waashk/dblp huggingface dataset:
landmark-based shape deformation with topology-preserving constraints. this paper presents a novel approach for landmark-basedshape deformation, in which fitting error and shapedifference are formulated into a support vector machine(svm) regression problem. to well describe nonrigid shapedeformation, this paper measures the shape difference usinga thin-plate spline model. the proposed approach iscapable of preserving the topology of the template shape inthe deformation. this property is achieved by inserting aset of additional points and imposing a set of linear equalityand/or inequality constraints. the underlying optimizationproblem is solved using a quadratic programming algorithm.the proposed method has been tested using practicaldata in the context of shape-based image segmentation.some relevant practical issues, such as missing detectedlandmarks and selection of the regularization parameter arealso briefly discussed.
error analysis of pure rotation-based self-calibration. abstract--self-calibration using pure rotation is a well-known technique and has been shown to be a reliable means for recovering intrinsic camera parameters. however, in practice, it is virtually impossible to ensure that the camera motion for this type of self-calibration is a pure rotation. in this paper, we present an error analysis of recovered intrinsic camera parameters due to the presence of translation. we derived closed-form error expressions for a single pair of images with nondegenerate motion; for multiple rotations for which there are no closed-form solutions, analysis was done through repeated experiments. among others, we show that translation-independent solutions do exist under certain practical conditions. our analysis can be used to help choose the least error-prone approach (if multiple approaches exist) for a given set of conditions.
3d shape and motion analysis from image blur and smear: a unified approach. this paper addresses 3d shape recovery and motion estimation using a realistic camera model with an aperture and a shutter. the spatial blur and temporal smear effects induced by the camera's finite aperture and shutter speed are used for inferring both the shape and motion of the imaged objects
separating reflections in human iris images for illumination estimation. a method is presented for separating corneal reflections in an image of human irises to estimate illumination from the surrounding scene. previous techniques for reflection separation have demonstrated success in only limited cases, such as for uniform colored lighting and simple object textures, so they are not applicable to irises which exhibit intricate textures and complicated reflections of the environment. to make this problem feasible, we present a method that capitalizes on physical characteristics of human irises to obtain an illumination estimate that encompasses the prominent light contributors in the scene. results of this algorithm are presented for eyes of different colors, including light colored eyes for which reflection separation is necessary to determine a valid illumination estimate.
fusion of static and dynamic body biometrics for gait recognition. human identification at a distance has recently gainedgrowing interest from computer vision researchers. thispaper aims to propose a visual recognition algorithmbased upon fusion of static and dynamic body biometrics.for each sequence involving a walking figure, posechanges of the segmented moving silhouettes arerepresented as an associated sequence of complex vectorconfigurations, and are then analyzed using the procrustesshape analysis method to obtain a compact appearancerepresentation, called static information of body. also, amodel-based approach is presented under a condensationframework to track the walker and to recover joint-angletrajectories of lower limbs, called dynamic information ofgait. both static and dynamic cues are respectively used forrecognition using the nearest exemplar classifier. they arealso effectively fused on decision level using differentcombination rules to improve the performance of bothidentification and verification. experimental results on adataset including 20 subjects demonstrate the validity ofthe proposed algorithm.
variable bandwidth qmdpe and its application in robust optical flow estimation. robust estimators, such as least median of squared (lmeds) residuals, m-estimators, the least trimmed squares (lts) etc., have been employed to estimate optical flow from image sequences in recent years. however, these robust estimators have a breakdown point of no more than 50%. in this paper, we propose a novel robust estimator, called variable bandwidth quick maximum density power estimator (vbqmdpe),which can tolerate more than 50% outliers. we apply the novel proposed estimator to robust optical flow estimation. our method yields better results than most other recently proposed methods, and it has the potential to better handle multiple motion effects.
eye gaze estimation from a single image of one eye. in this paper, we present a novel approach, called the"one-circle" algorithm, for measuring the eye gaze using a monocular image that zooms in on only one eye of a person. observing that the iris contour is a circle, we estimate the normal direction of this iris circle, considered as the eye gaze, from its elliptical image. from basic projective geometry, an ellipse can be back-projected into space onto two circles of different orientations. however, by using an anthropometric property of the eyeball, the correct solution can be disambiguated. this allows us to obtain a higher resolution image of the iris with a zoom-in camera and thereby achieving higher accuracies in the estimation. the robustness of our gaze determination approach was verified statistically by the extensive experiments on synthetic and real image data. the two key contributions in this paper are that we show the possibility of finding the unique eye gaze direction from a single image of one eye and that one can obtain better accuracy as a consequence of this.
unified subspace analysis for face recognition. we propose a face difference model that decomposesface difference into three components, intrinsicdifference, transformation difference, and noise. usingthe face difference model and a detailed subspace analysison the three components we develop a unified frameworkfor subspace analysis. using this framework we discoverthe inherent relationship among different subspacemethods and their unique contributions to the extractionof discriminating information from the face difference.this eventually leads to the construction of a 3dparameter space that uses three subspace dimensions asaxis. within this parameter space, we develop a unifiedsubspace analysis method that achieves better recognitionperformance than the standard subspace methods on over2000 face images from the feret database.
joint region tracking with switching hypothesized measurements. this paper proposes a switching hypothesized measurements (shm) model supporting multimodal probability distributions and presents the application ofthe model in handling potential variability in visual environments when tracking multiple objects jointly. for a set of occlusion hypotheses, a frame is measured once under each hypothesis, resulting in a set of measurements at each time instant. a computationally efficient shm filter is derived for online joint region tracking. both occlusion relationships and states of the objects are recursively estimated from the history of hypothesized measurements. the reference image is updated adaptively to deal with appearance changes of the objects. the shm model is generally applicable to various dynamic processes with multiple alternative measurement methods.
patch based blind image super resolution. in this paper, a novel method for learning based image super resolution (sr) is presented. the basic idea is to bridge the gap between a set of low resolution (lr) images and the corresponding high resolution (hr) image using both the sr reconstruction constraint and a patch based image synthesis constraint in a general probabilistic framework. we show that in this framework, the estimation of the lr image formation parameters is straightforward. the whole framework is implemented via an annealed gibbs sampling method. experiments on sr on both single image and image sequence input show that the proposed method provides an automatic and stable way to compute super-resolution and the achieved result is encouraging for both synthetic and real lr images.
cumulative residual entropy, a new measure of information & its application to image alignment. in this paper we use the cumulative distribution of a random variable to define the information content in it and use it to develop a novel measure of information that parallels shannon entropy, which we dub cumulative residual entropy (cre). the key features of cre may be summarized as, (1)its definition is valid in both the continuous and discrete domains,(2) it is mathematically more general than the shannon entropy and (3) its computation from sample data is easy and these computations converge asymptotically to the true values. we define the cross-cre (ccre) between two random variables and apply it to solve the uni- & multi-modal image alignment problem for parameterized (rigid, affine and projective) transformations. the key strengths of the ccre over using the now popular mutual information method (based on shannon's entropy) are that the former has significantly larger noise immunity and a much larger convergence range over the field of parameterized transformations. these strengths of ccre are demonstrated via experiments on synthesized and real image data.
modeling textured motion : particle, wave and sketch. in this paper, we present a generative model for textured motion phenomena, such as falling snow, wavy river and dancing grass, etc. firstly, we represent an image as a linear superposition of image bases selected from a generic and over-complete dictionary. the dictionary contains gabor bases for point/particle elements and fourier bases for wave-elements. these bases compete to explain the input images. the transform from a raw image to a base or a token representation leads to large dimension reduction. secondly, we introduce a unified motion equation to characterize the motion of these bases and the interactions between waves and particles, e.g. a ball floating on water. we use statistical learning algorithm to identify the structure of moving objects and their trajectories automatically. then novel sequences can be synthesized easily from the motion and image models. thirdly, we replace the dictionary of gabor and fourier bases with symbolic sketches (also bases). with the same image and motion model, we can render realistic and stylish cartoon animation. in our view, cartoon and sketch are symbolic visualization of the inner representation for visual perception. the success of the cartoon animation, in turn, suggests that our image and motion models capture the essence of visual perception of textured motion.
rigid body segmentation and shape description from dense optical flow under weak perspective. we present an algorithm for identifying and tracking independently moving rigid objects from optical flow. the proposed method uses the fact that each distinct object has a unique epipolar constraint associated with its motion. this is in contrast to using local optical flow information for segmentation. thus motion discontinuities based on self-occlusion are distinguished from those due to separate objects. the use of epipolar geometry allows for the determination of individual motion parameters for each object as well as the recovery of relative depth for each point on the object. the segmentation problem is formulated as a scene partitioning problem and a statistic-based algorithm which uses only nearest neighbor interactions and a finite number of iterations is developed. a kalman filter based approach is used for tracking motion parameters with time. the algorithm assumes an affine camera where perspective effects are limited to changes in overall scale. no camera calibration parameters are required.
intensity and feature based stereo matching by disparity parametrization. in this paper, we propose a new solution to the stereo correspondence problem by including features in intensity based matching. the features we use are intensity gradients in both the x and y directions of the left and the deformed right images. although a uniform smoothness constraint is still used, it is never-the-less applied only to non-feature regions. to avoid local minima in function minimization, we propose to parameterize the disparity function by hierarchical gaussians. a simple stochastic gradient method is used to estimate the gaussian weights. experiments with various real stereo images show robust performances.
color edge detection by photometric quasi-invariants. photometric invariance is used in many computer vision applications.the advantage of photometric invariance is therobustness against shadows, shading, and illumination conditions.however, the drawbacks of photometric invarianceis the loss of discriminative power and the inherent instabilitiescaused by the non-linear transformations to computethe invariants.in this paper, we propose a new class of derivativeswhich we refer to as photometric quasi-invariants. thesequasi-invariants share with full invariants the nice propertythat they are robust against photometric edges, such asshadows or specular edges. further, these quasi-invariantsdo not have the inherent instabilities of full photometric invariants.we will apply these quasi-invariant derivativesin the context of photometric invariant edge detection andclassification. experiments show that the quasi-invariantderivatives are stable and they significantly outperform thefull invariant derivatives in discriminative power.
segmentation using eigenvectors: a unifying view. automatic grouping and segmentation of images remains a challenging problem in computer vision. recently, a number of authors have demonstrated good performance on this task using methods that are based on eigenvectors of the affinity matrix. these approaches are extremely attractive in that they are based on simple eigen-decomposition algorithms whose stability is well understood. nevertheless, the use of eigen-decompositions in the context of segmentation is far from well understood.in this paper we give a unified treatment of these algorithms, and show the close connections between them while highlighting their distinguishing features. we then prove results on eigenvectors of block matrices that allow us to analyze the performance of these algorithms in simple grouping settings. finally, we use our analysis to motivate a variation on the existing methods that combines aspects from different eigenvector segmentation algorithms. we illustrate our analysis with results on real and synthetic images.
capturing subtle facial motions in 3d face tracking. facial motions produce not only facial feature points motions,but also subtle appearance changes such as wrinklesand shading changes. these subtle changes are importantyet difficult issues for both analysis (tracking) and synthesis(animation). previous approaches were mostly basedon models learned from extensive training appearance examples.however, the space of all possible facial motionappearance is huge. thus, it is not feasible to collect samplescovering all possible variations due to lighting conditions,individualities, and head poses. therefore, it is difficultto adapt such models to new conditions. in this paper,we present an adaptive technique for analyzing subtle facialappearance changes. we propose a new ratio-imagebased appearance feature, which is independent of a person'sface albedo. this feature is used to track face appearancevariations based on exemplars. to adapt the exemplarappearance model to new people and lighting conditions,we develop an online em-based algorithm. experimentsshow that the proposed method improves classification resultsin a facial expression recognition task, where a varietyof people and lighting conditions are involved.
combinatorial constraints on multiple projections of a set of points. multiple projections of a scene cannot be arbitrary, the allowedconfigurations being given by matching constraints.this paper presents new matching constraints on multipleprojections of a rigid point set by uncalibrated cameras, obtainedby formulation in the oriented projective rather thanprojective geometry. they follow from consistency of orientationsof camera rays and from the fact that the scene is theaffine rather that projective space. for their non-parametricnature, we call them combinatorial. the constraints are derivedin a unified theoretical framework using the theory oforiented matroids. for example, we present constraints on4 point correspondences for 2d camera resectioning, on 3correspondences in two 1d cameras, and on 4 correspondencesin two 2d cameras.
rendering real-world objects using view interpolation. presents a new approach to rendering arbitrary views of real-world 3d objects of complex shapes. we propose to represent an object by a sparse set of corresponding 2d views, and to construct any other view as a combination of these reference views. we show that this combination can be linear, assuming proximity of the views, and we suggest how the visibility of constructed points can be determined. our approach makes it possible to avoid difficult 3d reconstruction, assuming only rendering is required. moreover, almost no calibration of views is needed. we present preliminary results on real objects, indicating that the approach is feasible.
space-time scene manifolds. the space of images is known to be a non-linear sub-space that is difficult to model. this paper derives an algorithm that walks within this space. we seek a manifold through the video volume that is constrained to lie locally in this space. every local neighborhood within the manifold resembles some image patch. we call this the scene manifold because the solution traces the scene outline. for a broad class of inputs the problem can be posed as finding the shortest path in a graph and can thus be solved efficiently to produce the globally optimal solution. constraining appearance rather than geometry gives rise to numerous new capabilities. here we demonstrate the usefulness of this approach by posing the well-studied problem of mosaicing in a new way. instead of treating it as geometrical alignment, we pose it as an appearance optimization. since the manifold is constrained to lie in the space of valid image patches, the resulting mosaic is guaranteed to have the least distortions possible. any small part of it can be seen in some image even though the manifold spans the whole video. thus it can deal seamlessly with both static and dynamic scenes, with or without 3d parallax. essentially, the method simultaneously solves two problems that have been solved only separately until now: alignment and mosaicing.
consensus surfaces for modeling 3d objects from multiple range images. in this paper, we present a robust method for creating a triangulated surface mesh from multiple range images. our method merges a set of range images into a volumeteric implicit-surface representation which is converted to a surface mesh using a variant of the marching-cubes algorithm. unlike previous techniques based on implicit-surface representations, our method estimates the signed distance to the object surface by finding a consensus of locally coherent observations of the surface. we call this method the consensus-surface algorithm. this algorithm effectively eliminates many of the troublesome effects of noise and extraneous surface observations without sacrificing the accuracy of the resulting surface. we utilize octrees to represent volumetric implicit surfaces ¿ effectively reducing the computation and memory requirements of the volumetric representation without sacrificing accuracy of the resulting surface. we present results which demonstrate that our consensus-surface algorithm can construct accurate geometric models from rather noisy input range date.
algorithms for implicit deformable models. this paper presents a framework for implicit deformable models and a pair of new algorithms for solving the nonlinear partial differential equations that result from this framework. implicit models offer a useful alternative to parametric models, particularly when dealing with the deformation of higher-dimensional objects. the basic expressions for the evolution of implicit models are relatively straightforward; they follow as a direct consequence of the chain rule for differentiation. more challenging, however, is the development of algorithms that are stable and efficient. the first algorithm is a viscosity approximation which gives solutions over a dense set in the range, providing a means of calculating the solutions of embedded families of contours simultaneously. the second algorithm incorporates sparse solutions for a discrete set of contours. this sparse-field method requires a fraction of the computation compared to the first but offers solutions only for a finite number of contours. results from 3d medical data as well as video images are shown.
scene modeling based on constraint system decomposition techniques. we present a new approach to 3d scene modeling basedon geometric constraints. contrary to the existing methods,we can quickly obtain 3d scene models that respectthe given constraints exactly. our system can describe alarge variety of linear and non-linear constraints in a flexibleway.to deal with the constraints, we decided to exploit theproperties of the gpdof algorithm developed in the constraintprogramming community [12]. the approach isbased on a dictionary of so-called r-methods, based on theoremsof geometry, which can solve a subset of geometricconstraints in a very efficient way. gpdof is used to find,in polynomial-time, a reduced parameterization of a scene,and to decompose the equation system, induced by constraints,into a sequence of r-methods. we have validatedour approach in reconstructing, from images, 3d modelsof buildings based on linear and quadratic geometric constraints.
closing the loop on multiple motions. we describe a number of advances in the analysis of road scenes when the scene contains multiple moving objects and is observed by a single nonsteerable camera mounted on the front of a vehicle. our structure from motion approach to scene segmentation derives front the observed motions of independently moving objects and requires no prior knowledge. we describe a hierarchy of camera models for the analysis of the scene, the simpler models handle degeneracies that occur in the more complex models. the major technical contribution is the recursive computation of feature clusters, which are fed forward over time. this closed loop feature tracking generates extended feature trajectories which significantly improve the discriminating power of scene segmentation.
a quantitative analysis of view degeneracy and its use for active focal length control. we quantify the observation by kender and freudenstein (1987) that degenerate views occupy a significant fraction of the viewing sphere surrounding an object. this demonstrates that systems for recognition must explicitly account for the possibility of view degeneracy. we show that view degeneracy cannot be detected from a single camera viewpoint. as a result, systems designed to recognize objects from a single arbitrary viewpoint must be able to function in spite of possible undetected degeneracies, or else operate with imaging parameters that cause acceptably low probabilities of degeneracy. to address this need, we give a prescription for active control of focal length that allows a principled tradeoff between the camera field of view and probability of view degeneracy.