waashk/dblp huggingface dataset:
balanced exploration and exploitation model search for efficient epipolar geometry estimation. the estimation of the epipolar geometry is especially difficult when the putative correspondences include a low percentage of inlier correspondences and/or a large subset of the inliers is consistent with a degenerate configuration of the epipolar geometry that is totally incorrect. this work presents the balanced exploration and exploitation model search (beem) algorithm that works very well especially for these difficult scenes. the algorithm handles these two problems in a unified manner. it includes the following main features: (1) balanced use of three search techniques: global random exploration, local exploration near the current best solution and local exploitation to improve the quality of the model. (2) exploits available prior information to accelerate the search process. (3) uses the best found model to guide the search process, escape from degenerate models and to define an efficient stopping criterion. (4) presents a simple and efficient method to estimate the epipolar geometry from two sift correspondences. (5) uses the locality-sensitive hashing (lsh) approximate nearest neighbor algorithm for fast putative correspondences generation. the resulting algorithm when tested on real images with or without degenerate configurations gives quality estimations and achieves significant speedups compared to the state of the art algorithms.
the combinatorics of heuristic search termination for object recognition in cluttered environments. many current recognition systems terminate a search once an interpretation that is good enough is found. the author formally examines the combinatorics of this approach, showing that choosing correct termination procedures can dramatically reduce the search. in particular, the author provides conditions on the object model and the scene clutter such that the expected search is at most quartic. the analytic results are shown to be in agreement with empirical data for cluttered object recognition. these results imply that it is critical to use techniques that select subsets of the data likely to have come from a single object before establishing a correspondence between data and model features.
on the verification of hypthesized matches in model-based recognition. model-based recognition methods generally use ad hoc techniques to decide whether or not a model of an object matches a given scene. the most common such technique is to set an empirically determined threshold on the fraction of model features that must be matched to data features. conditions under which to accept a match as correct are rigorously derived. the analysis is based on modeling the recognition process as a statistical occupancy problem. this model makes the assumption that pairings of object and data features can be characterized as a random process with a uniform distribution. the authors present a number of examples illustrating that real image data are well approximated by such a random process. using a statistical occupancy model, they derive an expression for the probability that a randomly occurring match will account for a given fraction of the features of a particular object. this expression is a function of the number of model features, the number of data features, and bounds on the degree of sensor noise. it provides a means of setting a threshold such that the probability of a random match is very small.
incorporating non-motion cues into 3d motion segmentation. we address the problem of segmenting an image sequence into rigidly moving 3d objects. an elegant solution to this problem in the case of orthographic projection is the multibody factorization approach in which the measurement matrix is factored into lower rank matrices. despite progress in factorization algorithms, their performance is still far from satisfactory and in scenes with missing data and noise, most existing algorithms fail. in this paper we propose a method for incorporating 2d non-motion cues (such as spatial coherence) into multibody factorization. we show the similarity of the problem to constrained factor analysis and use the em algorithm to find the segmentation. we show that adding these cues improves performance in real and synthetic sequences.
video mensuration using a stationary camera. this paper presents a video metrology approach using an uncalibrated single camera that is either stationary or in planar motion. although theoretically simple, measuring the length of even a line segment in a given video is often a difficult problem. most existing techniques for this task are extensions of single image-based techniques and do not achieve the desired accuracy especially in noisy environments. in contrast, the proposed algorithm moves line segments on the reference plane to share a common endpoint using the vanishing line information followed by fitting multiple concentric circles on the image plane. a fully automated real-time system based on this algorithm has been developed to measure vehicle wheelbases using an uncalibrated stationary camera. the system estimates the vanishing line using invariant lengths on the reference plane from multiple frames rather than the given parallel lines, which may not exist in videos. it is further extended to a camera undergoing a planar motion by automatically selecting frames with similar vanishing lines from the video. experimental results show that the measurement results are accurate enough to classify moving vehicles based on their size.
reconstruction from projections using grassmann tensors. in this paper a general procedure is given for reconstruction of a set of feature points in an arbitrary dimensional projective space from their projections into lower dimensional spaces. this extends the methods applied in the well-studied problem of reconstruction of scene points in ¿3 given their projections in a set of images. in this case, the bifocal, trifocal and quadrifocal tensors are used to carry out this computation. it is shown that similar methods will apply in a much more general context, and hence may be applied to projections from ¿ n to ¿ m , which have been used in the analysis of dynamic scenes, and in radial distortion correction. for sufficiently many generic projections, reconstruction of the scene is shown to be unique up to projectivity, except in the case of projections onto one-dimensional image spaces (lines), in which case there are two solutions.projections from ¿ n to ¿2 have been considered by wolf and shashua (in international journal of computer vision 48(1): 53---67, 2002), where they were applied to several different problems in dynamic scene analysis. they analyzed these projections using tensors, but no general way of defining such tensors, and computing the projections was given. this paper settles the general problem, showing that tensor definition and retrieval of the projections is always possible.
confocal stereo. we present confocal stereo, a new method for computing 3d shape by controlling the focus and aperture of a lens. the method is specifically designed for reconstructing scenes with high geometric complexity or fine-scale texture. to achieve this, we introduce the confocal constancy property, which states that as the lens aperture varies, the pixel intensity of a visible in-focus scene point will vary in a scene-independent way, that can be predicted by prior radiometric lens calibration. the only requirement is that incoming radiance within the cone subtended by the largest aperture is nearly constant. first, we develop a detailed lens model that factors out the distortions in high resolution slr cameras (12mp or more) with large-aperture lenses (e.g., f1.2). this allows us to assemble an a×f aperture-focus image (afi) for each pixel, that collects the undistorted measurements over all a apertures and f focus settings. in the afi representation, confocal constancy reduces to color comparisons within regions of the afi, and leads to focus metrics that can be evaluated separately for each pixel. we propose two such metrics and present initial reconstruction results for complex scenes, as well as for a scene with known ground-truth shape.
dreams: deformable regions driven by an eulerian accurate minimization method for image and video segmentation. this paper deals with image and video segmentation using active contours. we propose a general form for the energy functional related to region-based active contours. we compute the associated evolution equation using shape derivation tools and accounting for the evolving region-based terms. then we apply this general framework to compute the evolution equation from functionals that include various statistical measures of homogeneity for the region to be segmented. experimental results show that the determinant of the covariance matrix appears to be a very relevant tool for segmentation of homogeneous color regions. as an example, it has been successfully applied to face segmentation in real video sequences.
robust multiple car tracking with occlusion reasoning. in this work we address the problem of occlusion in tracking multiple 3d objects in a known environment and propose a new approach for tracking vehicles in road traffic scenes using an explicit occlusion reasoning step. we employ a contour tracker based on intensity and motion boundaries. the motion of the contour of the vehicles in the image is assumed to be well describable by an affine motion model with a translation and a change in scale. a vehicle contour is represented by closed cubic splines the position and motion of which is estimated along the image sequence. in order to employ linear kalman filters we decompose the estimation process into two filters: one for estimating the affine motion parameters and one for estimating the shape of the contours of the vehicles. occlusion detection is performed by intersecting the depth ordered regions associated to the objects. the intersection part is then excluded in the motion and shape estimation. this procedure also improves the shape estimation in case of adjacent objects since occlusion detection is performed on slightly enlarged regions. in this way we obtain robust motion estimates and trajectories for vehicles even in the case of occlusions, as we show in some experiments with real world traffic scenes.
what energy functions can be minimized via graph cuts? many problems in computer vision can be naturally phrased in terms of energy minimization. in the last few years researchers have developed a powerful class of energy minimization methods based on graph cuts. these techniques construct a specialized graph, such that the minimum cut on the graph also minimizes the energy. the minimum cut in turn is efficiently computed by max flow algorithms. such methods have been successfully applied to a number of important vision problems, including image restoration, motion, stereo, voxel occupancy and medical imaging. however, each graph construction to date has been highly specific for a particular energy function. in this paper we address a much broader problem, by characterizing the class of energy functions that can be minimized by graph cuts, and by giving a general-purpose construction that minimizes any energy function in this class. our results generalize several previous vision algorithms based on graph cuts, and also show how to minimize an interesting new class of energy functions.
exemplar-based face recognition from video. a new exemplar-based probabilistic approach for face recognition in video sequences is presented. the approach has two stages: first, exemplars, which are selected representatives from the raw video, are automatically extracted from gallery videos. the exemplars are used to summarize the gallery video information.in the second part, exemplars are then used as centers for probabilistic mixture distributions for the tracking and recognition process. probabilistic methods are attractive in this context as they allow a systematic handling of uncertainty and an elegant way for fusing temporal information.contrary to some previous video-based approaches, our approach is not limited to a certain image representation. it rather enhances known ones, such as the pca, with temporal fusion and uncertainty handling. experiments demonstrate the effectiveness of each of the two stages. we tested this approach on more than 100 training and testing sequences, with 25 different individuals.
the relevance of non-generic events in scale space models. in order to investigate the deep structure of gaussian scale space images, one needs to understand the behaviour of spatial critical points under the influence of blurring. we show how the mathematical framework of catastrophe theory can be used to describe and model the behaviour of critical point trajectories when various different types of generic events, viz. annihilations and creations of pairs of spatial critical points, (almost) coincide. although such events are non-generic in mathematical sense, they are not unlikely to be encountered in practice due to numerical limitations. furthermore, the behaviour of these trajectories leads to the observation that fine-to-coarse tracking of critical points doesn't suffice, since they can form closed loops in scale space. the modelling of the trajectories include these loops. we apply the theory to an artificial image and a simulated mr image and show the occurrence of the described behaviour.
ellipse fitting with hyperaccuracy. for fitting an ellipse to a point sequence, ml (maximum likelihood) has been regarded as having the highest accuracy. in this paper, we demonstrate the existence of a "hyperaccurate" method which outperforms ml. this is made possible by error analysis of ml followed by subtraction of high-order bias terms. since ml nearly achieves the theoretical accuracy bound (the kcr lower bound), the resulting improvement is very small. nevertheless, our analysis has theoretical significance, illuminating the relationship between ml and the kcr lower bound.
uncalibrated factorization using a variable symmetric affine camera. in order to reconstruct 3-d euclidean shape by the tomasi-kanade factorization, one needs to specify an affine camera model such as orthographic, weak perspective, and paraperspective. we present a new method that does not require any such specific models. we show that a minimal requirement for an affine camera to mimic perspective projection leads to a unique camera model, called symmetric affine camera, which has two free functions. we determine their values from input images by linear computation and demonstrate by experiments that an appropriate camera model is automatically selected.
learning to combine bottom-up and top-down segmentation. bottom-up segmentation based only on low-level cues is a notoriously difficult problem. this difficulty has lead to recent top-down segmentation algorithms that are based on class-specific image information. despite the success of top-down algorithms, they often give coarse segmentations that can be significantly refined using low-level cues. this raises the question of how to combine both top-down and bottom-up cues in a principled manner.in this paper we approach this problem using supervised learning. given a training set of ground truth segmentations we train a fragment-based segmentation algorithm which takes into account both bottom-up and top-down cues simultaneously, in contrast to most existing algorithms which train top-down and bottom-up modules separately. we formulate the problem in the framework of conditional random fields (crf) and derive a feature induction algorithm for crf, which allows us to efficiently search over thousands of candidate fragments. whereas pure top-down algorithms often require hundreds of fragments, our simultaneous learning procedure yields algorithms with a handful of fragments that are combined with low-level cues to efficiently compute high quality segmentations.
direct evidence for occlusion in stereo and motion. discontinuities of surface properties are the most important locations in a scene; they are crucial for segmentation because they often coincide with object boundaries^1. standard approaches to discontinuity detection decouple detection of disparity discontinuities from disparity computation. we have developed techniques for locating disparity discontinuities using information internal to the stereo algorithm of drumheller and poggio^2 rather than by post-processing the stereo data. the algorithm determines displacements by maximizing the sum, at overlapping small regions, of local comparisons. the detection methods are motivated by analysis of the geometry of matching and occlusion, and the fact that detection is not just a pointwise decision. our methods can be used in combination to produce robust performance. this research is part of a project to build a vision machine^3 at mit that integrates outputs from early vision modules. our techniques have been extensively tested on real images.
elastically adaptive deformable models. we present a novel technique for the automatic adaptation of a deformable model's elastic parameters within a kalman filter framework for shape estimation applications. the novelty of the technique is that the model's elastic parameters are not constant, but spatio-temporally varying. the variation of the elastic parameters depends on the distance of the model from the data and the rate of change of this distance. each pass of the algorithm uses physics-based modeling techniques to iteratively adjust both the geometric and the elastic degrees of freedom of the model in response to forces that are computed from the discrepancy between the model and the data. by augmenting the state equations of an extended kalman filter to incorporate these additional variables, we are able to significantly improve the quality of the shape estimation. therefore, the model's elastic parameters are always initialized to the same value and they are subsequently modified depending on the data and the noise distribution. we present results demonstrating the effectiveness of our method for both two-dimensional and three-dimensional data.
hierarchical image analysis using irregular tessellations. a novel multiresolution image analysis technique based on hierarchies of irregular tessellations generated in parallel by independent stochastic processes is presented. like traditional image pyramids these hierarchies are constructed in a number of steps on the order of log(image-size) steps. however, the structure of a hierarchy is adapted to the image content and artifacts of rigid resolution reduction are avoided. two applications of these techniques are presented: connected component analysis of labeled images and segmentation of gray level images. in labeled images, every connected component is reduced to a separate root, with the adjacency relations among the components also extracted. in gray level images the output is a segmentation of the image into a small number of classes as well as the adjacency graph of the classes.
stereo using monocular cues within the tensor voting framework. we address the fundamental problem of matching in two static images. the remaining challenges are related to occlusion and lack of texture. our approach addresses these difficulties within a perceptual organization framework, considering both binocular and monocular cues. initially, matching candidates for all pixels are generated by a combination of matching techniques. the matching candidates are then embedded in disparity space, where perceptual organization takes place in 3d neighborhoods and, thus, does not suffer from problems associated with scanline or image neighborhoods. the assumption is that correct matches produce salient, coherent surfaces, while wrong ones do not. matching candidates that are consistent with the surfaces are kept and grouped into smooth layers. thus, we achieve surface segmentation based on geometric and not photometric properties. surface overextensions, which are due to occlusion, can be corrected by removing matches whose projections are not consistent in color with their neighbors of the same surface in both images. finally, the projections of the refined surfaces on both images are used to obtain disparity hypotheses for unmatched pixels. the final disparities are selected after a second tensor voting stage, during which information is propagated from more reliable pixels to less reliable ones. we present results on widely used benchmark stereo pairs.
limitations of non model-based recognition schemes. different approaches to visual object recognition can be divided into two general classes: model-based vs. non model-based schemes. in this paper we establish some limitation on the class of non model-based recognition schemes. we show that every function that is invariant to viewing position of all objects is the trivial (constant) function. it follows that every consistent recognition scheme for recognizing all 3-d objects must in general be model based. the result is extended to recognition schemes that are imperfect (allowed to make mistakes) or restricted to certain classes of objects.
assorted pixels: multi-sampled imaging with structural models. multi-sampled imaging is a general framework for using pixels on an image detector to simultaneously sample multiple dimensions of imaging (space, time, spectrum, brightness, polarization, etc.). the mosaic of red, green and blue spectral filters found in most solid-state color cameras is one example of multi-sampled imaging. we briefly describe how multi-sampling can be used to explore other dimensions of imaging. once such an image is captured, smooth reconstructions along the individual dimensions can be obtained using standard interpolation algorithms. typically, this results in a substantial reduction of resolution (and hence image quality). one can extract significantly greater resolution in each dimension by noting that the light fields associated with real scenes have enormous redundancies within them, causing different dimensions to be highly correlated. hence, multi-sampled images can be better interpolated using local structural models that are learned offline from a diverse set of training images. the specific type of structural models we use are based on polynomial functions of measured image intensities. they are very effective as well as computationally efficient. we demonstrate the benefits of structural interpolation using three specific applications. these are (a) traditional color imaging with a mosaic of color filters, (b) high dynamic range monochrome imaging using a mosaic of exposure filters, and (c) high dynamic range color imaging using a mosaic of overlapping color and exposure filters.