waashk/dblp huggingface dataset:
behind the depth uncertainty: resolving ordinal depth in sfm. structure from motion(sfm) is beset by the noise sensitivity problem. previous works show that some motion ambiguities are inherent and errors in the motion estimates are inevitable. these errors may render accurate metric depth estimate difficult to obtain. however, can we still extract some valid and useful depth information from the inaccurate metric depth estimates? in this paper, the resolution of ordinal depth extracted from the inaccurate metric depth is investigated. based on a general depth distortion model, a sufficient condition is derived for ordinal depth to be extracted validly. by studying the geometry and statistics of the image regions satisfying this condition, we found that although metric depth estimates are inaccurate, ordinal depth can still be discerned locally if physical metric depth difference is beyond certain discrimination threshold. the resolution level of discernible ordinal depth decreases as the visual angle subtended by the points increases, as the speed of the motion carrying the depth information decreases, and as points recede from the camera. these findings suggest that accurate knowledge of qualitative 3d structure is ensured in a small local image neighborhood, which might account for biological foveated vision and shed light on the nature of the perceived visual space.
direct bundle estimation for recovery of shape, reflectance property and light position. given a set of images captured with a fixed camera while a point light source moves around an object, we can estimate the shape, reflectance property and texture of the object, as well as the positions of the light source. our formulation is a large-scale nonlinear optimization that allows us to adjust the parameters so that the images synthesized from all of the parameters optimally fit the input images. this type of optimization, which is a variation of the bundle adjustment for structure and motion reconstruction, is often employed to refine a carefully constructed initial estimation. however, the initialization task often requires a great deal of labor, several special devices, or both. in the present paper, we describe (i) an easy method of initialization that does not require any special devices or a precise calibration and (ii) an efficient algorithm for the optimization. the efficiency of the optimization method enables us to use a simple initialization. for a set of synthesized images, the proposed method decreases the residual to zero. in addition, we show that various real objects, including toy models and human faces, can be successfully recovered.
photo and video quality evaluation: focusing on the subject. traditionally, distinguishing between high quality professional photos and low quality amateurish photos is a human task. to automatically assess the quality of a photo that is consistent with humans perception is a challenging topic in computer vision. various differences exist between photos taken by professionals and amateurs because of the use of photography techniques. previous methods mainly use features extracted from the entire image. in this paper, based on professional photography techniques, we first extract the subject region from a photo, and then formulate a number of high-level semantic features based on this subject and background division. we test our features on a large and diverse photo database, and compare our method with the state of the art. our method performs significantly better with a classification rate of 93% versus 72% by the best existing method. in addition, we conduct the first study on high-level video quality assessment. our system achieves a precision of over 95% in a reasonable recall rate for both photo and video assessments. we also show excellent application results in web image search re-ranking.
partial difference equations over graphs: morphological processing of arbitrary discrete data. mathematical morphology (mm) offers a wide range of operators to address various image processing problems. these processing can be defined in terms of algebraic set or as partial differential equations (pdes). in this paper, a novel approach is formalized as a framework of partial difference equations (pdes) on weighted graphs. we introduce and analyze morphological operators in local and nonlocal configurations. our framework recovers classical local algebraic and pdes-based morphological methods in image processing context; generalizes them for nonlocal configurations and extends them to the treatment of any arbitrary discrete data that can be represented by a graph. it leads to considering a new field of application of mm processing: the case of high-dimensional multivariate unorganized data.
scale-dependent/invariant local 3d shape descriptors for fully automatic registration of multiple sets of range images. despite the ubiquitous use of range images in various computer vision applications, little has been investigated about the size variation of the local geometric structures captured in the range images. in this paper, we show that, through canonical geometric scale-space analysis, this geometric scale-variability embedded in a range image can be exploited as a rich source of discriminative information regarding the captured geometry. we extend previous work on geometric scale-space analysis of 3d models to analyze the scale-variability of a range image and to detect scale-dependent 3d features --- geometric features with their inherent scales. we derive novel local 3d shape descriptors that encode the local shape information within the inherent support region of each feature. we show that the resulting set of scale-dependent local shape descriptors can be used in an efficient hierarchical registration algorithm for aligning range images with the same global scale. we also show that local 3d shape descriptors invariant to the scale variation can be derived and used to align range images with significantly different global scales. finally, we demonstrate that the scale-dependent/invariant local 3d shape descriptors can even be used to fully automatically register multiple sets of range images with varying global scales corresponding to multiple objects.
automatic generator of minimal problem solvers. finding solutions to minimal problems for estimating epipolar geometry and camera motion leads to solving systems of algebraic equations. often, these systems are not trivial and therefore special algorithms have to be designed to achieve numerical robustness and computational efficiency. the state of the art approach for constructing such algorithms is the gröbner basis method for solving systems of polynomial equations. previously, the gröbner basis solvers were designed ad hoc for concrete problems and they could not be easily applied to new problems. in this paper we propose an automatic procedure for generating gröbner basis solvers which could be used even by non-experts to solve technical problems. the input to our solver generator is a system of polynomial equations with a finite number of solutions. the output of our solver generator is the matlab or c code which computes solutions to this system for concrete coefficients. generating solvers automatically opens possibilities to solve more complicated problems which could not be handled manually or solving existing problems in a better and more efficient way. we demonstrate that our automatic generator constructs efficient and numerically stable solvers which are comparable or outperform known manually constructed solvers. the automatic generator is available at http://cmp.felk.cvut.cz/minimal
segmenting fiber bundles in diffusion tensor images. we consider the problem of segmenting fiber bundles in diffusion tensor images. we cast this problem as a manifold clustering problem in which different fiber bundles correspond to different submanifolds of the space of diffusion tensors. we first learn a local representation of the diffusion tensor data using a generalization of the locally linear embedding (lle) algorithm from euclidean to diffusion tensor data. such a generalization exploits geometric properties of the space of symmetric positive semi-definite matrices, particularly its riemannian metric. then, under the assumption that different fiber bundles are physically distinct, we show that the null space of a matrix built from the local representation gives the segmentation of the fiber bundles. our method is computationally simple, can handle large deformations of the principal direction along the fiber tracts, and performs automatic segmentation without requiring previous fiber tracking. results on synthetic and real diffusion tensor images are also presented.
constrained maximum likelihood learning of bayesian networks for facial action recognition. probabilistic graphical models such as bayesian networks have been increasingly applied to many computer vision problems. accuracy of inferences in such models depends on the quality of network parameters. learning reliable parameters of bayesian networks often requires a large amount of training data, which may be hard to acquire and may contain missing values. on the other hand, qualitative knowledge is available in many computer vision applications, and incorporating such knowledge can improve the accuracy of parameter learning. this paper describes a general framework based on convex optimization to incorporate constraints on parameters with training data to perform bayesian network parameter estimation. for complete data, a global optimum solution to maximum likelihood estimation is obtained in polynomial time, while for incomplete data, a modified expectation-maximization method is proposed. this framework is applied to real image data from a facial action unit recognition problem and produces results that are similar to those of state-of-the-art methods.
view point tracking of rigid objects based on shape sub-manifolds. we study the task to infer and to track the viewpoint onto a 3d rigid object by observing its image contours in a sequence of images. to this end, we consider the manifold of invariant planar contours and learn the low-dimensional submanifold corresponding to the object contours by observing the object off-line from a number of different viewpoints. this submanifold of object contours can be parametrized by the view sphere and, in turn, be used for keeping track of the object orientation relative to the observer, through interpolating samples on the submanifold in a geometrically proper way. our approach replaces explicit 3d object models by the corresponding invariant shape submanifolds that are learnt from a sufficiently large number of image contours, and is applicable to arbitrary objects.
multi-scale improves boundary detection in natural images. in this work we empirically study the multi-scale boundary detection problem in natural images. we utilize local boundary cues including contrast, localization and relative contrast, and train a classifier to integrate them across scales. our approach successfully combines strengths from both large-scale detection (robust but poor localization) and small-scale detection (detail-preserving but sensitive to clutter). we carry out quantitative evaluations on a variety of boundary and object datasets with human-marked groundtruth. we show that multi-scale boundary detection offers large improvements, ranging from 20% to 50%, over single-scale approaches. this is the first time that multi-scale is demonstrated to improve boundary detection on large datasets of natural images.
a probabilistic cascade of detectors for individual object recognition. a probabilistic system for recognition of individual objects is presented. the objects to recognize are composed of constellations of features, and features from a same object share the common reference frame of the image in which they are detected. features appearance and pose are modeled by probabilistic distributions, the parameters of which are shared across features in order to allow training from few examples.in order to avoid an expensive combinatorial search, our recognition system is organized as a cascade of well-established, simple and inexpensive detectors. the candidate hypotheses output by our algorithm are evaluated by a generative probabilistic model that takes into account each stage of the matching process.we apply our ideas to the problem of individual object recognition and test our method on several data-sets. we compare with lowe's algorithm [7] and demonstrate significantly better performance.
sparse long-range random field and its application to image denoising. many recent techniques for low-level vision problems such as image denoising are formulated in terms of markov random field (mrf) or conditional random field (crf) models. nonetheless, the role of the underlying graph structure is still not well understood. on the one hand there are pairwise structures where each node is connected to its local neighbors. these models tend to allow for fast algorithms but do not capture important higher-order statistics of natural scenes. on the other hand there are more powerful models such as field of experts (foe) that consider joint distributions over larger cliques in order to capture image statistics but are computationally challenging. in this paper we consider a graph structure with longer range connections that is designed to both capture important image statistics and be computationally efficient. this structure incorporates long-range connections in a manner that limits the cliques to size 3, thereby capturing important second-order image statistics while still allowing efficient optimization due to the small clique size. we evaluate our approach by testing the models on widely used datasets. the results show that our method is comparable to the current state-of-the-art in terms of psnr, is better at preserving fine-scale detail and producing natural-looking output, and is more than an order of magnitude faster.
learning optical flow. assumptions of brightness constancy and spatial smoothness underlie most optical flow estimation methods. in contrast to standard heuristic formulations, we learn a statistical model of both brightness constancy error and the spatial properties of optical flow using image sequences with associated ground truth flow fields. the result is a complete probabilistic model of optical flow. specifically, the ground truth enables us to model how the assumption of brightness constancy is violated in naturalistic sequences, resulting in a probabilistic model of "brightness inconstancy". we also generalize previous high-order constancy assumptions, such as gradient constancy, by modeling the constancy of responses to various linear filters in a high-order random field framework. these filters are free variables that can be learned from training data. additionally we study the spatial structure of the optical flow and how motion boundaries are related to image intensity boundaries. spatial smoothness is modeled using a steerable random field, where spatial derivatives of the optical flow are steered by the image brightness structure. these models provide a statistical motivation for previous methods and enable the learning of all parameters from training data. all proposed models are quantitatively compared on the middlebury flow dataset.
belief propagation with directional statistics for solving the shape-from-shading problem. the shape-from-shading [sfs] problem infers shape from reflected light, collected using a camera at a single point in space only. reflected light alone does not provide sufficient constraint and extra information is required; typically a smoothness assumption is made. a surface with lambertian reflectance lit by a single infinitely distant light source is also typical.we solve this typical sfs problem using belief propagation to marginalise a probabilistic model. the key novel step is in using a directional probability distribution, the fisher-bingham distribution. this produces a fast and relatively simple algorithm that does an effective job of both extracting details and being robust to noise. quantitative comparisons with past algorithms are provided using both synthetic and real data.
output regularized metric learning with side information. distance metric learning has been widely investigated in machine learning and information retrieval. in this paper, we study a particular content-based image retrieval application of learning distance metrics from historical relevance feedback log data, which leads to a novel scenario called collaborative image retrieval. the log data provide the side information expressed as relevance judgements between image pairs. exploiting the side information as well as inherent neighborhood structures among examples, we design a convex regularizer upon which a novel distance metric learning approach, named output regularized metric learning, is presented to tackle collaborative image retrieval. different from previous distance metric methods, the proposed technique integrates synergistic information from both log data and unlabeled data through a regularization framework and pilots the desired metric toward the ideal output that satisfies pairwise constraints revealed by side information. the experiments on image retrieval tasks have been performed to validate the feasibility of the proposed distance metric technique.
a new baseline for image annotation. automatically assigning keywords to images is of great interest as it allows one to index, retrieve, and understand large collections of image data. many techniques have been proposed for image annotation in the last decade that give reasonable performance on standard datasets. however, most of these works fail to compare their methods with simple baseline techniques to justify the need for complex models and subsequent training. in this work, we introduce a new baseline technique for image annotation that treats annotation as a retrieval problem. the proposed technique utilizes low-level image features and a simple combination of basic distances to find nearest neighbors of a given image. the keywords are then assigned using a greedy label transfer mechanism. the proposed baseline outperforms the current state-of-the-art methods on two standard and one large web dataset. we believe that such a baseline measure will provide a strong platform to compare and better understand future annotation techniques.
efficient ncc-based image matching in walsh-hadamard domain. in this paper, we proposed a fast image matching algorithm based on the normalized cross correlation (ncc) by applying the winner-update strategy on the walsh-hadamard transform. walsh-hadamard transform is an orthogonal transformation that is easy to compute and has nice energy packing capability. based on the cauchy-schwarz inequality, we derive a novel upper bound for the cross-correlation of image matching in the walsh-hadamard domain. applying this upper bound with the winner update search strategy can skip unnecessary calculation, thus significantly reducing the computational burden of ncc-based pattern matching. experimental results show the proposed algorithm is very efficient for ncc-based image matching under different lighting conditions and noise levels.
sift flow: dense correspondence across different scenes. while image registration has been studied in different areas of computer vision, aligning images depicting different scenes remains a challenging problem, closer to recognition than to image matching. analogous to optical flow, where an image is aligned to its temporally adjacent frame, we propose sift flow, a method to align an image to its neighbors in a large image collection consisting of a variety of scenes. for a query image, histogram intersection on a bag-of-visual-words representation is used to find the set of nearest neighbors in the database. the sift flow algorithm then consists of matching densely sampled sift features between the two images, while preserving spatial discontinuities. the use of sift features allows robust matching across different scene/object appearances and the discontinuity-preserving spatial model allows matching of objects located at different parts of the scene. experiments show that the proposed approach is able to robustly align complicated scenes with large spatial distortions. we collect a large database of videos and apply the sift flow algorithm to two applications: (i) motion field prediction from a single static image and (ii) motion synthesis via transfer of moving objects.
serboost: semi-supervised boosting with expectation regularization. the application of semi-supervised learning algorithms to large scale vision problems suffers from the bad scaling behavior of most methods. based on the expectation regularization principle, we propose a novel semi-supervised boosting method, called serboost that can be applied to large scale vision problems. the complexity is mainly dominated by the base learners. the algorithm provides a margin regularizer for the boosting cost function and shows a principled way of utilizing prior knowledge. we demonstrate the performance of serboost on the pascal voc2006 set and compare it to other supervised and semi-supervised methods, where serboost shows improvements both in terms of classification accuracy and computational speed.
a convex formulation of continuous multi-label problems. we propose a spatially continuous formulation of ishikawa's discrete multi-label problem. we show that the resulting non-convex variational problem can be reformulated as a convex variational problem via embedding in a higher dimensional space. this variational problem can be interpreted as a minimal surface problem in an anisotropic riemannian space. in several stereo experiments we show that the proposed continuous formulation is superior to its discrete counterpart in terms of computing time, memory efficiency and metrication errors.
prior-based piecewise-smooth segmentation by template competitive deformation using partitions of unity. we propose a new algorithm for two-phase, piecewise-smooth segmentation with shape prior. the image is segmented by a binary template that is deformed by a regular geometric transformation. the choice of the template together with the constraint on the transformation introduce the shape prior. the deformation is guided by the maximization of the likelihood of foreground and background intensity models, so that we can refer to this approach as competitive deformation. in each region, the intensity is modelled as a smooth approximation of the original image. we represent the transformation using a partition of unity finite element method, which consists in representing each component with polynomial approximations within local patches. a conformity constraint between the patches provides a way to control the globality of the deformation. we show several results on synthetic images, as well as on medical data from different modalities.
a generic neighbourhood filtering framework for matrix fields. the nonlocal data and smoothness (nds) filtering framework for greyvalue images has been recently proposed by mrázek et al. this model for image denoising unifies m-smoothing and bilateral filtering, and several well-known nonlinear filters from the literature become particular cases. in this article we extend this model to so-called matrix fields. these data appear, for example, in diffusion tensor magnetic resonance imaging (dt-mri). our matrix-valued nds framework includes earlier filters developped for dt-mri data, for instance, the affine-invariant and the log-euclidean regularisation of matrix fields. experiments performed with synthetic matrix fields and real dt-mri data showed excellent performance with respect to restoration quality as well as speed of convergence.