waashk/dblp huggingface dataset:
the local projective shape of smooth surfaces and their outlines. this paper examines projectively invariant local properties of smooth curves and surfaces. oriented projective differential geometry is proposed as a theoretical framework for establishing such invariants and describing the local shape of surfaces and their outlines. this framework is applied to two problems: a projective proof of koenderink's famous characterization of convexities, concavities, and inflections of apparent contours; and the determination of the relative orientation of rim tangents at frontier points.
affine-invariant local descriptors and neighborhood statistics for texture recognition. this paper presents a framework for texture recognitionbased on local affine-invariant descriptors and their spatiallayout. at modeling time, a generative model of localdescriptors is learned from sample images using the em algorithm.the em framework allows the incorporation ofunsegmented multi-texture images into the training set. thesecond modeling step consists of gathering co-occurrencestatistics of neighboring descriptors. at recognition time,initial probabilities computed from the generative modelare refined using a relaxation step that incorporates co-occurrencestatistics. performance is evaluated on imagesof an indoor scene and pictures of wild animals.
determining wet surfaces from dry. wet surfaces are ubiquitous in our visual experience. autonomous machines with vision systems will need to identify wet surfaces from dry. wet surfaces (especially rough, absorbent ones) appear darker when wet. this paper presents the lekner and dorf (1988) model for describing the darkening caused by wetting. we explain how to use this optics model to transform intensity values of a region of an image to make that region appear wet. we also show how the model can be reversed in order to make a wet part of an image appear dry. it is also shown that this technique can be used to identify wet regions. this identification is contrasted with darkening caused by shadows. comparisons of the gray-level histograms of these real images show the validity of this approach for distinguishing wet surfaces from dry.
a maximum entropy framework for part-based texture and object recognition. this paper presents a probabilistic part-based approach for texture and object recognition. textures are represented using a part dictionary found by quantizing the appearance of scale- or affine-invariant keypoints. object classes are represented using a dictionary of composite semi-local parts, or groups of neighboring keypoints with stable and distinctive appearance and geometric layout. a discriminative maximum entropy framework is used to learn the posterior distribution of the class label given the occurrences of parts from the dictionary in the training set. experiments on two texture and two object databases demonstrate the effectiveness of this framework for visual classification.
a real-time algorithm for medical shape recovery. in this paper, we present a shape recovery technique in 2d and 3d with specific applications in visualizing and measuring anatomical shapes from medical images. this algorithm models extremely corrugated structures like the brain, is topologically adaptable, is robust, and runs in o(n logn) time where n is the total number of points in the domain. our two-stage technique is based on the level set shape recovery scheme introduced in [11, 12, 4] and the fast march ing method in [19] for computing solutions to static hamilton-jacobi equations.
learning pedestrian models for silhouette refinement. we present a model-based method for accurate extraction ofpedestrian silhouettes from video sequences. our approachis based on two assumptions, 1) there is a common appearanceto all pedestrians, and 2) each individual looks likehim/herself over a short amount of time. these assumptionsallow us to learn pedestrian models that encompassboth a pedestrian population appearance and the individualappearance variations. using our models, we are ableto produce pedestrian silhouettes that have fewer noise pixelsand missing parts. we apply our silhouette extractionapproach to the nist gait data set and show that under thegait recognition task, our model-based sulhouettes resultin much higher recognition rates than silhouettes directlyextracted from background subtraction, or any non-model-basedsmoothing schemes.
shape recovery using dynamic subdivision surfaces. a new dynamic subdivision surface model is proposed for shape recovery from 3d data sets. the model inherits the attractive properties of the catmull-clark subdivision scheme and is set in a physics-based modeling paradigm. unlike other existing methods, our model does not require a parameterized input mesh to recover shapes of arbitrary topology, allows direct manipulation of the limit surface via application of forces and provides a fast, robust, and hierarchical approach to recover complex shapes from 3d data with very few degrees of freedom (contr ol vertic es). we provide an analytic formulation and introduce the physical quantities required to develop the dynamic subdivision surface model which can be deformed by applying forces synthesized from the data. our experiments demonstrate that this new dynamic model has a promising future in shape recovery from volume and range data sets.
a bilinear illumination model for robust face recognition. we present a technique to generate an illumination subspace for arbitrary 3d faces based on the statistics of measured illuminations under variable lighting conditions from many subjects. a bilinear model based on the higher-order singular value decomposition is used to create a compact illumination subspace given arbitrary shape parameters from a parametric 3d face model. using a fitting procedure based on minimizing the distance of the input image to the dynamically changing illumination subspace, we reconstruct a shape-specific illumination subspace from a single photograph. we use the reconstructed illumination subspace in various face recognition experiments with variable lighting conditions and obtain accuracies which are very competitive with previous methods that require specific training sessions or multiple images of the subject.
stereo depth estimation: a confidence interval approach. we describe an estimation technique which, given a measurement of the depth of a target from a wide-field-of-view (wfov) stereo camera pair, produces a minimax risk fixed-size confidence interval estimate for the target depth. this work constitutes the first application to the computer vision domain of optimal fixed-size confidence-interval decision theory. the approach is evaluated in terms of theoretical capture probability and empirical capture frequency during actual experiments with a target on an optical bench. the method is compared to several other procedures including the kalman filter. the minimax approach is found to dominate all the other methods in performance. in particular, for the minimax approach, a very close agreement is achieved between theoretical capture probability and empirical capture frequency. this allows performance to be accurately predicted, greatly facilitating the system design, and delineating the tasks that may be performed with a given system.
pims and invariant parts for shape recognition. we present completely new very powerful solutions to two fundamental problems central to computer vision. 1. given data sets representing c objects to be stored in a database, and given a new data set for an object, determine the object in the database that is most like the object measured. we solve this problem through use of pims ("polynomial interpolated measures"), which is a new representation integrating implicit polynomial curves and surfaces, explicit polynomials, and discrete data sets which may be sparse. the method provides high accuracy at low computational cost. 2. given noisy 2d data along a curve (or 3d data along a surface), decompose the data into patches such that new data taken along affine transformations or euclidean transformations of the curve (or surface) can be decomposed into correponding patches. then recognition of complex or partially occluded objects can be done in terms of invariantly determined patches. we briefly outline a low computational cost image-database indexing-system based on this representation for objects having complex shape-geometry.
bayesian fusion of color and texture segmentations. in many applications one would like to use information from both color and texture features in order to segment an image. we propose a novel technique to combine "soft" segmentations computed for two or more features independently. our algorithm merges models according to a maximum descriptiveness criterion, and allows to choose any number of classes for the final grouping. this technique also allows to improve the quality of supervised classification based on one feature (e.g. color) by merging information from unsupervised segmentation based on another feature (e.g., texture.)
independent component analysis of textures. a common method for texture representation is to use the marginal probability densities over the outputs of a set of multi-orientation, multi-scale filters as a description of the texture. we propose a technique, based on independent components analysis, for choosing the set of filters that yield the most informative marginals, meaning that the product over the marginals most closely approximates the joint probability density function of the filter outputs. the algorithm is implemented using a steerable filter space. experiments involving both texture classification and synthesis show that compared to principal components analysis, ica provides superior performance for modeling of natural and synthetic textures.
a spectral technique for correspondence problems using pairwise constraints. we present an efficient spectral method for finding consistent correspondences between two sets of features. we build the adjacency matrix m of a graph whose nodes represent the potential correspondences and the weights on the links represent pairwise agreements between potential correspondences. correct assignments are likely to establish links among each other and thus form a strongly connected cluster. incorrect correspondences establish links with the other correspondences only accidentally, so they are unlikely to belong to strongly connected clusters. we recover the correct assignments based on how strongly they belong to the main cluster of m, by using the principal eigenvector ofm and imposing the mapping constraints required by the overall correspondence mapping (one-to-one or one-to-many). the experimental evaluation shows that our method is robust to outliers, accurate in terms of matching rate, while being much faster than existing methods.
recognizing surfaces using three-dimensional textons. we study the recognition of surfaces made from different materials such as concrete, rug, marble or leather on the basis of their textural appearance. such natural textures arise from spatial variation of two surface attributes: (1) reflectance and (2) surface normal. in this paper, we provide a unified model to address both these aspects of natural texture. the main idea is to construct a vocabulary of prototype tiny surface patches with associated local geometric and photometric properties. we call these 3d textons. examples might be ridges, grooves, spots or stripes or combinations thereof. associated with each texton is an appearance vector, which characterizes the local irradiance distribution, represented as a set of linear gaussian derivative filter outputs, under different lighting and viewing conditions.given a large collection of images of different materials, a clustering approach is used to acquire a small (on the order of 100) 3d texton vocabulary. given a few (1 to 4) images of any material, it can be characterized using these textons. we demonstrate the application of this representation for recognition of the material viewed under novel lighting and viewing conditions.
control in a 3d reconstruction system using selective perception. this paper presents a control structure for general purpose image understanding that addresses both the high level of uncertainty in local hypotheses and the computational complexity of image interpretation. the control of vision algorithms is performed by an independent subsystem that uses bayesian networks and utility theory to compute the marginal value of information provided by alternative operators and selects the ones with the highest value.we have implemented and tested this control structure with several aerial image data-sets. the results show that the knowledge base used by the system can be acquired using standard learning techniques and that the value-driven approach to the selection of vision algorithms leads to performance gains. moreover, the modular system architecture simplifies the addition of both control knowledge and new vision algorithms.
adaptive enhancement of cardiac magnetic resonance (cmr) images. this paper presents a wavelet-based framework for enhancing the coherent structures attributable to the target organ in cardiac magnetic resonance (mr) images. previous approaches focus on the rician nature of noise in magnitude mr images. image noise is but only one of the confounding factors that obscure the anatomical structures of the target organ. this paper models the image noise in a magnitude mr image in terms of two noise classes which occur over different ranges of signal intensity. an adaptive enhancement scheme is developed to achieve simultaneous attenuation of the effects of these factors and improvement in image contrast.
a probabilistic framework for edge detection and scale selection. we devise a statistical framework for edge detection by performing a statistical analysis of zero crossings of the second derivative of an image. this analysis enables us to estimate at each pixel of an image the probability that an edge passes through the pixel. we present a statistical analysis of the lindeberg operators that we use to compute image derivatives. we also introduce a confidence probability that tells us how reliable the edge probability is, given the image's noise level and the operator's scale. combining the edge and confidence probabilities leads to a probabilistic scale selection algorithm. we present the results of experiments on natural images.
unsupervised improvement of visual detectors using co-training. one significant challenge in the construction of visualdetection systems is the acquisition of sufficient labeleddata. this paper describes a new technique for trainingvisual detectors which requires only a small quantity of labeleddata, and then uses unlabeled data to improve performanceover time. unsupervised improvement is based onthe co-training framework of blum and mitchell, in whichtwo disparate classifiers are trained simultaneously. unlabeledexamples which are confidently labeled by one classifierare added, with labels, to the training set of the otherclassifier. experiments are presented on the realistic task ofautomobile detection in roadway surveillance video. in thisapplication, co-training reduces the false positive rate by afactor of 2 to 11 from the classifier trained with labeled dataalone.
learning how to inpaint from global image statistics. inpainting is the problem of filling-in holes in images. considerable progress has been made by techniques that use the immediate boundary of the hole and some prior information on images to solve this problem. these algorithms successfully solve the local inpainting problem but they must, by definition, give the same completion to any two holes that have the same boundary, even when the rest of the image is vastly different. in this paper we address a different, more global inpainting problem. how can we use the rest of the image in order to learn how to inpaint? we approach this problem from the context of statistical learning. given a training image we build an exponential family distribution over images that is based on the histograms of local features. we then use this image specific distribution to in paint the hole by finding the most probable image given the boundary and the distribution. the optimization is done using loopy belief propagation. we show that our method can successfully complete holes while taking into account the specific image statistics. in particular it can give vastly different completions even when the local neighborhoods are identical.
randomized ransac with sequential probability ratio test. a randomized model verification strategy for ransac is presented. the proposed method finds, like ransac, a solution that is optimal with user-controllable probability n. a provably optimal model verification strategy is designed for the situation when the contamination of data by outliers is known, i.e. the algorithm is the fastest possible (on average) of all randomized ransac algorithms guaranteeing 1 - n confidence in the solution. the derivation of the optimality property is based on wald¿s theory of sequential decision making. the r-ransac with sprt, which does not require the a priori knowledge of the fraction of outliers and has results close to the optimal strategy, is introduced. we show experimentally that on standard test data the method is 2 to 10 times faster than the standard ransac and up to 4 times faster than previously published methods.
on representation and matching of multi-coloured objects. a new representation for objects with multiple colours-the colour adjacency graph (cag)-is proposed. each node of the cag represents a single chromatic component of the image defined as a set of pixels forming a unimodal cluster in the chromatic scattergram. edges encode information about adjacency of colour components and their reflectance ratio. the cag is related to both the histogram and region adjacency graph representations. it is shown to be preserving and combining the best features of these two approaches while avoiding their drawbacks. the proposed approach is tested on a range of difficult object recognition and localisation problems involving complex imagery of non-rigid 3d objects under varied viewing conditions with excellent results.
surface reconstruction by integrating 3d and 2d data of multiple views. surface representation is needed for almost all modelingand visualization applications, but unfortunately, 3d datafrom a passive vision system are often insufficient for a traditionalsurface reconstruction technique that is designedfor densely scanned 3d point data. in this paper, we developa new method for surface reconstruction by combiningboth 3d data and 2d image information. the silhouetteinformation extracted from 2d images can also be integratedas an option if it is available. the new methodis a variational approach with a new functional integrating3d stereo data with 2d image information. this givesa more robust approach than existing methods using onlypure 2d information or 3d stereo data. we also propose abounded regularization method to implement efficiently thesurface evolution by level-set methods. the properties ofthe algorithms are discussed, proved for some cases, andempirically demonstrated through intensive experiments onreal sequences.
automatic recognition of human facial expressions. the paper presents a new idea for detecting an unknown human face in input imagery and recognizing his/her facial expression represented in the deformation of the two dimensional net, called potential net. the method deals with the facial information, faceness and expressions, as an overall pattern of the net activated by edges in a single input image of face, rather than from changes in the shape of the facial organs or their geometrical relationships. we build models of facial expressions from the deformation patterns in the potential net for face images in the training set of different expressions and then project them into emotion space. expression of an unknown subject can be recognized from the projection of the net for the image into the emotion space. the potential net is further used to model the common human face. the mosaic method representing energy in the net is used as a template for finding candidates for the face area and the candidates are verified their faceness by projecting them into emotion space in order to select the finalist. precise location of the face is determined by the histogram analysis of vertical and horizontal projections of edges.
kernel-based multifactor analysis for image synthesis and recognition. in many vision problems, the appearances of the observed images, e.g. the human facial images, are often influenced by multiple underlying factors. in this paper, a kernel-based factorization framework is proposed to analyze a multifactor dataset. specifically, we perform n-mode singular value decomposition (n-mode svd) in a higher dimensional feature space instead of the input space by using kernel approaches. given an input sample, its specific underlying factors which may be all absent in the training set can be extracted and translated from one sample to another by using kernel-based ¿translation¿. therefore our framework is suitable for tasks of new image synthesis and underlying factor recognition. we demonstrate the capabilities of our framework on ensembles of facial images subjected to different person identities, view-points and illuminations with high-quality synthetic faces and high face recognition accuracy.
ambiguity in reconstruction from images of six points. let s be a set of six points in space, let \psi be any hyperboloid of one sheet containing s, and let i be a sequence of images of s taken by an uncalibrated camera moving over \psi. then reconstruction from i is subject to a three way ambiguity which is unbroken as long as the optical centre of the camera remains on \psi.let p be an image of s taken from a point on \psi. the images "near" p define a tangent space which splits into a direct sum w_p\oplus n_p\oplus f_p, where wp corresponds to images near p for which the ambiguity is maintained, np corresponds to images for which the ambiguity is broken and fp corresponds to images which are physically impossible.
a recursive filter for phase velocity assisted shape-based tracking of cardiac non-rigid motion. a framework for tracking pointwise periodic non-rigid motion of the heart's left ventricular (lv) wall is presented which incorporates information from two different magnetic resonance imaging (mri) techniques. new developments in phase-contrast cine mr imaging have produced spatial maps of instantaneous velocity that heave proven accuracy within the myocardium, or wall, of the heart. this information is combined with shape-based matching techniques to provide improved estimates of trajectories, especially in regions where shape information is limited. these raw trajectories act as input to a recursive least squares (rls) filter which applies the constraints of temporal periodicity and spatial smoothness for the final estimate. the results of the rls filter are compared with the motion of actual implanted markers. comparisons are also made between exclusively shape-based filtered and phase-contrast enhanced trajectory estimates using both phantom and actual canine heart mr images.