waashk/dblp huggingface dataset:
structured light in scattering media. virtually all structured light methods assume that the scene and the sources are immersed in pure air and that light is neither scattered nor absorbed. recently, however, structured lighting has found growing application in underwater and aerial imaging, where scattering effects cannot be ignored. in this paper, we present a comprehensive analysis of two representative methods - light stripe range scanning and photometric stereo - in the presence of scattering. for both methods, we derive physical models for the appearances of a surface immersed in a scattering medium. based on these models, we present results on (a) the condition for object detectability in light striping and (b) the number of sources required for photometric stereo. in both cases, we demonstrate that while traditional methods fail when scattering is significant, our methods accurately recover the scene (depths, normals, albedos) as well as the properties of the medium. these results are in turn used to restore the appearances of scenes as if they were captured in clear air. although we have focused on light striping and photometric stereo, our approach can also be extended to other methods such as grid coding, gated and active polarization imaging.
results using random field models for the segmentation of color images of natural scenes. we present results using a markov random field color texture model for the unsupervised segmentation of images of outdoor scenes. the color random field model describes textured regions in terms of spatial interaction within color bands and between different color bands. the model is used by a segmentation algorithm based on agglomerative hierarchical clustering. at the heart of the clustering is a step wise optimal merging process that at each iteration maximizes a global performance functional. the test for stopping the clustering is based on changes in the likelihood of the image. we provide experimental results that demonstrate the performance of the segmentation algorithm on color images of natural scenes. most of the processing during segmentation is local making the algorithm amenable to high performance parallel implementation.
a class of photometric invariants: separating material from shape and illumination. we derive a new class of photometric invariants that can beused for a variety of vision tasks including lighting invariantmaterial segmentation, change detection and tracking, aswell as material invariant shape recognition. the key ideais the formulation of a scene radiance model for the class of"separable" brdfs, that can be decomposed into materialrelated terms and object shape and lighting related terms.all the proposed invariants are simple rational functions ofthe appearance parameters (say, material or shape and lighting).the invariants in this class differ from one another in thenumber and type of image measurements they require. mostof the invariants in this class need changes in illumination orobject position between image acquisitions. the invariantscan handle large changes in lighting which pose problems formost existing vision algorithms. we demonstrate the power ofthese invariants using scenes with complex shapes, materials,textures, shadows and specularities.
constructing virtual worlds using dense stereo. we present virtualized reality, a technique to create virtual worlds out of dynamic events using densely distributed stereo views. the intensity image and depth map for each camera view at each time instant are combined to form a visible surface model. immersive interaction with the virtualized event is possible using a dense collection of such models. additionally, acomplete surface model of each instant can be built by merging the depth maps from different cameras into a common volumetric space. the corresponding model is compatible with traditional virtual models and can be interacted with immersively using standard tools. because both vsms and csms are fully three-dimensional, virtualized models can also be combined and modi.ed to build larger, more complex environments, an important capability for many non-trivial applications. we present results from 3d dome, our facility to create virtualized models.
how to deal with point correspondences and tangential velocities in the level set framework. in this paper, we overcome a major drawback of the levelset framework: the lack of point correspondences. we maintainexplicit backward correspondences from the evolvinginterface to the initial one by advecting the initial point coordinateswith the same speed as the level set function. ourmethod leads to a system of coupled eulerian partial differentialequations. we show in a variety of numerical experimentsthat it can handle both normal and tangential velocities,large deformations, shocks, rarefactions and topologicalchanges. applications are many in computer vision andelsewhere since our method can upgrade virtually any levelset evolution. we complement our work with the design ofnon zero tangential velocities that preserve the relative areaof interface patches; this feature may be crucial in such applicationsas computational geometry, grid generation orunfolding of the organs' surfaces, e.g. brain, in medicalimaging.
adaptive dynamic range imaging: optical control of pixel exposures over space and time. this paper presents a new approach to imaging thatsignificantly enhances the dynamic range of a camera.the key idea is to adapt the exposure of each pixel onthe image detector, based on the radiance value of thecorresponding scene point. this adaptation is done inthe optical domain, that is, during image formation. inpractice, this is achieved using a spatial light modulatorwhose transmittance can be varied with high resolutionover space and time. a real-time control algorithm isdeveloped that uses acquired images to automaticallyadjust the transmittance function of the spatial modulator. each captured image and its corresponding transmittance function are used to compute a very high dynamic range image that is linear in scene radiance.we have implemented a video-rate adaptive dynamicrange camera that consists of a color ccd detector anda controllable liquid crystal light modulator. experiments have been conducted in scenarios with complexand harsh lighting conditions. the results indicate thatadaptive imaging can have a significant impact on visionapplications such as monitoring, tracking, recognition,and navigation.
variational stereovision and 3d scene flow estimation with statistical similarity measures. we present a common variational framework for dense depth recovery and dense three-dimensional motion field estimation from multiple video sequences, which is robustto camera spectral sensitivity differences and illumination changes. for this purpose, we first show that both problems reduce to a generic image matching problem after backprojecting the input images onto suitable surfaces. we then solve this matching problem in the case of statistical similarity criteria that can handle frequently occurring non-affine image intensities dependencies. our method leads to an efficient and elegant implementation based on fast recursive filters. we obtain good results on real images.
vision in bad weather. current vision systems are designed to perform in clear weather. needless to say, in any outdoor application, there is no escape from &ldquo;bad&rdquo; weather. ultimately, computer vision systems must include mechanisms that enable them to function (even if somewhat less reliably) in the presence of haze, fog, rain, hail and snow. we begin by studying the visual manifestations of different weather conditions. for this, we draw on what is already known about atmospheric optics. next, we identify effects caused by bad weather that can be turned to our advantage. since the atmosphere modulates the information carried from a scene point to the observer it can be viewed as a mechanism of visual information coding. based on this observation, we develop models and methods for recovering pertinent scene properties, such as three-dimensional structure, from images taken under poor weather conditions
shadow flow: a recursive method to learn moving cast shadows. we present a novel algorithm to detect and remove cast shadows in a video sequence by taking advantage of the statistical prevalence of the shadowed regions over the object regions. we model shadows using multivariate gaussians. we apply a weak classifier as a pre-filter.we project shadow models into a quantized color space to update a shadow flow function. we use shadow flow, background models, and current frame to determine the shadow and object regions. this method has several advantages: it does not require a color space transformation. we pose the problem in the rgb color space, and we can carry out the same analysis in other cartesian spaces as well. it is data-driven and adapts to the changing shadow conditions. in other words, accuracy of our method is not limited by the preset values. furthermore, it does not assume any 3d models for the target objects or tracking of the cast shadows between frames. our results show that the detection performance is superior than the benchmark method.
real-time focus range sensor. structures of dynamic scenes can only be recovered using a real-time range sensor. depth-from-defocus offers a direct solution to fast and dense range estimation. it is computationally efficient as it circumvents the correspondence problem faced by stereo and feature tracking in structure-from-motion. however, accurate depth estimation requires theoretical and practical solutions to a variety of problems including the recovery of textureless surfaces, precise blur estimation, and magnification variations caused by defocusing. both textured and textureless surfaces are recovered using an illumination pattern that is projected via the same optical path used to acquire images. the illumination pattern is optimized to ensure maximum accuracy and spatial resolution in the computed depth. the relative blurring in two images is computed using a narrow-band linear operator that is designed by considering all the optical, sensing and computational elements of the depth-from-defocus system. defocus-invariant magnification is achieved by the use of an additional aperture in the imaging optics. a prototype focus range sensor has been developed that produces up to 512/spl times/480 depth estimates at 30 hz with an accuracy better than 0.3%. several experimental results are included to demonstrate the performance of the sensor.
comparing curved-surface range image segmenters. this work focuses on creating a framework for objectively evaluating the performance of range image segmentation algorithms. the algorithms are evaluated in terms of correct segmentation, over- and under- segmentation, missed and noise regions. a set of images with ground truth was created for this work. the images were captured using a structured light scanner. images used in the evaluation contain planar, spherical, cylindrical, toroidal andconical surface patches. the different surface patches in each image were manually identified to establish ground truth for performance evaluation. two segmentation algorithms from the literature are compared.
improved sub-pixel stereo correspondences through symmetric refinement. most dense stereo correspondence algorithms start by establishing discrete pixel matches and later refine these matches to sub-pixel precision. traditional sub-pixel refinement methods attempt to determine the precise location of points, in the secondary image, that correspond to discrete positions in the reference image. we show that this strategy can lead to a systematic bias associated with the violation of the general symmetry of matching cost functions. this bias produces random or coherent noise in the final reconstruction, but can be avoided by refining both image coordinates simultaneously, in a symmetric way. we demonstrate that the symmetric sub-pixel refinement strategy results in more accurate correspondences by avoiding bias while preserving detail.
"perspective shape from shading" and viscosity solutions. this article proposes a solution of the lambertian shapefrom shading (sfs) problem in the case of a pinhole cameramodel (performing a perspective projection). our approachis based upon the notion of viscosity solutions of hamilton-jacobiequations. this approach allows us to naturallydeal with nonsmooth solutions and provides a mathematicalframework for proving correctness of our algorithms.our work extends previous work in the area in three aspects.first, it models the camera as a pinhole whereasmost authors assume an orthographic projection (see [15]for a panorama of the sfs problem up to 1989 and [29, 17]for a recent survey), thereby extending the applicability ofshape from shading methods to more realistic images. inparticular it extends the work of [24] and [26]. second, byadapting the brightness equation to the perspective problem,we obtain a new partial differential equation (pde).results about the existence and uniqueness of its solutionare also obtained. third, it allows us to come up with a newapproximation scheme and a new algorithm for computingnumerical approximations of the "continuous" solution aswell as a proof of their convergence toward that solution.
a cubist approach to object recognition. we describe an appearance-based object recognition system using a keyed, multi-level context representation reminiscent of certain aspects of cubist art. specifically, we utilize distinctive intermediate-level features in this case automatically extracted 2-dboundary fragments, as keys, which are then verified within a local context, and assembled within a loose global context to evoke an overall percept. this system demonstrates good recognition of a variety of 3-d shapes, ranging from sports cars and fighter planes to snakes and lizards with full orthographic invariance. we report the results of large-scale tests, involving over 2000 separate test images, that evaluate performance with increasing number of items inthe database, in the presence of clutter, background change, and occlusion, and also the results of some generic classification experiments where the system is tested on objects never previously seen or modeled. to our knowledge, the results we report are the best in the literature for full-sphere tests of general shapes with occlusion and clutter resistance.
detecting rotational symmetries. we present an algorithm for detecting multiple rotational symmetries in natural images. given an image, its gradient magnitude field is computed, and information from the gradients is spread using a diffusion process in the form of a gradient vector flow (gvf) field. we construct a graph whose nodes correspond to pixels in the image, connecting points that are likely to be rotated versions of one another. the n-cycles present in the graph are made to vote for c_n symmetries, their votes being weighted by the errors in transformation between gvf in the neighborhood of the voting points, and the irregularity of the n-sided polygons formed by the voters. the votes are accumulated at the centroids of possible rotational symmetries, generating a confidence map for each order of symmetry. we tested the method with several natural images.
stereo with mirrors. in this paper, we propose the use of mirrors and a single camera for computational stereo. when compared to conventional stereo systems that use two cameras, our method has a number of significant advantages such as wide field of view, single viewpoint projection, identical camera parameters and ease of calibration. we propose four stereo systems that use a single camera pointed towards planar, ellipsoidal, hyperboloidal, and paraboloidal mirrors. in each case, we present a derivation of the epipolar constraints. next, we attempt to understand what can be seen by each system and formalize the notion of field of view. we conclude with two experiments to obtain 3-d structure. in the first we use a pair of planar mirrors, and in the second a pair of paraboloidal mirrors. the results of our experiments demonstrate the viability of stereo using mirrors.
wide baseline stereo matching. the objective of this work is to enlarge the class of camera motions for which epipolar geometry and image correspondences can be computed automatically. this facilitates matching between quite disparate views ¿ wide baseline stereo.two extensions are made to the current small baseline algorithms: first, and most importantly, a viewpoint invariant measure is developed for assessing the affinity of corner neighbourhoods over image pairs; second, algorithms are given for generating putative corner matches between image pairs using local homographies. two novel infrastructure developments are also described, the automatic generation of local homographies, and the combination of possibly conflicting sets of matches prior to ransac estimation.the wide baseline matching algorithm is demonstrated on a number of image pairs with varying relative motion, and for different scene types. all processing is automatic.
measuring convexity for figure/ground separation. in human perception, convex surfaces have a strong tendency to be perceived as the "figure". convexity has a stronger influence on figural organization than other global shape properties, such as symmetry ([9]). and yet, there has been very little work on convexity properties in computer vision.we present a model for figure/ground segregation which exhibits a preference for convex regions as the figure (i.e., the foreground). the model also shows a preference for smaller regions to be selected as figures, which is also known to hold for human visual perception (e.g., koffka [11]). the model is based on the machinery of markov random fields/random walks/diffusion processes, so that the global shape properties are obtained via local and stochastic computations. experimental results demonstrate that our model performs well on ambiguous figure/ground displays which were not captured before. in particular, in ambiguous displays where neither region is strictly convex, the model shows preference to the "more convex" region, thus offering a continuous measure of convexity in agreement with human perception.
reading between the lines: a method for extracting dynamic 3d with texture. a method is presented that extracts the 3d shape of objects, together with the surface texture. both shape and tecture are obtained from a single image. the underlying principle is based on an active technique. a high resolution pattern is projected onto the object and the deformations as observed by a single camera yield the 3rd dimension. furthermore, the surface texture is extracted from the same image. because the whole procedure is based on a single image, a frame-by-frame reconstruction of a video taken with the pattern projected throughout, yields 3dshape dynamics. the paper sketches the complete system but focuses on the problem of extraction.
eye design in the plenoptic space of light rays. natural eye designs are optimized with regard to the tasksthe eye-carrying organism has to perform for survival. thisoptimization has been performed by the process of naturalevolution over many millions of years. every eye capturesa subset of the space of light rays. the information containedin this subset and the accuracy to which the eye canextract the necessary information determines an upper limiton how well an organism can perform a given task. in thiswork we propose a new methodology for camera design. byinterpreting eyes as sample patterns in light ray space wecan phrase the problem of eye design in a signal processingframework. this allows us to develop mathematical criteriafor optimal eye design, which in turn enables us to build thebest eye for a given task without the trial and error phase ofnatural evolution. the principle is evaluated on the task of3d ego-motion estimation.
an enhanced correlation-based method for stereo correspondence with sub-pixel accuracy. the invariance of the similarity measure in photometric distortions as well as its capability in producing sub-pixel accuracy are two desired and often required features in most stereo vision applications. in this paper we propose a new correlation-based measure which incorporates both mentioned requirements. specifically, by using an appropriate interpolation scheme in the candidate windows of the matching image, and using the classical zero mean normalized cross correlation function, we introduce a suitable measure. although the proposed measure is a non-linear function of the sub-pixel displacement parameter, its maximization results in a closed form solution, resulting in reduced complexity for its use in matching techniques. application of the proposed measure in a number of benchmark stereo pair images reveals its superiority over existing correlation-based techniques used for sub-pixel accuracy.
automatic video summarization by graph modeling. we propose a unified approach for summarization based on the analysis of video structures and video highlights. our approach emphasizes both the content balance and perceptual quality of a summary. normalized cut algorithm is employed to globally and optimally partition a video into clusters. a motion attention model based on human perception is employed to compute the perceptual quality of shots and clusters. the clusters, together with the computed attention values, form a temporal graph similar to markov chain that inherently describes the evolution and perceptual importance of video clusters. in our application, the flow of a temporal graph is utilized to group similar clusters into scenes, while the attention values are used as guidelines to select appropriate sub-shots in scenes for summarization.
multiscale annealing for real-time unsupervised texture segmentation. we derive real-time global optimization algorithms for several clustering optimization methods used in unsupervised texture segmentation. speed is achieved by exploiting the topological relation of features to design a multiscale optimization technique, while accuracy and global optimization properties are provided by a deterministic annealing method. coarse grained cost functions are derived for both central and sparse pairwise clustering, where the problem of coarsening sparse random graphs is solved by the concept of structured randomization. annealing schedules and coarse-to-fine optimization are tightly coupled by a statistical convergence criterion derived from computational learning theory. the algorithms are benchmarked on brodatz-like micro-texture mondrians. results are presented for an autonomous robotics application.
surface reconstruction: gncs and mfa. the reconstruction of noise corrupted surfaces can be inferred by methodologies such as bayesian estimation and minimum description length. both of these imply a formulation where the reconstruction minimizes a functional. often this functional is non convex and the minimum cannot be found by simple gradient methods. the paper concerns functionals with quadratic data term, criteria for such functionals to be convex, and the variational approach of minimizing non convex functionals. initial convexity of the approximating functional is considered to be a critical point. two fully automatic methods of generating convex functionals are presented. they are based on gaussian convolution and are compared to the blake-zisserman graduated non convexity (gnc) (a. blake, a. zisserman, 1987) and g.l. bilbro et al. (1992) and d. geiger and f. girosi's (1991) mean field annealing (mfa) of the weak membrane.