algorithm, and showcase two applications to demonstrate how the new theoretical revelation may solve subspace segmentation and model selection problems under less restrictive conditions with improved results.
class-based grouping in perspective images. in any object recognition system a major and primary task is to associate those image features, within an image of a complex scene, that arise from an individual object. the key idea here is that a geometric class defined in 3d induces relationships in the image which must hold between points on the image outline (the perspective projection of the object). the resulting image constraints enable both identification and grouping of image features belonging to objects of that class. the classes include surfaces of revolution, canal surfaces (pipes) and polyhedra. recognition proceeds by first recognising an object as belonging to one of the classes (for example a surface of revolution) and subsequently identifying the object (for example as a particular vase). this differs from conventional object recognition systems where recognition is generally targetted at particular objects. these classes also support the computation of 3d invariant descriptions including symmetry axes, canonical coordinate frames and projective signatures. the constraints and grouping methods are viewpoint invariant, and proceed with no information on object pose. we demonstrate the effectiveness of this class-based grouping on real, cluttered scenes using grouping algorithms developed for rotationally symmetric surfaces, canal-surfaces and polyhedra.
counting people in crowds with a real-time network of simple image sensors. estimating the number of people in a crowded environment is a central task in civilian surveillance. most vision-based counting techniques depend on detecting individuals in order to count, an unrealistic proposition in crowded settings. we propose an alternative approach that directly estimates the number of people. in our system, groups of image sensors segment foreground objects from the background, aggregate the resulting silhouettes over a network, and compute a planar projection of the scene's visual hull. we introduce a geometric algorithm that calculates bounds on the number of persons in each region of the projection, after phantom regions have been eliminated. the computational requirements scale well with the number of sensors and the number of people, and only limited amounts of data are transmitted over the network. because of these properties, our system runs in real-time and can be deployed as an untethered wireless sensor network. we describe the major components of our system, and report preliminary experiments with our first prototype implementation.
consistent segmentation for optical flow estimation. in this paper, we propose a method for jointly computing optical flow and segmentating video while accounting for mixed pixels (matting). our method is based on statistical modeling of an image pair using constraints on appearance and motion. segments are viewed as overlapping regions with fractional (á) contributions. bidirectional motion is estimated based on spatial coherence and similarity of segment colors. our model is extended to video by chaining the pairwise models to produce a joint probability distribution to be maximized. to make the problem more tractable, we factorize the posterior distribution and iteratively minimize its parts. we demonstrate our method on frame interpolation.
schwarz representation for matching and similarity analysis. this paper presents a novel multiscale representation of one-dimensional signal based on complex analysis. we show that a signal and its derivative at different scales can be represented by one analytic function defined on the unit disc in the complex plane, which is called the schwarz representation of a signal. this representation is applied to the matching problem. using the theory of analytic functions, we are able to define the inverse of a signal. the matching function between two signals can be defined as the composition of one signal's schwarz representation and another signal's inverse. the matching function determined bythis method has a group structure and is close-formed.
dealing with textureless regions and specular highlights - a progressive space carving scheme using a novel photo-consistency measure. we present two extensions to the space carving framework. the first is a progressive scheme to better reconstruct surfaces lacking sufficient textures. the second is a novel photo-consistency measure that is valid for both specular and diffuse surfaces, under unknown lighting conditions.
exact eye contact with virtual humans. this paper describes a simple yet effective method for achieving accurate, believable eye contact between humans and computer-generated characters, which to the author's knowledge is demonstrated here for the first time. a prototype system provides a high-fidelity stereoscopic head-tracked virtual environment, within which the user can engage in eye contact with a near-photorealistic virtual human model. the system does not require eye tracking. the paper describes design and implementation details, and reports qualitative positive feedback from initial testers.
geometric segmentation of perspective images based on symmetry groups. symmetry is an effective geometric cue to facilitate conventionalsegmentation techniques on images of man-madeenvironment. based on three fundamental principles thatsummarize the relations between symmetry and perspectiveimaging, namely, structure from symmetry, symmetry hypothesistesting, and global symmetry testing, we developa prototype system which is able to automatically segmentsymmetric objects in space from single 2-d perspective images.the result of such a segmentation is a hierarchy ofgeometric primitives, called symmetry cells and complexes,whose 3-d structure and pose are fully recovered. such ageometrically meaningful segmentation may greatly facilitateapplications such as feature matching and robot navigation.
vision-based projected tabletop interface for finger interactions. we designed and implemented a vision-based projected table-top interface for finger interaction. the system offers a simple and quick setup and economic design. the projection onto the tabletop provides more comfortable and direct viewing for users, and more natural, intuitive yet flexible interaction than classical or tangible interfaces. homography calibration techniques are used to provide geometrically compensated projections on the tabletop. a robust finger tracking algorithm is proposed to enable accurate and efficient interactions using this interface. two applications have been implemented based on this interface.
learning the probability of correspondences without ground truth. we present a quality assessment procedure for correspondence estimation based on geometric coherence rather than ground truth. the procedure can be used for performance evaluation of correspondence extraction schemes developed by researchers, as well as for online learning and adaptation aimed at better system performance. a very important aspect of the proposed procedure is that it considers uncertainty in the correspondence extraction, and encourages the evaluated methods to deal correctly with uncertainty. other important strengths of the procedure are that it does not use any manual work, and that it does not put any strong constraints on the scene, but rather relies on geometric coherence in the motion. thanks to these strengths, it can therefore be used with large amounts of real, potentially application specific data, or even data acquired during system operation. in the evaluation the correspondence extractor is handled as a black box producing a probability distribution for the local motion vector between a pair of image patches. the procedure is therefore quite general. we are making the evaluation procedure available for public use.