waashk/dblp huggingface dataset:
sparse structures in l-infinity norm minimization for structure and motion reconstruction. this paper presents a study on how to numerically solve the feasibility test problem which is the core of the bisection algorithm for minimizing the l &infin; error functions. we consider a strategy that minimizes the maximum infeasibility. the minimization can be performed using several numerical computation methods, among which the barrier method and the primal-dual method are examined. in both of the methods, the inequalities are sequentially approximated by log-barrier functions. an initial feasible solution is found easily by the construction of the feasibility problem, and newton-style update computes the optimal solution iteratively. when we apply the methods to the problem of estimating the structure and motion, every newton update requires solving a very large system of linear equations. we show that the sparse bundle-adjustment technique, previously developed for structure and motion estimation, can be utilized during the newton update. in the primal-dual interior-point method, in contrast to the barrier method, the sparse structure is all destroyed due to an extra constraint introduced for finding an initial solution. however, we show that this problem can be overcome by utilizing the matrix inversion lemma which allows us to exploit the sparsity in the same manner as in the barrier method. we finally show that the sparsity appears in both of the l &infin; formulations - linear programming and second-order cone programming.
online sparse matrix gaussian process regression and vision applications. we present a new gaussian process inference algorithm, called online sparse matrix gaussian processes (osmgp), and demonstrate its merits with a few vision applications. the osmgp is based on the observation that for kernels with local support, the gram matrix is typically sparse. maintaining and updating the sparse cholesky factor of the gram matrix can be done efficiently using givens rotations. this leads to an exact, online algorithm whose update time scales linearly with the size of the gram matrix. further, if approximate updates are permissible, the cholesky factor can be maintained at a constant size using hyperbolic rotations to remove certain rows and columns corresponding to discarded training examples. we demonstrate that, using these matrix downdates, online hyperparameter estimation can be included without affecting the linear runtime complexity of the algorithm. the osmgp algorithm is applied to head-pose estimation and visual tracking problems. experimental results demonstrate that the proposed method is accurate, efficient and generalizes well using online learning.
learning to localize objects with structured output regression. sliding window classifiers are among the most successful and widely applied techniques for object localization. however, training is typically done in a way that is not specific to the localization task. first a binary classifier is trained using a sample of positive and negative examples, and this classifier is subsequently applied to multiple regions within test images. we propose instead to treat object localization in a principled way by posing it as a problem of predicting structured data: we model the problem not as binary classification, but as the prediction of the bounding box of objects located in images. the use of a joint-kernel framework allows us to formulate the training procedure as a generalization of an svm, which can be solved efficiently. we further improve computational efficiency by using a branch-and-bound strategy for localization during both training and testing. experimental evaluation on the pascal voc and tu darmstadt datasets show that the structured training procedure improves performance over binary training as well as the best previously published scores.
robust multiple structures estimation with j-linkage. this paper tackles the problem of fitting multiple instances of a model to data corrupted by noise and outliers. the proposed solution is based on random sampling and conceptual data representation. each point is represented with the characteristic function of the set of random models that fit the point. a tailored agglomerative clustering, called j-linkage, is used to group points belonging to the same model. the method does not require prior specification of the number of models, nor it necessitate parameters tuning. experimental results demonstrate the superior performances of the algorithm.
something old, something new, something borrowed, something blue. my first paper of a "computer vision" signature (on invariants related to optic flow) dates from 1975. i have published in computer vision (next to work in cybernetics, psychology, physics, mathematics and philosophy) till my retirement earlier this year (hence the slightly blue feeling), thus my career roughly covers the history of the field. "vision" has diverse connotations. the fundamental dichotomy is between "optically guided action" and "visual experience". the former applies to much of biology and computer vision and involves only concepts from science and engineering (e.g., "inverse optics"), the latter involves intention and meaning and thus additionally involves concepts from psychology and philosophy. david marr's notion of "vision" is an uneasy blend of the two: on the one hand the goal is to create a "representation of the scene in front of the eye" (involving intention and meaning), on the other hand the means by which this is attempted are essentially "inverse optics". although this has nominally become something of the "standard model" of cv, it is actually incoherent. it is the latter notion of "vision" that has always interested me most, mainly because one is still grappling with basic concepts. it has been my aspiration to turn it into science, although in this i failed. yet much has happened (something old) and is happening now (something new). i will discuss some of the issues that seem crucial to me, mostly illustrated through my own work, though i shamelessly borrow from friends in the cv community where i see fit.
a segmentation based variational model for accurate optical flow estimation. segmentation has gained in popularity in stereo matching. however, it is not trivial to incorporate it in optical flow estimation due to the possible non-rigid motion problem. in this paper, we describe a new optical flow scheme containing three phases. first, we partition the input images and integrate the segmentation information into a variational model where each of the segments is constrained by an affine motion. then the errors brought in by segmentation are measured and stored in a confidence map. the final flow estimation is achieved through a global optimization phase that minimizes an energy function incorporating the confidence map. extensive experiments show that the proposed method not only produces quantitatively accurate optical flow estimates but also preserves sharp motion boundaries, which makes the optical flow result usable in a number of computer vision applications, such as image/video segmentation and editing.
robust optimal pose estimation. we study the problem of estimating the position and orientation of a calibrated camera from an image of a known scene. a common problem in camera pose estimation is the existence of false correspondences between image features and modeled 3d points. existing techniques such as ransac to handle outliers have no guarantee of optimality. in contrast, we work with a natural extension of the l &infin; norm to the outlier case. using a simple result from classical geometry, we derive necessary conditions for l &infin; optimality and show how to use them in a branch and bound setting to find the optimum and to detect outliers. the algorithm has been evaluated on synthetic as well as real data showing good empirical performance. in addition, for cases with no outliers, we demonstrate shorter execution times than existing optimal algorithms.
integration of multiview stereo and silhouettes via convex functionals on convex domains. we propose a convex framework for silhouette and stereo fusion in 3d reconstruction from multiple images. the key idea is to show that the reconstruction problem can be cast as one of minimizing a convex functional where the exact silhouette consistency is imposed as a convex constraint that restricts the domain of admissible functions. as a consequence, we can retain the original stereo-weighted surface area as a cost functional without heuristic modifications by balloon terms or other strategies, yet still obtain meaningful (nonempty) global minimizers. compared to previous methods, the introduced approach does not depend on initialization and leads to a more robust numerical scheme by removing the bias near the visual hull boundary. we propose an efficient parallel implementation of this convex optimization problem on a graphics card. based on a photoconsistency map and a set of image silhouettes we are therefore able to compute highly-accurate and silhouette-consistent reconstructions for challenging real-world data sets in less than one minute.
shape matching by segmentation averaging. we use segmentations to match images by shape. to address the unreliability of segmentations, we give a closed form approximation to an average over all segmentations. our technique has many extensions, yielding new algorithms for tracking, object detection, segmentation, and edge-preserving smoothing. for segmentation, instead of a maximum a posteriori approach, we compute the "central" segmentation minimizing the average distance to all segmentations of an image. our methods for segmentation and object detection perform competitively, and we also show promising results in tracking and edge---preserving smoothing.
analysis of building textures for reconstructing partially occluded facades. as part of an architectural modeling project, this paper investigates the problem of understanding and manipulating images of buildings. our primary motivation is to automatically detect and seamlessly remove unwanted foreground elements from urban scenes. without explicit handling, these objects will appear pasted as artifacts on the model. recovering the building facade in a video sequence is relatively simple because parallax induces foreground/background depth layers, but here we consider static images only. we develop a series of methods that enable foreground removal from images of buildings or brick walls. the key insight is to use a priori knowledge about grid patterns on building facades that can be modeled as near regular textures (nrt). we describe a markov random field (mrf) model for such textures and introduce a markov chain monte carlo (mcmc) optimization procedure for discovering them. this simple spatial rule is then used as a starting point for inference of missing windows, facade segmentation, outlier identification, and foreground removal.
efficient dense scene flow from sparse or dense stereo data. this paper presents a technique for estimating the three-dimensional velocity vector field that describes the motion of each visible scene point (scene flow). the technique presented uses two consecutive image pairs from a stereo sequence. the main contribution is to decouple the position and velocity estimation steps, and to estimate dense velocities using a variational approach. we enforce the scene flow to yield consistent displacement vectors in the left and right images. the decoupling strategy has two main advantages: firstly, we are independent in choosing a disparity estimation technique, which can yield either sparse or dense correspondences, and secondly, we can achieve frame rates of 5 fps on standard consumer hardware. the approach provides dense velocity estimates with accurate results at distances up to 50 meters.
similarity features for facial event analysis. each facial event will give rise to complex facial appearance variation. in this paper, we propose similarity features to describe the facial appearance for video-based facial event analysis. inspired by the kernel features, for each sample, we compare it with the reference set with a similarity function, and we take the log-weighted summarization of the similarities as its similarity feature. due to the distinctness of the apex images of facial events, we use their cluster-centers as the references. in order to capture the temporal dynamics, we use the k-means algorithm to divide the similarity features into several clusters in temporal domain, and each cluster is modeled by a gaussian distribution. based on the gaussian models, we further map the similarity features into dynamic binary patterns to handle the issue of time resolution, which embed the time-warping operation implicitly. the haar-like descriptor is used to extract the visual features of facial appearance, and adaboost is performed to learn the final classifiers. extensive experiments carried on the cohn-kanade database show the promising performance of the proposed method.
estimating 3d face model and facial deformation from a single image based on expression manifold optimization. facial expression modeling is central to facial expression recognition and expression synthesis for facial animation. previous works reported that modeling the facial expression with low-dimensional manifold is more appropriate than using a linear subspace. in this paper, we propose a manifold-based 3d face reconstruction approach to estimating the 3d face model and the associated expression deformation from a single face image. in the training phase, we build a nonlinear 3d expression manifold from a large set of 3d facial expression models to represent the facial shape deformations due to facial expressions. then a gaussian mixture model in this manifold is learned to represent the distribution of expression deformation. by combining the merits of morphable neutral face model and the low-dimensional expression manifold, we propose a new algorithm to reconstruct the 3d face geometry as well as the 3d shape deformation from a single face image with expression in an energy minimization framework. experimental results on cmu-pie image database and fg-net video database are shown to validate the effectiveness and accuracy of the proposed algorithm.
simultaneous motion detection and background reconstruction with a mixed-state conditional markov random field. we consider the problem of motion detection by background subtraction. an accurate estimation of the background is only possible if we locate the moving objects; meanwhile, a correct motion detection is achieved if we have a good available background model. this work proposes a new direction in the way such problems are considered. the main idea is to formulate this class of problem as a joint decision-estimation unique step. the goal is to exploit the way two processes interact, even if they are of a dissimilar nature (symbolic-continuous), by means of a recently introduced framework called mixed-state markov random fields. in this paper, we will describe the theory behind such a novel statistical framework, that subsequently will allows us to formulate the specific joint problem of motion detection and background reconstruction. experiments on real sequences and comparisons with existing methods will give a significant support to our approach. further implications for video sequence inpainting will be also discussed.
modeling and recognition of landmark image collections using iconic scene graphs. this paper presents an approach for modeling landmark sites such as the statue of liberty based on large-scale contaminated image collections gathered from the internet. our system combines 2d appearance and 3d geometric constraints to efficiently extract scene summaries, build 3d models, and recognize instances of the landmark in new test images. we start by clustering images using low-dimensional global "gist" descriptors. next, we perform geometric verification to retain only the clusters whose images share a common 3d structure. each valid cluster is then represented by a single iconic view, and geometric relationships between iconic views are captured by an iconic scene graph. in addition to serving as a compact scene summary, this graph is used to guide structure from motion to efficiently produce 3d models of the different aspects of the landmark. the set of iconic images is also used for recognition, i.e., determining whether new test images contain the landmark. results on three data sets consisting of tens of thousands of images demonstrate the potential of the proposed approach.
estimating geo-temporal location of stationary cameras using shadow trajectories. using only shadow trajectories of stationary objects in a scene, we demonstrate that using a set of six or more photographs are sufficient to accurately calibrate the camera. moreover, we present a novel application where, using only three points from the shadow trajectory of the objects, one can accurately determine the geo-location of the camera, up to a longitude ambiguity, and also the date of image acquisition without using any gps or other special instruments. we refer to this as "geo-temporal localization". we consider possible cases where ambiguities can be removed if additional information is available. our method does not require any knowledge of the date or the time when the pictures are taken, and geo-temporal information is recovered directly from the images. we demonstrate the accuracy of our technique for both steps of calibration and geo-temporal localization using synthetic and real data.
search space reduction for mrf stereo. we present an algorithm to reduce per-pixel search ranges for markov random fields-based stereo algorithms. our algorithm is based on the intuitions that reliably matched pixels need less regularization in the energy minimization and neighboring pixels should have similar disparity search ranges if their pixel values are similar. we propose a novel bi-labeling process to classify reliable and unreliable pixels that incorporate left-right consistency checks. we then propagate the reliable disparities into unreliable regions to form a complete disparity map and construct per-pixel search ranges based on the difference between the disparity map after propagation and the one computed from a winner-take-all method. experimental results evaluated on the middlebury stereo benchmark show our proposed algorithm is able to achieve 77% average reduction rate while preserving satisfactory accuracy.
brain hallucination. in this paper, we investigate brain hallucination, or generating a high resolution brain image from an input low-resolution image, with the help of another high resolution brain image. contrary to interpolation techniques, the reconstruction process is based on a physical model of image acquisition. our contribution is a new regularization approach that uses an example-based framework integrating non-local similarity constraints to handle in a better way repetitive structures and texture. the effectiveness of our approach is demonstrated by experiments on realistic magnetic resonance brain images generating automatically high-quality hallucinated brain images from low-resolution input.
reformulating and optimizing the mumford-shah functional on a graph - a faster, lower energy solution. active contour formulations predominate current minimization of the mumford-shah functional (msf) for image segmentation and filtering. unfortunately, these formulations necessitate optimization of the contour by evolving via gradient descent, which is known for its sensitivity to initialization and the tendency to produce undesirable local minima. in order to reduce these problems, we reformulate the corresponding msf on an arbitrary graph and apply combinatorial optimization to produce a fast, low-energy solution. the solution provided by this graph formulation is compared with the solution computed via traditional narrow-band level set methods. this comparison demonstrates that our graph formulation and optimization produces lower energy solutions than gradient descent based contour evolution methods in significantly less time. finally, by avoiding evolution of the contour via gradient descent, we demonstrate that our optimization of the msf is capable of evolving the contour with non-local movement.
helmholtz stereopsis: exploiting reciprocity for surface reconstruction. we present a method&mdash;termed helmholtz stereopsis&mdash;for reconstructing the geometry of objects from a collection of images. unlike existing methods for surface reconstruction (e.g., stereo vision, structure from motion, photometric stereopsis), helmholtz stereopsis makes no assumptions about the nature of the bidirectional reflectance distribution functions (brdfs) of objects. this new method of multinocular stereopsis exploits helmholtz reciprocity by choosing pairs of light source and camera positions that guarantee that the ratio of the emitted radiance to the incident irradiance is the same for corresponding points in the two images. the method provides direct estimates of both depth and surface normals, and consequently weds the advantages of both conventional stereopsis and photometric stereopsis. results from our implementation lend empirical support to our technique.
floor fields for tracking in high density crowd scenes. this paper presents an algorithm for tracking individual targets in high density crowd scenes containing hundreds of people. tracking in such a scene is extremely challenging due to the small number of pixels on the target, appearance ambiguity resulting from the dense packing, and severe inter-object occlusions. the novel tracking algorithm, which is outlined in this paper, will overcome these challenges using a scene structure based force model. in this force model an individual, when moving in a particular scene, is subjected to global and local forces that are functions of the layout of that scene and the locomotive behavior of other individuals in the scene. the key ingredients of the force model are three floor fields, which are inspired by the research in the field of evacuation dynamics, namely static floor field (sff), dynamic floor field (dff), and boundary floor field (bff). these fields determine the probability of move from one location to another by converting the long-range forces into local ones. the sff specifies regions of the scene which are attractive in nature (e.g. an exit location). the dff specifies the immediate behavior of the crowd in the vicinity of the individual being tracked. the bff specifies influences exhibited by the barriers in the scene (e.g. walls, no-go areas). by combining cues from all three fields with the available appearance information, we track individual targets in high density crowds.
rank classification of linear line structure in determining trifocal tensor. the problem we address is: given line correspondences over three views, what is the condition of the line correspondences for the spatial relation of the three associated camera positions to be uniquely recoverable? we tackle the problem from the perspective of trifocal tensor, a quantity that captures the relative positions of the cameras in relation to the three views. we show that the rank of the matrix that leads to the estimation of the tensor reduces to 7, 11, 15 respectively for line pencil, point star, and ruled plane, which are structures that belong to linear line space; and 12, 19, 23 for general ruled surface, general linear congruence, and general linear line complex. these critical structures are quite typical in reality, and thus the findings are important to the validity and stability of practically all algorithms related to structure from motion and projective reconstruction using line correspondences.