waashk/dblp huggingface dataset:
recognition of group activities using dynamic probabilistic networks. dynamic probabilistic networks (dpns) are exploitedfor modelling the temporal relationships among a set of differentobject temporal events in the scene for a coherentand robust scene-level behaviour interpretation. in particular,we develop a dynamically multi-linked hidden markovmodel (dml-hmm) to interpret group activities involvingmultiple objects captured in an outdoor scene. the model isbased on the discovery of salient dynamic interlinks amongmultiple temporal events using dpns. object temporalevents are detected and labelled using gaussian mixturemodels with automatic model order selection. a dml-hmmis built using schwarz's bayesian information criterionbased factorisation resulting in its topology being intrinsicallydetermined by the underlying causality and temporalorder among different object events. our experimentsdemonstrate that its performance on modelling group activitiesin a noisy outdoor scene is superior compared to thatof a multi-observation hidden markov model (mohmm),a parallel hidden markov model (pahmm) and a coupledhidden markov model (chmm).
fast stereo matching using reliability-based dynamic programming and consistency constraints. a method for solving binocular and multi-view stereomatching problems is presented in this paper. a weakconsistency constraint is proposed, which expresses thevisibility constraint in the image space. it can be provedthat the weak consistency constraint holds for scenes thatcan be represented by a set of 3d points. as well, alsoproposed is a new reliability measure for dynamicprogramming techniques, which evaluates the reliability ofa given match. a novel reliability-based dynamicprogramming algorithm is derived accordingly, which canselectively assign disparity values to pixels when thereliabilities of the corresponding matches exceed a giventhreshold. consistency constraints and the new reliability-baseddynamic programming algorithm can be combinedin an iterative approach. the experimental results showthat the iterative approach can produce dense (60~90%)and reliable (total error rate of 0.1~1.1%) matching forbinocular stereo datasets. it can also generate promisingdisparity maps for trinocular and multi-view stereodatasets.
applying the information bottleneck principle to unsupervised clustering of discrete and continuous image representations. in this paper we present a method for unsupervised clustering of image databases. the method is based on a recently introduced information-theoretic principle, the information bottleneck (ib) principle. image archives are clustered such that the mutual information between the clusters and the image content is maximally preserved. the ib principle is applied to both discrete and continuous image representations, using discrete image histograms and probabilistic continuous image modeling based on mixture of gaussian densities, respectively. experimental results demonstrate the performance of the proposed method forimage clustering on a large image database. several clustering algorithms derived from the ib principle are explored and compared.
recovery of epipolar geometry as a manifold fitting problem. the introduction of the joint image manifold allows to treatthe problem of recovering camera motion and epipolar geometryas the problem of fitting a manifold to the data measuredin a stereo pair. the manifold has a singularity andboundary, therefore care must be taken when fitting it.this paper reviews the notion of joint image manifold,and how previous motion recovery methods can be viewedin its context, and then offers a new fitting method, whichimproves upon previous results, especially when the extentof the data and/or the motion are small.
the pyramid match kernel: discriminative classification with sets of image features. discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. kernel-based classification methods can learn complex decision boundaries, but a kernel over unordered set inputs must somehow solve for correspondences ¿ generally a computationally expensive task that becomes impractical for large set sizes. we present a new fast kernel function which maps unordered feature sets to multi-resolution histograms and computes a weighted histogram intersection in this space. this "pyramid match" computation is linear in the number of features, and it implicitly finds correspondences based on the finest resolution histogram cell where a matched pair first appears. since the kernel does not penalize the presence of extra features, it is robust to clutter. we show the kernel function is positive-definite, making it valid for use in learning algorithms whose optimal solutions are guaranteed only for mercer kernels. we demonstrate our algorithm on object recognition tasks and show it to be accurate and dramatically faster than current approaches.
inferring 3d structure with a statistical image-based shape model. we present an image-based approach to infer 3d structureparameters using a probabilistic "shape+structure" model.the 3d shape of an object class is represented by setsof contours from silhouette views simultaneously observedfrom multiple calibrated cameras, while structural featuresof interest on the object are denoted by a number of 3d locations.a prior density over the multi-view shape and correspondingstructure is constructed with a mixture of probabilisticprincipal components analyzers. given a novelset of contours, we infer the unknown structure parametersfrom the new shape's bayesian reconstruction. modelmatching and parameter inference are done entirely in theimage domain and require no explicit 3d construction. ourshape model enables accurate estimation of structure despitesegmentation errors or missing views in the input silhouettes,and it works even with only a single input view.using a training set of thousands of pedestrian images generatedfrom a synthetic model, we can accurately infer the3d locations of 19 joints on the body based on observedsilhouette contours from real images.
probabilistic bilinear models for appearance-based vision. we present a probabilistic approach to learning objectrepresentations based on the "content and style" bilineargenerative model of tenenbaum and freeman. in contrastto their earlier svd-based approach, our approach modelsimages using particle filters. we maintain separate particlefilters to represent the content and style spaces, allowing usto define arbitrary weighting functions over the particles tohelp estimate the content/style densities. we combine thisapproach with a new em-based method for learning basisvectors that describe content-style mixing. using a particle-basedrepresentation permits good reconstruction despitereduced dimensionality, and increases storage capacity andcomputational efficiency. we describe how learning the distributionsusing particle filters allows us to efficiently computea probabilistic "novelty" term. our example applicationconsiders a dataset of faces under different lightingconditions. the system classifies faces of people it has seenbefore, and can identify previously unseen faces as new content.using a probabilistic definition of novelty in conjunctionwith learning content-style separability provides a crucialbuilding block for designing real-world, real-time objectrecognition systems.
towards a mathematical theory of primal sketch and sketchability. in this paper, we present a mathematical theory for marr'sprimal sketch. we first conduct a theoretical study ofthe descriptive markov random field model and the generativewavelet/sparse coding model from the perspectiveof entropy and complexity. the competition between thetwo types of models defines the concept of "sketchability",which divides image into texture and geometry. we then proposea primal sketch model that integrates the two modelsand, in addition, a gestalt field model for spatial organization.we also propose a sketching pursuit process that coordinatesthe competition between two pursuit algorithms:the matching pursuit [8] and the filter pursuit [12], that seekto explain the image by bases and filters respectively. themodel can be used to learn a dictionary of image primitives,or textons in julesz's language, for natural images.the primal sketch model is not only parsimonious for imagerepresentation, but produces meaningful sketches overa large number of generic images.
face surveillance. most of the research on face recognition addresses the match problem and it assumes a closed universe where there is no need for a reject ('false positive') option. the surveillance problem is addressed indirectly, if at all, through the match problem, where the size of the gallery rather than that of the probe set is very large. this paper addresses the proper surveillance problem where the size of the probe ('unknown image') set vs gallery ('known image') set is 450 vs 50 frontal images. we developed robust face id verification ('classification') and retrieval schemes based on hybrid classifiers and showed their feasibility using the feret face database. the hybrid classifier architecture consists of an ensemble of connectionist networks - radial basis functions (rbf) and inductive decision trees (dt). experimental results prove the feasibility of our approach and yield 97% accuracy using the probe and gallery sets specified above.
shading primitives: finding folds and shallow grooves. diffuse interreflections cause effects that make current theories of shape from shading unsatisfactory. we show that distant radiating surfaces produce radiosity effects at low spatial frequencies. this means that, if a shading pattern has a small region of support, unseen surfaces in the environment can only produce effects that vary slowly over the support region. it is therefore relatively easy to construct matching processes for such patterns that are robustto interreflections. we call regions with these patterns "shading primitives."folds and grooves on surfaces provide two examples of shading primitives; the shading pattern is relatively independent of surface shape at a fold or a groove, and the pattern is localised. we show that the pattern of shading can be predicted accurately by a simple model, and derive a matching process from this model. both groove and fold matchers are shown to work well on images of real scenes.
calibration-free visual control using projective invariance. much of the previous work on hand-eye coordination has emphasized the reconstructive aspects of vision. recently, techniques that avoid explicit reconstruction by placing visual feedback into a control loop have been developed. when properly defined, these methods lead to calibration insensitive hand-eye coordination. recent work on projective geometry as applied to vision is used to extend this paradigm in two ways. first, it is shown how results from projective geometry can be used to perform online calibration. second, results on projective invariance are used to define setpoints for visual control that are independent of viewing location. these ideas are illustrated through a number of examples and have been tested on an implemented system.
degenerate cases and closed-form solutions for camera calibration with one-dimensional objects. camera calibration with one-dimensional objects is based on an algebraic constraint on the image of the absolute conic. we will give an alternative derivation to this constraint, allowing a geometrical interpretation. from this we derive the degenerate cases, or critical motions, where the calibration algorithm will fail. we also show that constraints on the intrinsic parameters lead to simplified closed-form solutions and a reduced set of critical motions. a simulation and a real data experiment is performed to evaluate the accuracy of the calibration result for motions close to being critical.
on-line density-based appearance modeling for object tracking. object tracking is a challenging problems in real-time computer vision due to variations of lighting condition, pose, scale, and view-point over time. however, it is exceptionally difficult to model appearance with respect to all of those variations in advance; instead, on-line update algorithms are employed to adapt to these changes. we present a new on-line appearance modeling technique which is based on sequential density approximation. this technique provides accurate and compact representations using gaussian mixtures, in which the number of gaussians is automatically determined. this procedure is performed in linear time at each time step, which we prove by amortized analysis. features for each pixel and rectangular region are modeled together by the proposed sequential density approximation algorithm, and the target model is updated in scale robustly. we show the performance of our method by simulations and tracking in natural videos.
on optimizing template matching via performance characterization. template matching is a fundamental operator in computer vision and is widely used in feature tracking, motion estimation, image alignment, and mosaicing. under a certain parameterized warping model, the traditional template matching algorithm estimates the geometric warp parameters that minimize the ssd between the target and a warped template. the performance of the template matching can be characterized by deriving the distribution of warp parameter estimate as a function of the ideal template, the ideal warp parameters, and a given noise or perturbation model. in this paper, we assume a discretization of the warp parameter space and derive the theoretical expression for the probability mass function (pmf) of the parameter estimate. as the pmf is also a function of the template size, we can optimize the choice of the template or block size by determining the template/block size that gives the estimate with minimum entropy. experimental results illustrate the correctness of the theory. an experiment involving feature point tracking in face video is shown to illustrate the robustness of the algorithm in a real-world problem.
bottom-up/top-down image parsing by attribute graph grammar. in this paper, we present an attribute graph grammar for image parsing on scenes with man-made objects, such as buildings, hallways, kitchens, and living rooms. we choose one class of primitives ¿ 3d planar rectangles projected on images, and six graph grammar production rules. each production rule not only expands a node into its components, but also includes a number of equations that constrain the attributes of a parent node and those of its children. thus our graph grammar is context sensitive. the grammar rules are used recursively to produce a large number of objects and patterns in images and thus the whole graph grammar is a type of generative model. the inference algorithm integrates bottom-up rectangle detection which activates top-down prediction using the grammar rules. the final results are validated in a bayesian framework. the output of the inference is a hierarchical parsing graph with objects, surfaces, rectangles, and their spatial relations. in the inference, the acceptance of a grammar rule means a recognition of an object, and actions are taken to pass the attributes between a node and its parent through the constraint equations associated with this production rule. when an attribute is passed from a child node to a parent node, it is called bottom-up, and the opposite is called top-down.
determining reflectance and light position from a single image without distant illumination assumption. several techniques have been developed for recovering reflectance properties of real surfaces under unknown illumination conditions. however, in most cases, those techniques assume that the light sources are located at inifinity,which cannot be applied to, for example, photometric modeling of indoor environments. in this paper, we propose two methods to estimate the surface reflectance property of an object, as well as the position of a light source from a single image without the distant illumination assumption. given a color image of an object with specular reflection as an input, the first method estimates the light source position by fitting to the lambertian diffuse component, while separating the specular and diffuse components by using an iterative relaxation scheme. moreover, we extend the above method by using a single specular image as an input, thus removing its constraints on the diffuse reflectance property and the number of light sources. this method simultaneously recovers the reflectance properties and the light source positions by optimizing the linearity of a log-transformed torrance-sparrow model. by estimating the object's reflectance property and the light source position, we can freely generate synthetic images of the target object under arbitrary source directions and source-surface distances.
multiple light sources and reflectance property estimation based on a mixture of spherical distributions. in this paper, we propose a new method for simultaneously estimating the illumination of the scene and the reflectanceproperty of the object from a single image. we assume that the illumination consists of multiple point sources and the shape of the object is known. unlike previous methods, we will recover not only the direction and intensity of the light sources, but also the number of light sources and the specular reflection parameter of the object. first, we represent the illumination on the surface of a unit sphere as a finite mixture of von mises-fisher distributions by deriving a spherical specular reflection model. next, we estimate this mixture and the number of distributions. finally, using this result as initial estimates, we refine the estimates using the original specular reflection model. we can use the results to render the object under novel lighting conditions.
a linear method for reconstruction from lines and points. discusses the basic role of the trifocal tensor in scene reconstruction. this 3/spl times/3/spl times/3 tensor plays a role in the analysis of scenes from three views analogous to the role played by the fundamental matrix in the two-view case. in particular, the trifocal tensor maybe computed by a linear algorithm from a set of 13 line correspondences in three views. it is further shown in this paper to be essentially identical to a set of coefficients introduced by shashua (1994) to effect point transfer in the three-view case. this observation means that the 13-line algorithm may be extended to allow for the computation of the trifocal tensor given any mixture of sufficiently many line and point correspondences. from the trifocal tensor, the camera image matrices may be computed, and the scene may be reconstructed. for unrelated uncalibrated cameras, this reconstruction is unique up to projectivity. thus, projective reconstruction of a set of lines and points may be reconstructed linearly from three views.
in defence of the 8-point algorithm. the fundamental matrix is a basic tool in the analysis of scenes taken with two uncalibrated cameras, and the 8 point algorithm is a frequently cited method for computing the fundamental matrix from a set of 8 or more point matches. it has the advantage of simplicity of implementation. the prevailing view is, however, that it is extremely susceptible to noise and hence virtually useless for most purposes. the paper challenges that view, by showing that by preceding the algorithm with a very simple normalization (translation and scaling) of the coordinates of the matched points, results are obtained comparable with the best iterative algorithms. this improved performance is justified by theory and verified by extensive experiments on real images.
parameter-free radial distortion correction with centre of distortion estimation. we propose a method of simultaneously calibrating the radial distortion function of a camera alongwith the other internal calibration parameters. the method relies on the use of a planar (or alternatively non-planar) calibration grid, which is captured in several images. in this way, the determination of the radial distortion is an easy add-on to the popular calibration method proposed by zhang [17]. the method is entirely non-iterative, and hence is extremely rapid and immune from the problem of local minima. our method determines the radial distortion in a parameter-free way, not relying on any particular radial distortion model. this makes it applicable to a large range of cameras from narrow-angle to fish-eye lenses. the method also computes the centre of radial distortion, which we argue is important in obtaining optimal results. experiments show that this point may be significantly displaced from the centre of the image, or the principal point of the camera.
photo-consistent 3d fire by flame-sheet decomposition. this paper considers the problem of reconstructing visuallyrealistic 3d models of fire from a very small setof simultaneous views (even two). by modeling fire as asemi-transparent 3d density field, we show that fire reconstructionis equivalent to a severely under-constrained computerizedtomography problem, for which traditional methodsbreak down. our approach is based on the observationthat every pair of photographs of a semi-transparentscene defines a unique density field, called a flame sheet,that (1) concentrates all its density on one connected, semi-transparentsurface, (2) reproduces the two photos exactly,and (3) is the most spatially-coherent density field that doesso. from this observation, we reduce fire reconstruction tothe convex combination of sheet-like density fields, each ofwhich is derived from the flame sheet of two input photos.experimental results suggest that this method enables high-qualityview extrapolation without over-fitting artifacts.
segmented shape descriptions from 3-view stereo. we address the recovery of segmented, 3-d descriptions of an object from intensity images. we use three views of an object from slightly different viewpoints as our input. for each image we extract a hierarchy of groups based on proximity, parallelism and symmetry in a robust manner. the groups in the three images are matched by computing the epipolar geometry. for each set of matched groups from the three images, we then label the contours of the groups as "true" or "limb" edges. using the information about groups, the label associated with their contours and projective properties of subclasses of generalized cylinders, we infer the 3-d structure of these groups. the proposed method not only allows robust shape recovery but also produces segmented parts. our approach can also deal with groups generated as a result of texture or shadows on the object. we present results on real images of moderately complex objects.