waashk/dblp huggingface dataset:
a hierarchical field framework for unified context-based classification. we present a two-layer hierarchical formulation to exploit different levels of contextual information in images for robust classification. each layer is modeled as a conditional field that allows one to capture arbitrary observation-dependent label interactions. the proposed framework has two main advantages. first, it encodes both the short-range interactions (e.g., pixelwise label smoothing) as well as the long-range interactions (e.g., relative configurations of objects or regions) in a tractable manner. second, the formulation is general enough to be applied to different domains ranging from pixelwise image labeling to contextual object detection. the parameters of the model are learned using a sequential maximum-likelihood approximation. the benefits of the proposed framework are demonstrated on four different datasets and comparison results are presented.
an integrated stereo-based approach to automatic vehicle guidance. proposes a new approach for vision-based longitudinal and lateral vehicle control. the novel feature of this approach is the use of binocular vision. we integrate two modules consisting of a new, domain-specific, efficient binocular stereo algorithm, and a lane marker detection algorithm, and show that the integration results in a improved performance for each of the modules. longitudinal control is supported by detecting and measuring the distances to leading vehicles using binocular stereo. the knowledge of the camera geometry with respect to the locally planar road is used to map the images of the road plane in the two camera views into alignment. this allows us to separate image features into those lying in the road plane, e.g. lane markers, and those due to other objects which are dynamically integrated into an obstacle map. therefore, in contrast with the previous work, we can cope with the difficulties arising from occlusion of lane markers by other vehicles. the detection and measurement of the lane markers provides us with the positional parameters and the road curvature which are needed for lateral vehicle control. moreover, this information is also used to update the camera geometry with respect to the road, therefore allowing us to cope with the problem of vibrations and road inclination to obtain consistent results from binocular stereo.
learning layered motion segmentation of video. we present an unsupervised approach for learning a layered representation of a scene from a video for motion segmentation. our method is applicable to any video containing piecewise parametric motion. the learnt model is a composition of layers, which consist of one or more segments. the shape of each segment is represented using a binary matte and its appearance is given by the rgb value for each point belonging to the matte. included in the model are the effects of image projection, lighting, and motion blur. furthermore, spatial continuity is explicitly modeled resulting in contiguous segments. unlike previous approaches, our method does not use reference frame(s) for initialization. the two main contributions of our method are: (i) a novel algorithm for obtaining the initial estimate of the model by dividing the scene into rigidly moving components using efficient loopy belief propagation; and (ii) refining the initial estimate using ¿ β-swap and ¿-expansion algorithms, which guarantee a strong local minima. results are presented on several classes of objects with different types of camera motion, e.g. videos of a human walking shot with static or translating cameras. we compare our method with the state of the art and demonstrate significant improvements.
affine surface reconstruction by purposive viewpoint control. we present an approach for building an affine representation of an unknown curved object viewed under orthographic projection from images of its occluding contour. it is based on the observation that the projection of a point on a curved, featureless surface can be computed along a special viewing direction that does not belong to the point's tangent plane. we show that by circumnavigating the object on the tangent plane of selected surface points, we can (1) compute two orthogonal projections of every point projecting to the occluding contour during this motion, and (2) compute the affine coordinates of these points. our approach demonstrates that affine shape of curved objects can be computed directly, i.e., without euclidean calibration or image velocity and acceleration measurements.
edge-based rich representation for vehicle classification. in this paper we propose an approach to vehicle classification under a mid-field surveillance framework. we develop a repeatable and discriminative feature based on edge points and modified sift descriptors, and introduce a rich representation for object classes. experimental results show the proposed approach is promising for vehicle classification in surveillance videos despite great challenges such as limited image size and quality and large intra-class variations. comparisons demonstrate the proposed approach outperforms other methods.
geometric invariants and applications under catadioptric camera model. this paper presents geometric invariants of points and their applications under central catadioptric camera model. although the image has severe distortions under the model, we establish some accurate projective geometric invariants of scene points and their image points. these invariants, being functions of principal point, are useful, from which a method for calibrating the camera principal point and a method for recovering planar scene structures are proposed. the main advantage of using these invariants for plane reconstruction is that neither camera motion nor the intrinsic parameters, except for the principal point, is needed. the theoretical correctness of the established invariants and robustness of the proposed methods are demonstrated by experiments. in addition, our results are found to be applicable to some more general camera models other than the catadioptric one.
a theory of refractive and specular 3d shape by light-path triangulation. we investigate the feasibility of reconstructing an arbitrarily-shaped specular scene (refractive or mirror-like) from one or more viewpoints. by reducing shape recovery to the problem of reconstructing individual 3d light paths that cross the image plane, we obtain three key results. first, we show how to compute the depth map of a specular scene from a single viewpoint, when the scene redirects incoming light just once. second, for scenes where incoming light undergoes two refractions or reflections, we show that three viewpoints are sufficient to enable reconstruction in the general case. third, we show that it is impossible to reconstruct individual light paths when light is redirected more than twice. our analysis assumes that, for every point on the image plane, we know at least one 3d point on its light path. this leads to reconstruction algorithms that rely on an "environment matting" procedure to establish pixel-to-point correspondences along a light path. preliminary results for a variety of scenes (mirror, glass, etc.) are also presented.
euclidean reconstruction and reprojection up to subgroups. the necessary and sufficient conditions for being able to estimate scene structure, motion and camera calibration from a sequence of images are very rarely satisfied in practice. what exactly can be estimated in sequences of practical importance, when such conditions are not satisfied? in this paper we give a complete answer to this question. for every camera motion that fails to meet the conditions, we give explicit formulas for the ambiguities in the reconstructed scene, motion and calibration. such a characterization is crucial both for designing robust estimation algorithms (that do not try to recover parameters that cannot be recovered), and for generating novel views of the scene by controlling the vantage point. to this end, we characterize explicitly all the vantage points that give rise to a valid euclidean reprojection regardless of the ambiguity in the reconstruction. we also characterize vantage points that generate views that are altogether invariant to the ambiguity. all the results are presented using simple notation that involves no tensors nor complex projective geometry, and should be accessible with basic background in linear algebra.
a theory of shape by space carving. in this paper we consider the problem of computing the 3d shape of an unknown, arbitrarily-shaped scene from multiple photographs taken at known but arbitrarily-distributed viewpoints. by studying the equivalence class of all 3d shapes that reproduce the input photographs, we prove the existence of a special member of this class, the photo hull, that (1) can be computed directly from photographs of the scene, and (2) subsumes all other members of this class. we then give a provably-correct algorithm, called space carving, for computing this shape and present experimental results on complex real-world scenes. the approach is designed to (1) capture photorealistic shapes that accurately model scene appearance from a wide range of viewpoints, and (2) account for the complex interactions between occlusion, parallax, shading, and their view-dependent effects on scene-appearance.
a probabilistic contour discriminant for object localisation. a method of localising objects in images is proposed. possible configurations are evaluated using the contour discriminant, a likelihood ratio which is derived from a probabilistic model of the feature detection process. we treat each step in this process probabilistically, including the occurrence of clutter features, and derive the observation densities for both correct "target" configurations and incorrect "clutter" configurations. the contour discriminant distinguishes target objects from the background even in heavy clutter, making only the most general assumptions about the form that clutter might take. the method generates samples stochastically to avoid the cost of processing an entire image, and promises to be particularly suited to the task of initialising contour trackers based on sampling methods.
hierarchical statistical models for the fusion of multiresolution image data. this paper presents a class of nonlinear hierarchical algorithms for the fusion of multiresolution image data in low-level vision. the approach combines nonlinear causal markov models defined on hierarchical graph structures, with standard bayesian estimation theory. two random processes defined on simple hierarchical graphs (quadtrees or "ternary graphs") are introduced to represent the multiresolution observations at hand and the hidden labels to be estimated. an optimal algorithm (inspired from the viterbi algorithm) is developed to compute the bayesian estimates on the hierarchical graph structures. estimates are obtained within two passes on the graph structure. this algorithm is non-iterative and yields a per pixel computational complexity which is independent of image size. this approach is compared to the multiscale algorithm proposed by (bouman et al., 1994) for single-resolution image segmentation (that we have extended for multiresolution data fusion).
a probabilistic exclusion principle for tracking multiple objects. tracking multiple targets is a challenging problem, especially when the targets are &ldquo;identical&rdquo;, in the sense that the same model is used to describe each target. in this case, simply instantiating several independent 1-body trackers is not an adequate solution, because the independent trackers tend to coalesce onto the best-fitting target. this paper presents an observation density for tracking which solves this problem by exhibiting a probabilistic exclusion principle. exclusion arises naturally from a systematic derivation of the observation density, without relying on heuristics. another important contribution of the paper is the presentation of partitioned sampling, a new sampling method for multiple object tracking. partitioned sampling avoids the high computational load associated with fully coupled trackers, while retaining the desirable properties of coupling.
morphological corner detection. this paper presents a new operater for corner detection. this operator uses a variant of the morphological closing operator, which we have called asymmetrical closing. it consists of the successive application of different morphological transformations using different structuring elements. each of these structuring elements used to probe the image under study is tuned to affect corners of different orientation and brightness. we found that this kind of approach, based on brightness comparisons, leads to better quality results than others and is achieved at a lower computational cost.
removal of translation bias when using subspace methods. given estimates of the motion field (optic flow) from an image sequence, it is possible to recover translational direction, ~ t , using a variety of techniques. one such technique, known as "subspace methods," generates constraints which are perpendicular to ~ t , so that two distinct constraints allow a solution for ~ t . in practice many constraints are used in a least-squares solution, but it has been observed that the recovered estimates for ~ t are biased towards the optical axis. while the cause of the bias is well known, previous attempts to remove it have been awed. this paper outlines a new method which removes the bias. the technique is simple to apply and computationally efficient.
beyond trees: common-factor models for 2d human pose recovery. tree structured models have been widely used for determining the pose of a human body, from either 2d or 3d data. while such models can effectively represent the kinematic constraints of the skeletal structure, they do not capture additional constraints such as coordination of the limbs. tree structured models thus miss an important source of information about human body pose, as limb coordination is necessary for balance while standing, walking, or running, as well as being evident in other activities such as dancing and throwing. in this paper we consider the use of undirected graphical models that augment a tree structure with latent variables in order to account for coordination between limbs. we refer to these as common-factor models, since they are constructed by using factor analysis to identify additional correlations in limb position that are not accounted for by the kinematic tree structure. these common-factor models have an underlying tree structure and thus a variant of the standard viterbi algorithm for a tree can be applied for efficient estimation. we present some experimental results contrasting common-factor models with tree models, and quantify the improvement in pose estimation for 2d image data.
surface reflectance modeling of real objects with interreflections. in mixed reality, especially in augmented virtuality which virtualizes real objects, it is important to estimate object surface reflectance properties to render the objects under arbitrary illumination conditions. though several methods have been explored to estimate the surface reflectance properties, it is still difficult to estimate surface reflectance parameters faithfully for complex objects which have non-uniform surface reflectance properties and exhibitinter reflections. this paper describes a new method for densely estimating non-uniform surface reflectance properties of real objects constructed of convex and concave surfaces with interreflections. we use registered range and surface color texture images obtained by a laser range finder. experiments show the usefulness of the proposed method.
a unified approach to coding and interpreting face images. face images are difficult to interpret because they are highly variable. sources of variability include individual appearance, 3d pose, facial expression and lighting. we describe a compact parametrised model of facial appearance which takes into account all these sources of variability. the model represents both shape and grey-level appearance and is created by performing a statistical analysis over a training set of face images. a robust multi-resolution search algorithm is used to fit the model to faces in new images. this allows the main facial features to be located and a set of shape and grey-level appearance parameters to be recovered. a good approximation to a given face can be reconstructed using less than 100 of these parameters. this representation can be used for tasks such as image coding, person identification, pose recovery, gender recognition and expression recognition. the system performs well on all the tasks listed above.
minimum risk distance measure for object recognition. the optimal distance measure for a given discrimination task under the nearest neighbor framework has been shown to be the likelihood that a pair of measurements have different class labels [5]. for implementation and efficiency considerations, the optimal distance measure was approximated by combining more elementary distance measures defined on simple feature spaces. in this paper, we address two important issues that arise in practice for such an approach:(a) what form should the elementary distance measure in each feature space take? we motivate the need to use the optimal distance measure in simple feature spaces as the elementary distance measures; such distance measures have the desirable property that they are invariant to distance-respecting transformations. (b) how do we combine the elementary distance measures? we present the precise statistical assumptions under which a linear logistic model holds exactly. we benchmark our model with three other methods on a challenging face discrimination task and show that our approach is competitive with the state of the art.
periodic motion detection and segmentation via approximate sequence alignment. a method for defecting and segmenting periodic motion is presented. we exploit periodicity as a cite and detect periodic motion in complex scenes where common methods for rnotion segmentation are likely to fail. we note that periodic motion detection can be seen as an approximate case of sequence alignment where an image sequence is matched to itself over one or more periods of time. to use this observation, we first consider alignment of two video sequences obtained by independently moving cameras. under assumption of constant translation, the fundamental matrices and the homographies are shown to be time-linear matrix functions. these dynamic quantities can be estimated by matching corresponding space-time points with similar local motion and shape. for periodic motion, we match corresponding points across periods and develop a ransac procedure to simultaneously estimate the period and the dynamic geometric transformations between periodic views. using this method, we demonstrate detection and segmentation of human periodic motion in complex scenes with non-rigid backgrounds, moving camera and motion parallax.
segmentation of salient closed contours from real images. using a saliency measure based on the global property of contour closure, we have developed a method that reliably segments out salient contours bounding unknown objects from real edge images. the measure also incorporates the gestalt principles of proximity and smooth continuity that previous methods have exploited. unlike previous measures, we incorporate contour closure by finding the eigen-solution associated with a stochastic process that models the distribution of contours passing through edges in the scene. the segmentation algorithm utilizes the saliency measure to identify multiple closed contours by finding strongly- connected components on an induced graph. the determination of strongly connected components is a direct consequence of the property of closure. we report for the first time, results on large real images for which segmentation takes an average of about 10 secs per object on a general-purpose workstation. the segmentation is made efficient for such large images by exploiting the inherent symmetry in the task.
space-time interest points. local image features or interest points provide compact and abstract representations of patterns in an image. in this paper, we propose to extend the notion of spatial interest points into the spatio-temporal domain and show how the resulting features often reflect interesting events that can be used for a compact representation of video data as well as for its interpretation. to detect spatio-temporal events, we build on the idea of the harris and f&ouml;rstner interest point operators and detect local structures in space-time where the image values have significant local variations in both space and time. we then estimate the spatio-temporal extents of the detected events and compute their scale-invariant spatio-temporal descriptors. using such descriptors, we classify events and construct video representation in terms of labeled space-time points. for the problem of human motion analysis, we illustrate how the proposed method allows for detection of walking people in scenes with occlusions and dynamic back-grounds.
geotensity: combining motion and lighting for 3d surface reconstruction. this paper is about automatically reconstructing the full 3d surface of an object observed in motion by a single static camera. we introduce the geotensity constraint that governs the relationship between four images of a moving object under fairly general lighting conditions. we show that it is possible in theory to solve for 3d surface structure for both the case of a single point light source and a pair of point light sources and propose that a solution exists for an arbitrary number point light sources. the surface may or may not be textured. we then give an example of automatic surface reconstruction of a face under a point light source. the geotensity constraint provides the theoretical foundation for the full automatic 3d reconstruction of lambertian objects using a single fixed camera and arbitrary unknown object motion under arbitrary lighting conditions.
textons, contours and regions: cue integration in image segmentation. this paper makes two contributions. it provides (1) an operational definition of textons, the putative elementary units of texture perception, and (2) an algorithm for partitioning the image into disjoint regions of coherent bright-ness and texture, where boundaries of regions are defined by peaks in contour orientation energy and differences in texton densities across the contour.julesz introduced the term texton, analogous to a phoneme in speech recognition, but did not provide an operational definition for gray-level images. here we re-invent textons as frequently co-occurring combinations of oriented linear filter outputs. these can be learned using a k-means approach. by mapping each pixel to its nearest texton, the image can be analyzed into texton channels, each of which is a point set where discrete techniques such as voronoi diagrams become applicable.local histograms of texton frequencies can be used with a x2 test for significant differences to find texture boundaries. natural images contain both textured and untextured regions, so we combine this cue with that of the presence of peaks of contour energy derived from outputs of odd- and even-symmetric oriented gaussian derivative filters. each of these cues has a domain of applicability, so to facilitate cue combination we introduce a gating operator based on a statistical test for isotropy of delaunay neighbors. having obtained a local measure of how likely two nearby pixels are to belong to the same region, we use the spectral graph theoretic framework of normalized cuts to find partitions of the image into regions of coherent texture and brightness. experimental results on a wide range of images are shown.