waashk/dblp huggingface dataset:
a curvature-based approach to contour motion estimation. we present a novel method of velocity field estimation for points on moving contours in an image sequence. the method determines the corresponding point in the next image frame by considering curvature changes at each point on a contour. in previous methods, there are errors in estimation for the points which have low curvature variations since those methods compute the solutions by approximatingthe normal component of optical flow. the proposed method computes optical flow vectors of contour points by minimizing the curvature changes. as a first step, snakes are used to locate smooth curves in 2d imagery. then, the extracted curves are tracked continuously. we excluded the rearranging process in snakes and allowed the snaxel distance to vary. each point on a contour has a unique corresponding point in thenext frame. experimental results showed that the proposed method computes accurate optical flow vectors for various moving contours.
volumetric deformable models with parameter functions: a new approach to the 3d motion analysis of the lv from mri-spamm. we present a new method for analyzing the 3d motion of the heart's left ventricle (lv) from tagged magnetic resonance imaging (mri) data. our technique is based on the development of a new class of volumetric physics-based deformable models whose parameters are functions and can capture the local shape variation of an object. these parameters require no complex post-processing in order to be used by a physician. these volumetric models allow the accurate estimation of the shape and motion of the inner and outer walls of the lv as well as within the walls. we also present a new technique for calculating forces exerted by tagged mri data to material points of the deformable model. furthermore, by plotting the variations over time of the extracted lv model parameters from normal heart data we are able to quantitatively analyze and compare the epicardial and endocardial motion.
background modeling and subtraction of dynamic scenes. background modeling and subtraction is a core componentin motion analysis. the central idea behind such moduleis to create a probabilistic representation of the staticscene that is compared with the current input to performsubtraction. such approach is efficient when the scene to bemodeled refers to a static structure with limited perturbation.in this paper, we address the problem of modeling dynamicscenes where the assumption of a static backgroundis not valid. waving trees, beaches, escalators, naturalscenes with rain or snow are examples. inspired by the workproposed in [4], we propose an on-line auto-regressivemodel to capture and predict the behavior of such scenes.towards detection of events we introduce a new metric thatis based on a state-driven comparison between the predictionand the actual frame. promising results demonstratethe potentials of the proposed framework.
cluster-based segmentation of natural scenes. in cluster-based segmentation pixels are mapped into various feature-spaces whereupon they are subjected to a grouping-algorithm. in this paper we develop a robust and versatile non-parametric clustering algorithm that is able to handle the unbalanced and irregular clusters encountered in such segmentation- applications.the strength of our approach lies in the definition and use of two cluster-validity indices that are independent of the cluster-topology. by combining them, an excellent clustering can be identified, and experiments confirm that the associated clusters do indeed correspond to perceptually salient image-regions.
shape extraction for curves using geometry-driven diffusion and functional optimization. in this paper we show how both geometry-driven diffusion and optimization of the mumford-shah functional can be used to develop a type of curve-evolution that is able to preserve salient features of closed curves (such as corners and straight line segments), while simultaneously suppressing noise and irrelevant details. the idea is to characterize the curve by means of its angle-function (i.e. the angle between the tangent and a fixed axis) and to apply the appropriate dynamics to this one-dimensional representation. we show how constrained evolution equations can be used to keep the corresponding curve closed at all times.
evaluation of features detectors and descriptors based on 3d objects. we explore the performance of a number of popular feature detectors and descriptors in matching 3d object features across viewpoints and lighting conditions. to this end we design a method, based on intersecting epipolar constraints, for providing ground truth correspondence automatically. these correspondences are based purely on geometric information, and do not rely on the choice of a specific feature appearance descriptor. we test detector-descriptor combinations on a database of 100 objects viewed from 144 calibrated viewpoints under three different lighting conditions. we find that the combination of hessian-affine feature finder and sift features is most robust to viewpoint change. harris-affine combined with sift and hessian-affine combined with shape context descriptors were best respectively for lighting change and change in camera focal length. we also find that no detector-descriptor combination performs well with viewpoint changes of more than 25---30¿.
a bayesian approach for shadow extraction from a single image. this paper addresses the problem of shadow extraction from a single image of a complex natural scene. no simplifying assumption on the camera and the light source other than the lambertian assumption is used. our method is unique because it is capable of translating very rough user-supplied hints into the effective likelihood and prior functions for our bayesian optimization. the likelihood function requires a decent estimation of the shadowless image, which is obtained by solving the associated poisson equation. our bayesian framework allows for the optimal extraction of smooth shadows while preserving texture appearance under the extracted shadow. thus our technique can be applied to shadow removal, producing some best results to date compared with the current state-of-the-art techniques using a single input image. we propose related applications in shadow compositing and image repair using our bayesian technique.
integration of conditionally dependent object features for robust figure/background segmentation. we propose a new technique for fusing multiple cues to robustly segment an object from its background in video sequences that suffer from abrupt changes of both illumination and position of the target. robustness is achieved by the integration of appearance and geometric object features and by their description using particle filters. previous approaches assume independence of the object cues or apply the particle filter formulation to only one of the features, and assume a smooth change in the rest, which can prove is very limiting, especially when the state of some features needs to be updated using other cues or when their dynamics follow non-linear and unpredictable paths. our technique offers a general framework to model the probabilistic relationship between features. the proposed method is analytically justified and applied to develop a robust tracking system that adapts online and simultaneously the colorspace where the image points are represented, the color distributions, and the contour of the object. results with synthetic data and real video sequences demonstrate the robustness and versatility of our method.
dominant sets and hierarchical clustering. dominant sets are a new graph-theoretic concept that has proven to be relevant in partitional (flat) clustering as well as image segmentation problems. however, in many computer vision applications, such as the organization of an image database, it is important to provide the data to be clustered with a hierarchical organization, and it is not clear how to do this within the dominant set framework. in this paper we address precisely this problem, and present a simple and elegant solution to it. to this end, we consider a family of (continuous) quadratic programs which contain a parameterized regularization term that controls the global shape of the energy landscape. when the regularization parameter is zero the local solutions are known to be in one-to-one correspondence with dominant sets, but when it is positive an interesting picture emerges. we determine bounds for the regularization parameter that allow us to exclude from the set of local solutions those inducing clusters of size smaller than a prescribed threshold. this suggests a new (divisive) hierarchical approach to clustering, which is based on the idea of properly varying the regularization parameter during the clustering process. straight forward dynamics from evolutionary game theory are used to locate the solutions of the quadratic programs at each level of the hierarchy. we apply the proposed framework to the problem of organizing a shape database. experiments with three different similarity matrices (and databases) reported in the literature have been conducted, and the results confirm the effectiveness of our approach.
guiding model search using segmentation. in this paper we show how a segmentation as preprocessing paradigm can be used to improve the efficiency and accuracy of model search in an image. we operationalize this idea using an over-segmentation of an image into superpixels. the problem domain we explore is human body pose estimation from still images. the superpixels prove useful in two ways. first, we restrict the joint positions in our human body model to lie at centers of superpixels, which reduces the size of the model search space. in addition, accurate support masks for computing features on half-limbs of the body model are obtained by using agglomerations of superpixels as half-limb segments. we present results on a challenging dataset of people in sports news images.
dynamic refraction stereo. in this paper we consider the problem of reconstructing the 3d position and surface normal of points on an unknown, arbitrarily-shaped refractive surface. we show that two viewpoints are sufficient to solve this problem in the general case, even if the refractive index is unknown. the key requirements are (1) knowledge of a function that maps each point on the two image planes to a known 3d point that refracts to it, and (2) light is refracted only once. we apply this result to the problem of reconstructing the time-varying surface of a liquid from patterns placed below it. to do this, we introduce a novel "stereo matching" criterion called refractive disparity, appropriate for refractive scenes, and develop an optimization-based algorithm for individually reconstructing the position and normal of each point projecting to a pixel in the input views. results on reconstructing a variety of complex, deforming liquid surfaces suggest that our technique can yield detailed reconstructions that capture the dynamic behavior of free-flowing liquids.
a unified factorization algorithm for points, line segments and planes with uncertainty models. in this paper we present a unified factorization algorithm for recovering structure and motion from image sequences by using point features, line segments and planes. this new formulation is based on directional uncertainty model for features. points and line segments are both described by the same probabilistic models and so can be recovered in the same way. prior information on the coplanarity of features is shown to fit naturally into the new factorization formulation and provides additional constraints for the shape recovery. this formulation leads to a weighted least squares motion and shape recovery problem which is solved by an efficient quasi-linear algorithm. the statistical uncertainty model also enables us to recover uncertainty estimates for the reconstructed three dimensional feature locations.
determining facial expressions in real-time. we suggest an approach to describing and tracking the deformation of facial features. we concentrate on the mouth since its shape is important in detecting emotion. however, we believe that our system could be extended to deal with other facial features. in our system, the mouth is described by a valley contour which is based between the lips. this contour is shown to exist independently of illumination, viewpoint, identity, and expression. we present a real time mouth tracking system that follows this valley. it is shown to be robust to changes in identity, illumination and viewpoint. a simple classification algorithm was found to be sufficient to discriminate between 5 different mouth shapes, with a 100% recognition rate.
robust tracking with spatio-velocity snakes: kalman filtering approach. using results from robust kalman filtering, we present a new kalman filter-based snake model for tracking of nonrigid objects in combined spatio-velocity space. the proposed model is the stochastic version of the velocity snake which is an active contour model for combined tracking of position and velocity of nonrigid boundaries. the proposed model uses image gradient and optical flow measurements along the contour as system measurements. an optical-flow based measurement error is used to detect and reject image measurements which correspond to image clutter or to other objects. the method was applied to object tracking ofboth rigid and nonrigid objects, resulting in good tracking results and robustness to image clutter, occlusions and numerical noise.
objective image fusion performance characterisation. image fusion as a way of combining multiple image signals into a single fused image has in recent years been extensively researched for a variety of multisensor applications. choosing an optimal fusion approach for each application from the plethora of algorithms available however, remains a largely open issue. a small number of metrics proposed so far provide only a rough, numerical estimate of fusion performance with limited understanding of the relative merits of different fusion schemes. this paper proposes a method for comprehensive, objective, image fusion performance characterisation using a fusion evaluation framework based on gradient information representation. the method provides an in-depth analysis of fusion performance by quantifying: information contributions by each sensor, fusion gain, fusion information loss and fusion artifacts (artificial information created). it is demonstrated on the evaluation of an extensive dataset of multisensor images fused with a wide range of established image fusion algorithms. the results demonstrate and quantify a number of well known issues concerning the performance of these schemes and provide a useful insight into a number of more subtle yet important fusion performance effects not immediately accessible to an observer.
automatic generation of grbf networks for visual learning. learning can often be viewed as the problem of mapping from an input space to an output space. examples of these mappings are used to construct a continuous function that approximates given data and generalizes for intermediate instances. generalized radial basis function (grbf) networks are used to formulate this approximating function. a novel method is introduced to construct an optimal grbf network for a given mapping and error bound using the integral wavelet transform. simple one-dimensional examples are used to demonstrate how the optimal network is superior to one constructed using standard ad hoc optimization techniques. the paper concludes with an application of optimal grbf networks to object recognition and pose estimation. the results of this application are favorable.
combining image regions and human activity for indirect object recognition in indoor wide-angle views. traditional methods of object recognition are reliant on shape and so are very difficult to apply in cluttered, wide-angle and low-detail views such as surveillance scenes. to address this, a method of indirect object recognition is proposed, where human activity is used to infer both the location and identity of objects. no shape analysis is necessary. the concept is dubbed ¿interaction signatures¿, since the premise is that a human will interact with objects in ways characteristic of the function of that object ¿ for example, a person sits in a chair and drinks from a cup. the human-centred approach means that recognition is possible in low-detail views and is largely invariant to the shape of objects within the same functional class. this paper implements a bayesian network for classifying region patches with object labels, building upon our previous work in automatically segmenting and recognising a human¿s interactions with the objects. experiments show that interaction signatures can successfully find and label objects in low-detail views and are equally effective at recognising test objects that differ markedly in appearance from the training objects.
n-dimensional probablility density function transfer and its application to colour transfer. this article proposes an original method to estimate a continuous transformation that maps one n-dimensional distribution to another. the method is iterative, non-linear, and is shown to converge. only 1d marginal distributions are used in the estimation process, hence involving low computation costs. as an illustration this mapping is applied to colour transfer between two images of different contents. the paper also serves as a central focal point for collecting together the research activity in this area and relating it to the important problem of automated colour grading.
a shape-based segmentation approach: an improved technique using level sets. we propose a novel approach for shape-based segmentation based on a specially designed level set function format. this format permits us to better control the process of object registration which is an important part in the shapebased segmentation framework. the method depends on a set of training shapes used to build a parametric shape model. the color is taken into consideration besides the shape prior information. the shape model is fitted to the image volume by registration through an energy minimization problem. the approach overcomes the conventional methods problems like point correspondences and weighing coefficients tuning of the partial differential equations (pde's). also it is suitable for multi-dimensional data and computationally efficient. results of extracting the 2d star fish and the brain ventricles in 3d demonstrate theefficiency of the approach.
efficient model-based 3d tracking of deformable objects. efficient incremental image alignment is a topic of renewed interest in the computer vision community because of its applications in model fitting and model-based object tracking. successful compositional procedures for aligning 2d and 3d models under weak-perspective imaging conditions have already been proposed. here we present a mixed compositional and additive algorithm which is applicable to the full projective camera case.
image spaces and video trajectories: using isomap to explore video sequences. dimensionality reduction techniques seek to representa set of images as a set of points in a low dimensionalspace.here we explore a video representation thatconsiders a video as two parts - a space of possibleimages and a trajectory through that space.the non-lineardimensionality reduction technique of isomap,gives, for many interesting scenes, a very low dimensionalrepresentation of the space of possible images.analysis of the shape of the video trajectory throughthese image spaces gives new tools for video analysis.experiments with natural video sequences illustratemethods for the very different tasts of classifyingvideo clips and temporal super-resolution.
recognizing 3d objects using photometric invariant. we describe an efficient algorithm for recognizing 3d objects by combining photometric, and geometric invariants. a photometric property is derived, that is invariant to the changes of illumination and to relative object motion with respect to the camera and/or the lighting source in 3d space. we argue that conventional color constancy algorithms can not be used in the recognition of 3d objects. further we show recognition does not require a full constancy of colors, rather, it only needs something that remains unchanged under the varying light conditions and poses of the objects. combining the derived color invariant and the spatial constraints on the object surfaces, we identify corresponding positions in the model and the data space coordinates, using centroid invariance of corresponding groups of feature positions. tests are given to show the stability and efficiency of our approach to 3d object recognition.
bias-corrected optical flow estimation for road vehicle tracking. model-based vehicle tracking in traffic image sequences can be made more robust by matching expected displacementrates of vehicle surface points to optical flow (of) vectors computed from an image sequence. the capability to track vehicles uninterruptedly in thismanner over extended image sequences results in the ability to investigate even small errors in of estimation. it turns out that the of magnitudes are systematically underestimated. the ¿ albeit small ¿ bias can be corrected by analyzing the influence of explicitly modeled grey value noise on the precision of of values estimated by means of the neighborhood sampling method.
self-calibration and metric reconstruction in spite of varying and unknown internal camera parameters. in this paper the feasibility of self-calibration in the presence of varying internal camera parameters is under investigation. a self-calibration method is presented which efficiently deals with all kinds of constraints on the internal camera parameters. within this framework a practical method is proposed which can retrieve metric reconstruction from image sequences obtained with uncalibrated zooming/focusing cameras. the feasibility of the approach is illustrated on real and synthetic examples.
mesh optimization using an inconsistency detection template. we propose a new technique for optimizing a triangular mesh for polyhedral representation of the scene in a video stream. we introduce a specially designed template that can effectively detect color and texture discontinuities. using real images, we demonstrate that our method is superior to existing methods.
structure and semi-fluid motion analysis of stereoscopic satellite images for cloud tracking. time-varying multispectral observations of clouds from meteorological satellites are used to estimate cloud-top heights (structure) and cloud winds (semi-fluid motion). stereo image pairs over several time steps were acquired by two geostationary satellites with synchronized scanning instruments. cloud-top height estimation from these image pairs is performed using an improved automatic stereo analysis algorithm on a massively parallel maspar computer with 16 k processors. a new category of motion behavior known as semi-fluid motion is described for modeling cloud motions and an automatic algorithm for extracting semi-fluid motion is developed to track cloud winds. the time sequential dense estimates of cloud-top height depth maps in conjunction with intensity data are used to estimate local semi-fluid motion parameters for cloud tracking. both stereo disparities and motion correspondences are estimated to sub-pixel accuracy. the interactive image spreadsheet (iiss) is a new versatile visualization tool that was enhanced to analyze and visualize the results of the stereo analysis and semi-fluid motion estimation algorithms. experimental results using time-varying data of the visible channel from two satellites in geosynchronous orbit is presented for the hurricane frederic.