waashk/dblp huggingface dataset:
building a classification cascade for visual identification from one example. object identification (oid) is specialized recognition where the category is known (e.g. cars) and the algorithm recognizes an object¿s exact identity (e.g. bob¿s bmw). two special challenges characterize oid. (1) inter-class variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. (2) there may be many classes but few or just one positive "training" examples per class. due to (1), a solution must locate possibly subtle object-specific salient features (a door handle) while avoiding distracting ones (a specular highlight). however, (2) rules out direct techniques of feature selection. we describe an online algorithm that takes one model image from a known category and builds an efficient "same" vs. "different" classification cascade by predicting the most discriminative feature set for that object. our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered feature sequence unique to a specific model image, maximizing cumulative information content. learned stopping thresholds make the classifier very efficient. to make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled image pairs of the category, without prior knowledge about the category. our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods.
discontinuity preserving stereo with small baseline multi-flash illumination. currently, sharp discontinuities in depth and partial occlusions in multiview imaging systems pose serious challenges for many dense correspondence algorithms. however, it is important for 3d reconstruction methods to preserve depth edges as they correspond to important shape features like silhouettes which are critical for understanding the structure of a scene. in this paper we show how active illumination algorithms can produce a rich set of feature maps that are useful in dense 3d reconstruction. we start by showing a method to compute a qualitative depth map from a single camera, which encodes object relative distances and can be used as a prior for stereo. in a multiview setup, we show that along with depth edges, binocular half-occluded pixels can also be explicitly and reliably labeled. to demonstrate the usefulness of these feature maps, we show how they can be used in two different algorithms for dense stereo correspondence. our experimental results show that our enhanced stereo algorithms are able to extract high quality, discontinuity preserving correspondence maps from scenes that are extremely challenging for conventional stereo methods.
global rigidity constraints in image displacement fields. image displacement fields-optical flow fields, stereo disparity fields, normal flow fields-due to rigid motion possess a global geometric structure which is independent of the scene in view. motion vectors of certain lengths and directions are constrained to lie on the imaging surface at particular loci whose location and form depends solely on the 3d motion parameters. if optical flow fields or stereo disparity fields are considered, then equal vectors are shown to lie on conic sections. similarly, for normal motion fields, equal vectors lie within regions whose boundaries also constitute conics. by studying various properties of these curves and regions and their relationships, a characterization of the structure of rigid motion fields is given. the goal of this paper is to introduce a concept underlying the global structure of image displacement fields. this concept gives rise to various constraints that could form the basis of algorithms for the recovery of visual information from multiple views.
which shape from motion?. in a practical situation, the rigid transformation relating different views is recovered with errors. in such a case, the recovered depth of the scene contains errors, and consequently a distorted version of visual space is computed. what then are meaningful shape representations that can be computed from the images? the result presented in this paper states that if the rigid transformation between different views is estimated in a way that gives rise to a minimum number of negative depth values, then at the center of the image affine shape can be correctly computed. this result is obtained by exploiting properties of the distortion function, developed in [1]. the distortion model turns out to be a very powerful tool in the analysis and design of 3d motion and shape estimation algorithms, and as a byproduct of our analysis we present a computational explanation of psychophysical results demonstrating human visual space distortion from motion information.
building qualitative event models automatically from visual input. we describ e an implemented technique for generating event models automatically based on qualitative reasoning and a statistical analysis of video input. using an existing tracking program which generates labelled contours for objects in every frame, the view from a fixed camera is partitioned into semantically relevant regions based on the paths followed by movingobjects. the paths are indexed with temporal information so objects moving along the same path at different speeds can be distinguished. using a notion of proximity based on the speed of the moving objects and qualitative spatial reasoning techniques, event models describing the behaviour of pairs of objects can be built, again using statistical methods. the system has been tested on a traffic domain and learns various event models expressed in the qualitative calculus which represent human observable events. the system can then be used to recognise subsequent selected event occurrences or unusual behaviours.
achieving a fitts law relationship for visual guided reaching. in order to take advantage of the top speed of manipulators, vision can not be tightly integrated into the motion control loop. past visual servo control systems have performed satisfactorily with this constraint, however it can be shown that the task execution time can be reduced if the vision system is de-coupled from the low level motor control system.for reaching, there is a trade-off between the accuracy of a motion and the time requir ed to execute a motion. in studies of human motor contr ol this trade-off is quantified by fitts law, a relationship between the motion time, the target distance, and the target width. these studies suggest that vision is not used tightly within the control loop, i.e. as a sensor that is servo-ed on, but rather vision is used to determine where the reaching target is and whether target has been reached successfully. through a simple robotic example we demonstrate that a similar trade off exists between motion accuracy and the motion execution time for visual guided robot reaching motions. the acm portal is published by the association for computing machinery. copyright © 2010 acm, inc. terms of usage privacy policy code of ethics contact us useful downloads: adobe acrobat quicktime windows media player real player
color constancy in diagonal chromaticity space. simple constraints on the sets of possible surface reflectances and illuminants are exploited in a new color constancy algorithm that builds upon forsyth's (1990) theory of color constancy. the goal defined for a color constancy algorithm is to discount variations in the color and intensity of the incident illumination and thereby extract illumination-independent descriptors of surface colors from images. forsyth's method is based on two constraints: first, the surface colors under a canonical illuminant all fall within an established maximal convex gamut of possible colors and second that a diagonal matrix accurately maps colors between illuminants. these constraints taken together turn out to be very effective in solving for color constancy; however, other strong assumptions about the scenes are required for the method to work-the illumination must be uniform, the surfaces must be planar, and there can be no specularities. we show that these restrictions are necessary only because forsyth sets out to recover the intensity of descriptors. at the outset we abandon 3-dimensional descriptor recovery in favor of recovering only orientation (i.e. 2 dimensions). intensity information is factored out of the problem by mapping 3-dimensional (r, g, b) camera responses onto 2-dimensional chromaticities; specifically (r/b, g/b). we show that this "diagonal chromaticity space" has two important properties: first, gamut convexity is preserved and second illumination change is still described by a diagonal matrix. it follows that forsyth's algorithm can be directly applied to the recover chromaticity descriptors and from these the 3d descriptor orientations can be derived. the basic algorithm is then extended to include a maximal gamut constraint on the set of illuminants that is analogous to the gamut constraint on surface colors. the diagonal chromaticity space facilitates the expression of the illumination constraint in the algorithm. tests on real images show that the algorithm provides good color constancy.
color constancy under varying illumination. illumination is rarely constant in intensity or color throughout a scene. multiple light sources with different spectra-sun and sky, direct and interreflected light-are the norm. nonetheless, almost all color constancy algorithms assume that the spectrum of the incident illumination remains constant across the scene. we assume the converse, that illumination does vary, in developing a new algorithm for color constancy. rather than creating difficulties, varying illumination is in fact a very powerful constraint. indeed tests of our algorithm using real images of an office scene show excellent results.
colour by correlation: a simple, unifying approach to colour constancy. in this paper we consider the problem of color constancy; how given an image of a scene under an unknown illuminant can we recover an estimate of that light? rather than recovering a single estimate of the illuminant as many previous authors have done, in the first instance we recover a measure of the likelihood that each possible illuminant was the scene illuminant. we do this by correlating image colors with the colors that can occur under each of a set of possible lights. we then recover an estimate of the scene illuminant based on these likelihoods. computation is expressed and performed in a generic correlation framework which we develop in this paper. we develop a new probabilistic instantiation of this framework which delivers very good color constancy on synthetic and real images. we show that the proposed framework is rich enough to allow many existing algorithms to be expressed within it; e.g. the grey-world and gamut mapping algorithms. we explore too the relationship of these algorithms to other probabilistic and neural network approaches.
gamut constrained illuminant estimation. this paper presents a novel solution to the illuminant estimationproblem: the problem of how, given an image of ascene taken under an unknown illuminant, we can recoveran estimate of that light. the work is founded on previousgamut mapping solutions to the problem which solvefor a scene illuminant by determining the set of diagonalmappings which take image data captured under an unknownlight to a gamut of reference colours taken undera known light. unfortunately a diagonal model is not alwaysa valid model of illumination change and so previousapproaches sometimes return a null solution. in addition,previous methods are difficult to implement. we addressthese problems by recasting the problem as one ofilluminant classification: we define a prioria set of plausiblelights thus ensuring that a scene illuminant estimatewill always be found. a plausible light is represented bythe gamut of colours observable under it and the illuminantin an image is classified by determining the plausible lightwhose gamut is most consistent with the image data. weshow that this step (the main computational burden of thealgorithm) can be performed simply, quickly, and efficientlyby means of a non-negative least-squares optimisation. wereport results on a large set of real images which show thatit provides excellent illuminant estimation, outperformingprevious algorithms.
image-based rendering using image-based priors. given a set of images acquired from known viewpoints, wedescribe a method for synthesizing the image which wouldbe seen from a new viewpoint.in contrast to existing techniques,which explicity reconstruct the 3d geometry of thescene, we transform the problem to the reconstruction ofcolour rather than depth.this retains the benefits of geometricconstraints, but projects out the ambiquities in depthestimation which occur in textureless regions.on the other hand, regularization is still needed in orderto generate high-quality images.the paper's secondcontribution is to constrain the generated views to lie in thespace of images whose texture statistics are those of the inputimages.this amounts to a image-based prior on thereconstruction which regularizes the solution, yielding realisticsynthetic views.examples are given of new viewgeneration for cameras interpolated between the acquisitionviewpoints-which enables synthetic steadicam stabilizationof a sequence with a high level of realism.
fixed point probability field for complex occlusion handling. in this paper, we show that in a multi-camera context, we can effectively handle occlusions in real-time at each frame independently, even when the only available data comes from the binary output of a simple blob detector, and the number of present individuals is a priori unknown. we start from occupancy probability estimates in a top view and rely on a generative model to yield probability images to be compared with the actual input images. we then refine the estimates so that the probability images match the binary input images as well as possible. we demonstrate the quality of our results on several sequences involving complex occlusions.
camera calibration with known rotation. we address the problem of using external rotation informationwith uncalibrated video sequences. the main problemaddressed is, what is the benefit of the orientation infomationfor camera calibration? it is shown that in case ofa rotating camera the camera calibration problem is lineareven in the case that all intrinsic parameters vary. forarbitrarily moving cameras the calibration problem is alsolinear but underdetermined for the general case of varyingall intrinsic parameters. however, if certain constraints areapplied to the intrinsic parameters the camera calibrationcan be computed linearily. it is analyzed which constraintsare needed for camera calibration of freely moving cameras.furthermore we address the problem of aligning thecamera data with the rotation sensor data in time. we givean approach to align these data in case of a rotating camera.
fusion of multi-view silhouette cues using a space occupancy grid. in this paper, we investigate what can be inferred from several silhouette probability maps, in multi-camera environments. to this aim, we propose a new framework for multi-view silhouette cue fusion. this framework uses a space occupancy grid as a probabilistic 3d representation of scene contents. such a representation is of great interest for various computer vision applications in perception, or localization for instance. our main contribution is to introduce the occupancy grid concept, popular in the robotics community, for multi-camera environments. the idea is to consider each camera pixel as a statistical occupancy sensor. all pixel observations are then used jointly to infer where, and how likely, matter is present in the scene. as our results illustrate, this simple model has various advantages. most sources of uncertainty are explicitly modeled, and no premature decisions about pixel labeling occur, thus preserving pixel knowledge. consequently, optimal scene object localization, and robust volume reconstruction, can be achieved, with no constraint on camera placement and object visibility. in addition, this representation allows to improve silhouette extraction in images.
robust structure from motion and identified dynamics. this paper addresses robust recovery of structure and motion for rigid bodies in video sequences. for small interframe motion, feature appearance is commonly estimated according to the previous frame. however, in the case of occlusion or corrupt frames, the small inter-frame motion model fails. in this paper, we propose to use robustidentification techniques to estimate the motion dynamics based on a set of previous frames. these dynamics are then recursively used to robustly estimate object structure and motion and to predict the object appearance in future frames. results are tested for 3d reconstructions of rigid bodies in real and synthetic image sequences.
svm-based nonparametric discriminant analysis, an application to face detection. detecting the dominant normal directions to the decisionsurface is an established technique for feature selectionin high dimensional classification problems. several approacheshave been proposed to render this strategy moreamenable to practice, but they still show a number of importantshortcomings from a pragmatic point of view. this paperintroduces a novel such approach, which combines thenormal directions idea with support vector machine classifiers.the two make a natural and powerful match, as svsare located nearby, and fully describe the decision surfaces.the approach can be included elegantly into the training ofperformant classifiers from extensive datasets. the potentialis corroborated by experiments, both on synthetic andreal data, the latter on a face detection experiment. in thisexperiment we demonstrate how our approach can lead to asignificant reduction of cpu-time, with neglectable loss ofclassification performance.
bayesian decision theory, the maximum local mass estimate, and color constancy. vision algorithms are often developed in a bayesian framework. two estimators are commonly used: maximum a posteriori (map), and minimum mean squared error (mmse). we argue that neither is appropriate for perception problems. the map estimator makes insufficient use of structure in the posterior probability. the squared error penalty of the mmse estimator does not reflect typical penalties. we describe a new estimator, which we call maximum local mass (mlm) [10, 26, 65], which integrates the local probability density. the mlm method is sensitive to local structure of the posterior probability, which map is not. the new method uses an optimality criterion that is appropriate for perception tasks: it finds the most probable approximately correct answer. for the case of low observation noise, we provide an efficient approximation. we apply this new estimator to color constancy. an unknown illuminant falls on surfaces of unknown colors. we seek to estimate both the illuminant spectrum and the surface spectra from photosensor responses which depend on the product of the unknown spectra. in simulations, we show that the mlm method performs better than the map estimator, and better than two standard color constancy algorithms. the mlm method may prove useful in other vision problems as well.
learning low-level vision. we show a learning-based method for low-level vision problems{estimating scenes from images. we generate a synthetic world of scenes and their corresponding rendered images. we model that world with a markov network, learning the network parameters from the examples. bayesian belief propagation allows us to efficiently find a local maximum of the posterior probability for the scene, given the image. we call this approach vista{vision by image/scene training.we apply vista to the "super-resolution" problem (estimating high frequency details from a low-resolution image), showing good results. for the motion estimation problem, we show figure/ground discrimination, solution of the aperture problem, and filling-in arising from application of the same probabilistic machinery.
transformed component analysis: joint estimation of spatial transformations and image components. a simple, effective way to model images is to represent each input pattern by a linear combination of "component" vectors, where the amplitudes of the vectors are modulated to match the input. this approach includes principal component analysis, independent component analysis and factor analysis. in practice, images are subjected to randomly selected transformations of a known nature, such as translation and rotation. direct use of the above methods will lead to severely blurred components that tend to ignore the more interesting and useful structure. in previous work, we introduced a clustering algorithm that is invariant to transformations [1].in this paper, we propose a method called transformed component analysis, which incorporates a discrete, hidden variable that accounts for transformations and uses the expectation maximization algorithm to jointly extract components and normalize for transformations. we illustrate the algorithm using a shading problem, facial expression modeling and written digit recognition.
integrating representative and discriminative models for object category detection. category detection is a lively area of research. while categorization algorithms tend to agree in using local descriptors, they differ in the choice of the classifier, with some using generative models and others discriminative approaches. this paper presents a method for object category detection which integrates a generative model with a discriminative classifier. for each object category, we generate an appearance codebook, which becomes a common vocabulary for the generative and discriminative methods. given a query image, the generative part of the algorithm finds a set of hypotheses and estimates their support in location and scale. then, the discriminative part verifies each hypothesis on the same codebook activations. the new algorithm exploits the strengths of both original methods, minimizing their weaknesses. experiments on several databases show that our new approach performs better than its building blocks taken separately. moreover, experiments on two challenging multi-scale databases show that our new algorithm outperforms previously reported results.
efficient block noise removal based on nonlinear manifolds. the problem of block noise removal is considered. it is assumed that the original image is on or close to a sub-space of admissible images in the form of a low dimensional nonlinear manifold. we propose to use a close variant of the total variation regularizer for measuring block noise. based on this noise measure, we present an effective approach that reconstructs the original image in the presence of block noise. our main computational task is the solution of a quadratic programming problem, for which we propose a very efficient interior point method. the effectiveness and efficiency of our approach is demonstrated by an example.