waashk/dblp huggingface dataset:
uncalibrated motion capture exploiting articulated structure constraints. we present an algorithm for 3d reconstruction of dynamic articulated structures, such as humans, from uncalibrated multiple views. the reconstruction exploits constraints associated with a dynamic articulated structure, specifically the conservation over time of length between rotational joints. these constraints admit reconstruction of metric structure from at least two different images in each of two uncalibrated parallel projection cameras. as a by product, the calibration of the cameras can also be computed. the algorithm is based on a stratified approach, starting with affine reconstruction from factorization, followed by rectification to metric structure using the articulated structure constraints. the exploitation of these specific constraints admits reconstruction and self-calibration with fewer feature points and views compared to standard self-calibration. the method is extended to pairs of cameras that are zooming, where calibration of the cameras allows compensation for the changing scale factor in a scaled orthographic camera. results are presented in the form of stick figures and animated 3d reconstructions using pairs of sequences from broadcast television. the technique shows promise as a means of creating 3d animations of dynamic activities such as sports events.
dynamic measurement clustering to aid real time tracking. we present a technique for clustering measurements such that high-dimensional parameter estimation problems can be simplified. the key idea is to find rows of the measurement jacobian whose rank is significantly less than its width. such a set of rows gives a cluster of measurements which is affected only by a subset of the parameter space. this cluster can be used independently from other measurements to isolate parameter decisions. unlike static partitioning techniques, the method presented dynamically generates clusters at each step of the estimation. this achieves substantial computational reductions, even for problems which cannot be partitioned in the traditional sense. the technique is applied to the task of tracking camera motions in real-time and video sequences are used to compare the resulting system to previous methods.
passive photometric stereo from motion. we introduce an iterative algorithm for shape reconstruction from multiple images of a moving (lambertian) object illuminated by distant (and possibly time varying) lighting. starting with an initial piecewise linear surface, the algorithm iteratively estimates a new surface based on the previous surface estimate and the photometric information available from the input image sequence. during each iteration, standard photometric stereo techniques are applied to estimate the surface normals up to an unknown generalized bas-relief transform, and a new surface is computed by integrating the estimated normals. the algorithm essentially consists of a sequence of matrix factorizations (of intensity values) followed by minimization using gradient descent (integration of the normals). conceptually, the algorithm admits a clear geometric interpretation, which is used to provide a qualitative analysis of the algorithm¿s convergence. implementation-wise, it is straightforward, being based on several established photometric stereo and structure from motion algorithms. we demonstrate experimentally the effectiveness of our algorithm using several videos of hand-held objects moving in front of a fixed light and camera.
a level line selection approach for object boundary estimation. an energy model-based approach for estimating object boundaries is presented. we study a particular energy, which minimizer can be determined. the method estimates the unknown number of objects and draws object boundaries by selecting the "best" level lines computed from level sets of the original image. unlike previous standard methods, the proposed method does not require iteration for minimizing the energy. in addition, our segmentation algorithm combines anisotropic diffusion-based regularization with level line selection to extract smooth object boundaries. experimental results on 2d biomedical and meteorological images are reported.
an integrated framework for image segmentation and perceptual grouping. this paper presents an efficient algorithm for image segmentation and a framework for perceptual grouping. it makes an attempt to provide one way of combining bottom-up and top-down approaches. in image segmentation, it generalizes the swendsen-wang cut algorithm [1] (swc) to make both 2-way and m-way cuts, and includes topology change processes (graph repartitioning and boundary diffusion). the method directly works at a low temperature without using annealing. we show that it is much faster than the ddmcmc approach [12] and more robust than the swc method. the results are demonstrated on the berkeley data set [7]. in perceptual grouping, it integrates discriminative model learning/computing, a belief propagation algorithm (bp) [15] , and swc into a three-layer computing framework. these methods are realized as different levels of approximation to an "ideal" generative model. we demonstrate the algorithm on the problem of human body configuration.
probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering. in this paper, a new learning framework¿probabilistic boosting-tree (pbt), is proposed for learning two-class and multi-class discriminative models. in the learning stage, the probabilistic boosting-tree automatically constructs a tree in which each node combines a number of weak classifiers (evidence, knowledge) into a strong classifier (a conditional posterior probability). it approaches the target posterior distribution by data augmentation (tree expansion) through a divide-and-conquer strategy. in the testing stage, the conditional probability is computed at each tree node based on the learned classifier, which guides the probability propagation in its sub-trees. the top node of the tree therefore outputs the overall posterior probability by integrating the probabilities gathered from its sub-trees. also, clustering is naturally embedded in the learning phase and each sub-tree represents a cluster of certain level. the proposed framework is very general and it has interesting connections to a number of existing methods such as the a* algorithm, decision tree algorithms, generative models, and cascade approaches. in this paper, we show the applications of pbt for classification, detection, object recognition. we have also applied the framework in segmentation.
gradient flows and geometric active contour models. in this paper, we analyze the geometric active contour models discussed previously from a curve evolution point of view and propose some modifications based on gradient flows relative to certain new feature-based riemannian metrics. this leads to a novel snake paradigm in which the feature of interest may be considered to lie at the bottom of a potential well. thus the snake is attracted very naturally and efficiently to the desired feature. moreover, we consider some 3-d active surface models based on these ideas.
a practical single image based approach for estimating illumination distribution from shadows. this paper presents a practical method that estimates illumination distribution from shadows where the shadows are assumed to be cast on a textured, lambertian surface. previous methods usually require that the reflectance property of the surface be constant or uniform, or need an additional image to cancel out the effects of varying albedo of the textured surface. we deal with an estimation problem for which surface albedo information is not available. in this case, the estimation problem corresponds to an underdetermined one. we show that combination of regularization by correlation and some user-specified information can be a practical method for solving the problem. in addition, as an optimization tool for solving the problem, we develop a constrained non-negative quadratic programming (nnqp) technique into which not only regularization but also user-specified information are easily incorporated. we test and validate our method on both synthetic and real images and present some experimental results.
a representation of specular appearance. the appearance of an object can vary considerably with changes in illumination conditions. methods have been developed to describe these differences for diffuse reflection using the lambertian model, but little work has been done in characterizing specular appearance.towards a more comprehensive global reflectance descriptor, this paper focuses on a representation of specular appearance based on an approximate specular reflection model derived from torrance-sparrow. we propose that under certain illumination and surface conditions local specular structure can be expressed by the logarithms of three intensity-normalized photometric images. the total number of photometric images needed for representing global specular appearance depends on the object surface roughness, and we suggest an illumination planning method for determining the number of images. experimental results demonstrate the effectiveness of this logarithmic model as a specular descriptor.
visual correspondence using energy minimization and mutual information. we address visual correspondence problems without assuming that scene points have similar intensities in different views. this situation is common, usually due tonon-lambertian scenes or to differences between cameras. we use maximization of mutual information, apowerful technique for registering images that requiresno apriori model of the relationship between scene intensities in different views. however, it has provendifficult to use mutual information to compute densevisual correspondence. comparing fixed-size windowsvia mutual information suffers from the well-knownproblems of fixed windows, namely poor performanceat discontinuities and in low-texture regions. in thispaper, we show how to compute visual correspondenceusing mutual information without suffering from theseproblems. using a simple approximation, mutual information can be incorporated into the standard energyminimization framework used in early vision. the energy can then be efficiently minimized using graph cuts,which preserve discontinuities and handle low-textureregions. the resulting algorithm combines the accuratedisparity maps that come from graph cuts with the tolerance for intensity changes that comes from mutual information.
estimation of diffuse and specular appearance. to account for the variability of object appearance due to differences in illumination, attention has recently been focused on representing the set of images for all possible lighting conditions. approaches that address this problem have primarily focused on lighting differences for diffuse reflection using the lambertian model; however, specular reflections can additionally present considerable disparity in appearance.we present a method for representing illumination appearance for both diffuse and specular reflections for objects of uniform surface roughness using four photometric images. this approach uses separation of reflection components, extracts surface reflectances and roughness, and produces arbitrary lighting images without explicit computation of surface shape. experimental results demonstrate the validity of the proposed method for constructing diffuse and specular appearances.
fast vehicle detection with probabilistic feature grouping and its application to vehicle tracking. generating vehicle trajectories from video data is an important application of its (intelligent transportation systems). we introduce a new tracking approach which uses model-based 3-d vehicle detection and description algorithm. our vehicle detection and description algorithm is based on a probabilistic line feature grouping, and it is faster (by up to an order of magnitude) and more flexible than previous image-based algorithms. we present the system implementation and the vehicle detection and tracking results.
opaque document imaging: building images of inaccessible texts. this paper introduces a method for building a readable image of an opaque, rolled or folded text from a volumetric, penetrating scan. the problem is framed by localizing, constructing, and manipulating an image induced by a surface embedded in a 3d voxel space. there are two central contributions that lead to the demonstrated results. first is an energy-based texture formation algorithm, which is a function of voxel intensities and the geometry of the embedded surface. second is a regularization algorithm based on a constrained mapping of the embedded surface to a regularized image plane. the mapping preserves angles and lengths, which minimizes the distortion of text in the image. the experimental results show readable images derived from custom, high resolution (x-ray-based) ct scans of rolled papyrus and ink samples. these methods are significant for scholars seeking to study inaccessible texts, and may lead to viable techniques for scanning everyday opaque objects (books) without opening them.
dynamic stroke information analysis for video-based handwritten chinese character recognition. video-based handwritten character recognition(vcr) system is a new type of character recognitionsystem with many unique advantages over on-linecharacter recognition system. its main problem is toeffectively extract stroke dynamic information fromvideo data for character recognition. in this paper, wepropose a new stroke extraction algorithm throughdynamic stroke information analysis for a vcr system.the experimental results on over 3000 video charactersequences show that our system can extract the chinesecharacter stroke dynamic information similar to an on-line system.
physics-based 3d position analysis of a soccer ball from monocular image sequences. in this paper, we propose a method for locating 3d position of a soccor ball from monocular image sequence of soccor games. toward this goal, we adopted ground-model-to-image transformation together with physics-based approach that a ball follows the parabolic trajectory in the air. by using the transformation the heights of a ball can be easily calculated using simple triangular geometric relations given the start and the end position of the ball on the ground. here the heights of a ball are determined in terms of a player's height. even if the end position of a ball is not given on the ground due to kicking or heading of a falling ball before it touches the ground, the most probable trajectory can be determined by searching based on the physical fact taht the ball follows a parabolic trajectory in the air. we have tested and experimented with a real image sequence the results of which seem promising.
coupled space learning for image style transformation. in this paper, we present a new learning framework for image style transforms. considering that the images in different style representations constitute different vector spaces, we propose a novel framework called coupled space learning to learn the relations between different spaces and use them to infer the images from one style to another style. observing that for each style, only the components correlated to the space of the target style are useful for inference, we first develop the correlative component analysis to pursue the embedded hidden subspaces that best preserve the inter-space correlation information. then we develop the coupled bidirectional transform algorithm to estimate the transforms between the two embedded spaces, where the coupling between the forward transform and the backward transform is explicitly taken into account. to enhance the capability of modelling complex data, we further develop the coupled gaussian mixture model to generalize our framework to a mixture-model architecture. the effectiveness of the framework is demonstrated in the applications including face super-resolution and bidirectional portrait style transforms.
a segmentation algorithm for contrast-enhanced images. medical imaging often involves the injection of contrast agents and the subsequent analysis of tissue enhancement patterns. many important types of tissue have characteristic enhancement patterns; for example, in magnetic resonance (mr) mammography, malignancies exhibit a characteristic "wash out" temporal pattern, while in mr angiography, arteries, veins and parenchyma each have their own distinctive temporal signature. in such image sequences, there are substantial changes in intensities; however, this change is due primarily to the contrast agent rather than the motion of scene elements. as a result, the task of segmenting contrast-enhanced images poses interesting new challenges for computer vision. in this paper, we propose a new image segmentation algorithm for image sequences with contrast enhancement, using a model-based time series analysis of individual pixels. we use energy minimization via graph cuts to efficiently ensure spatial coherence. the energy is minimized in an expectation-maximization fashion that alternates between segmenting the image into a number of non-overlapping regions and finding the temporal profile parameters which best describe the behavior of each region. preliminary experiments on mr mammography and mr angiography studies show the algorithm's ability to find an accurate segmentation.
direct estimation of affine image deformations using visual front-end operations with automatic scale selection. this article deals with the problem of estimating deformations of brightness patterns using visual front-end operations. estimating such deformations constitutes an important subtask in several computer vision problems relating to image correspondence and shape estimation. the following subjects are treated: the problem of decomposing affine flow fields into simpler components is analysed in detail. a canonical parametrization is presented based on singular value decomposition, which naturally separates the rotationally invariant components of the flow field from the rotationally variant ones. a novel mechanism is presented for automatic selection of scale levels when estimating local affine deformations. this mechanism is expressed within a multiscale framework where disparity estimates are computed in a hierarchical coarse-to-fine manner and corrected using iterative techniques. then, deformation estimates are selected from the scales that minimize a certain normalized residual over scales. finally, the descriptors so obtained serve as initial data for computing refined estimates of the local deformations.
robotic control with partial visual information. we consider a class of control tasks, which rely from partial visual information in a robotic setting. these tasks are hard in the sense that at every given moment, the available information is insufficient for the control task. still, the amount of information collected throughout the control process is large and thus seems sufficient for carrying out the task.such situations commonly arise when the object is frequently occluded from one of the cameras in a stereo pair or when only one moving camera is available.we propose a generic contr ol rule for such tasks and characterize the conditions required for the success of the task. the analysis is based on the observation that mathematically, the behavior of such systems is related to a class of row-action (and pocs) optimization algorithms.in the second part of the paper we focus on one particular task from this class: position and orientation control with a single rotating camera. we show that this task may be carried out, in principle, for any camera movement, and suggest efficient control strategy and camera moving strategy. interestingly, it seems that the advisable control law is not consistent withsimple-minded intuition. we substantiate our claims by simulations and experiments on real data.
deformation invariant image matching. we propose a novel framework to build descriptors of local intensity that are invariant to general deformations. in this framework, an image is embedded as a 2d surface in 3d space, with intensity weighted relative to distance in x-y. we show that as this weight increases, geodesic distances on the embedded surface are less affected by image deformations. in the limit, distances are deformation invariant. we use geodesic sampling to get neighborhood samples for interest points, then use a geodesic-intensity histogram (gih) as a deformation invariant local descriptor. in addition to its invariance, the new descriptor automatically finds its support region. this means it can safely gather information from a large neighborhood to improve discriminability. furthermore, we propose a matching method for this descriptor that is invariant to affine lighting changes. we have tested this new descriptor on interest point matching for two data sets, one with synthetic deformation and lighting change, another with real non-affine deformations. our method shows promising matching results compared to several other approaches.
representation and self-similarity of shapes. representing shapes is a significant problem for vision systems that must recognize or classify objects. we derive a representation for a given shape by investigating its self-similarities, and constructing its shape axis(sa) and shape axis tree (sa-tree).we start with a shape, its boundary contour, and two different parameterizations for the contour. to measure its self-similarity we consider matching pairs of points (and their tangents) along the boundary contour, i.e., matching the two parameterizations. the matching, or self-similarity criteria may vary, e.g., co-circularity, parallelism, distance, region homogeneity. the loci of middle points of the pairing contour points are the shape axis and they can be grouped into a unique tree graph, the sa-tree. the shape axis for the co-circularity criteria is compared to the symmetry axis. an interpretation in terms of object parts is also presented.
towards a unified iu environment: coordination of existing iu tools with the iue. progress in the field of computer vision/image understanding (iu) has long been hampered by the lack of standard software environments for research and application development. the image understanding environment (iue) is being implemented to provide a freely-distributed standard software environment that is appropriate for a wide range of research and development activities. nevertheless, we recognize that the iue will not serve everyone's individual needs and that conversion to the iue could be an expensive process for many sites. therefore, we are investing significant effort to make the iue an open environment that can work in coordination with existing iu systems and tools. this paper introduces the work being performed at amerinex ai to address the processing and data representation factors involved in this coordination.
effciently solving dynamic markov random fields using graph cuts. in this paper we present a fast new fully dynamic algorithm for the st-mincut/max-flow problem. we show how this algorithm can be used to efficiently compute map estimates for dynamically changing mrf models of labelling problems in computer vision, such as image segmentation. specifically, given the solution of the max-flow problem on a graph, we show how to efficiently compute the maximum flow in a modified version of the graph. our experiments showed that the time taken by our algorithm is roughly proportional to the number of edges whose weights were different in the two graphs. we test the performance of our algorithmon one particular problem: the object-background segmentation problem for video and compare it with the best known st-mincut algorithm. the results show that the dynamic graph cut algorithm is much faster than its static counterpart and enables real time image segmentation. it should be noted that our method is generic and can be used to yield similar improvements in many other cases that involve dynamic change in the graph.