waashk/dblp huggingface dataset:
a sparse probabilistic learning algorithm for real-time tracking. this paper addresses the problem of applying powerful pattern recognition algorithms based on kernels to efficient visual tracking. recently avidan [1] has shown that object recognizers using kernel-svms can be elegantly adapted to localization by means of spatial perturbation of the svm, using optic flow. whereas avidan's svm applies to each frame of a video independently of other frames, the benefits of temporal fusion of data are well known. this issue is addressed here by using a fully probabilistic 'relevance vector machine' (rvm) to generate observations with gaussian distributions that can be fused over time. to improve performance further, rather than adapting a recognizer, webuild a localizer directly using the regression form of the rvm. a classification svm is used in tandem, for object verification, and this provides the capability of automatic initialization and recovery. the approach is demonstrated in real-time face and vehicle tracking systems. the 'sparsity' of the rvms means that only a fraction of cpu time is required to track at frame rate. tracker output is demonstrated in a camera management task in which zoom and pan are controlled inresponse to speaker/vehicle position and orientation, over an extended period. the advantages of temporal fusion inthis system are demonstrated.
stochastic completion fields: a neural model of illusory contour shape and salience. we describe an algorithm and representation level theory of illusory contour shape and salience. unlike previous theories, our model is derived from a single assumption-namely, that the prior probability distribution of boundary completion shape can be modeled by a random walk in a lattice whose points are positions and orientations in the image plane (i.e. the space which one can reasonably assume is represented by neurons of the mammalian visual cortex). our model does not employ numerical relaxation or other explicit minimization, but instead relies on the fact that the probability that a particle following a random walk will pass through a given position and orientation on a path joining two boundary fragments can be computed directly as the product of two vector-field convolutions. we show that for the random walk we define, the maximum likelihood paths are curves of least energy, that is, on average, random walks follow paths commonly assumed to model the shape of illusory contours. a computer model is demonstrated on numerous illusory contour stimuli from the literature.
recognition and interpretation of parametric gesture. a new method for the representation, recognition, and interpretation of parameterized gesture is presented. by parameterized gesture we mean gestures that exhibit a meaniful variation; one example is a point gesture where the important parameter is the 2-dimnesional direction. our approach is to extend the standard hidden markov model method of gesture recognition by including a global parametric variation in the output probabilities of the states of the hmm. using a linear model to derive the theory, we formulate an expectation-maximization (em) method for training the parametric hmm. during testing, the parametric hmm simultaneously recognizes the gesture and estimates the quantifying parameters. using visually-derived and directly measured 3-dimensional hand position measurements as input, we present results on two different movements ¿ a size gesture and a point gesture ¿ and show robustness with respect to noise in the input features.
relational matching with dynamic graph structures. the paper describes a novel approach to relational matching problems in machine vision. rather than matching static scene descriptions, the approach adopts an active representation of the data to be matched. this representation is iteratively reconfigured to increase its degree of topological congruency with the model relational structure in a reconstructive matching process. the active reconfiguration of relational structures is controlled by a map update process. the final restored graph representation is optimal in the sense that it has maximum a posteriori probability with respect to the available attributes for the objects under match. the benefits of the technique are demonstrated experimentally on the matching of cluttered synthetic aperture radar data to a model in the form of a digital map. the operational limits of the method are established in a simulation study.
object categorization by learned universal visual dictionary. this paper presents a new algorithm for the automatic recognition of object classes from images (categorization). compact and yet discriminative appearance-based object class models are automatically learned from a set of training images. the method is simple and extremely fast, making it suitable for many applications such as semantic image retrieval, web search, and interactive image editing. itclassifies a region according to the proportions of different visual words (clusters in feature space). the specific visual words and the typical proportions in each object are learned from a segmented training set. the main contribution of this paper is two fold: i) an optimally compact visual dictionary is learned by pair-wise merging of visual words from an initially large dictionary. the final visual words are described by gmms. ii) a novel statistical measure of discrimination is proposed which is optimized by each merge operation. high classification accuracy is demonstrated for nine object classes on photographs of real objects viewed under general lighting conditions, poses and viewpoints. the set of test images used for validation comprise: i) photographs acquired by us, ii) images from the web and iii) images from the recently released pascal dataset. the proposed algorithm performs well on both texture-rich objects (e.g. grass, sky, trees) and structure-rich ones (e.g. cars, bikes, planes).
locus: learning object classes with unsupervised segmentation. we address the problem of learning object class models and object segmentations from unannotated images. we introduce locus (learning object classes with unsupervised segmentation) which uses a generative probabilistic model to combine bottom-up cues of color and edge with top-down cues of shape and pose. a key aspect of this model is that the object appearance is allowed to vary from image to image, allowing for significant within-class variation. by iteratively updating the belief in the object¿s position, size, segmentation and pose, locus avoids making hard decisions about any of these quantities and so allows for each to be refined at any stage. we show that locus successfully learns an object class model from unlabeled images, whilst also giving segmentation accuracies that rival existing supervised methods. finally, we demonstrate simultaneous recognition and segmentation in novel images using the learned models for a number of object classes, as well as unsupervised object discovery and tracking in video.
feature selection for unsupervised and supervised inference: the emergence of sparsity in a weighted-based approach. the problem of selecting a subset of relevant features in a potentially overwhelming quantity of data is classic and found in many branches of science including - examples in computer vision, text processing and more recently bio-informatics are abundant. in this work we present a definition of '"relevancy" based on spectral properties of the affinity (or laplacian) of the features' measurement matrix. the feature selection process is then based on a continuous ranking of the features defined by a least-squares optimization process. a remarkable property of the feature relevance function is that sparse solutions for the ranking values naturally emerge as a result of a "biased non-negativity" of a key matrix in the process. as a result, a simple least-squares optimization process converges onto a sparse solution, i.e., a selection of a subset of features which form a local maxima over the relevance function. the feature selection algorithm can be embedded in both unsupervised and supervised inference problems and empirical evidence show that the feature selections typically achieve high accuracy even when only a small fraction of the features are relevant.
3d surface topography from intensity images. the paper demonstrates how a new shape from shading scheme can be used to extract topographic information from 2d intensity imagery. the shape-from-shading scheme has two novel ingredients. firstly, it uses a geometric update procedure which allows the image irradiance equation to be satisfied as a hard constraint. this not only improves the data closeness of the recovered needle-map, but also removes the necessity for extensive parameter tuning. secondly, we use curvature information to impose topographic constraints on the recovered needle-map. the topographic information is captured using the shape index of j.j. koenderink and a.j. van doorn (1995) and consistency is imposed using a robust error function. we show that the new shape-from-shading scheme leads to a meaningful topographic labelling of 3d surface structures
8-point algorithm revisited: factorized 8-point algorithm. in this paper, a novel algorithm for the fundamental matrix estimation, called factorized 8-point algorithm, is presented. the factorized 8-point algorithm is composed of three steps: (1) the measurement matrix in the traditional 8-point algorithm is decomposed into two factor matrices; (2) by introducing some auxiliary variables, a new linear minimization problem is formed, where every element of its associated measurement matrix is simply either a measurement datum or a constant; (3) the fundamental matrix is determined by solving this minimization problem by a least squares method. like the traditional 8-point algorithm and hartley¿s normalized 8-point algorithm, the factorized8-point algorithm is also completely linear. but unlike the normalized 8-point algorithm, the factorized 8-point algorithm does not need any pre-normalization step. since every element of the measurement matrix in the factorized8-point algorithm is a measurement datum or a constant, no amplification of measurement error is involved; the factorized 8-point algorithm can boost effectively the robustness of the estimation. large numbers of experiments show that the factorized 8-point algorithm consistently outperforms the traditional 8-point algorithm. in addition, although the factorized 8-point algorithm is specially designed for fundamental matrix estimation, its basic principle can be generalized to other estimation problems in computer vision, such as camera projection matrix estimation, homography estimation, focus of expansion estimation, and trifocal tensor estimation.
tracking articulated body by dynamic markov network. a new method for visual tracking of articulated objectsis presented. analyzing articulated motion is challengingbecause the dimensionality increase potentially demandstremendous increase of computation. to ease this problem,we propose an approach that analyzes subparts locallywhile reinforcing the structural constraints at the meantime. the computational model of the proposed approachis based on a dynamic markov network, a generative modelwhich characterizes the dynamics and the image observationsof each individual subpart as well as the motion constraintsamong different subparts. probabilistic variationalanalysis of the model reveals a mean field approximationto the posterior densities of each subparts given visual evidence,and provides a computationally efficient way forsuch a difficult bayesian inference problem. in addition,we design mean field monte carlo (mfmc) algorithms, inwhich a set of low dimensional particle filters interact witheach other and solve the high dimensional problem collaboratively.extensive experiments on tracking human bodyparts demonstrate the effectiveness, significance and computationalefficiency of the proposed method.
optical flow estimation using wavelet motion model. a motion estimation algorithm using wavelet approximation as an optical flow model has been developed to estimate accurate dense optical flow from an image sequence. this wavelet motion model is particularly useful in estimating optical flows with large displacement. traditional pyramid methods which use the coarse-to-fine image pyramid by image burring in estimating optical flow often produce incorrect results when the coarse-level estimates contain large errors that cannot be corrected at the subsequent finer levels. this happens when regions of low texture become flat or certain patterns result in spatial aliasing due to imageblurring. our method, in contrast, uses large-to-small full-resolution regions without blurring images, and simultaneously optimizes the coarser and finer parts of optical flow so that the large and small motion can be estimated correctly. we compare results obtained by using our method with those obtained by using one of the leading optical flow methods, the szeliski pyramid spline-based method. the experiments include cases of small displacement (less than4 pixels under 128 × 128 image size or equivalent displacement under other image sizes), and those of large displacement (10 pixels). while both methods produce comparableresults when the displacements are small, our method out performs pyramid spline-based method when the displacements are large.
equivalence of julesz and gibbs texture ensembles. research on texture has been pursued along two different lines. the first line of research, pioneered by julesz (1962), seeks the essential ingredients in terms of features and statistics in human texture perception. this leads us to a mathematical definition of texture as a julesz ensemble. a julesz ensemble is the maximum set of images that share the same value of some basic feature statistics as the image \math, or equivalently it is a uniform distribution on this set. the second line of research studies statistical models, in particular, markov random field (mrf) and frame models (zhu, wu, and mumford 1997), to characterize texture patterns locally.in this article, we bridge the two lines by the fundamental principle of equivalence of ensembles in statistical mechanics (gibbs, 1902). we prove that 1). the conditional probability of an arbitrary image patch given its environment, under the julesz ensemble or the uniform model, is inevitably a frame (mrf) model, and 2). the limit of the frame (mrf) model, which we called the gibbs ensemble, is equivalent to a julesz ensemble as \math. thus the advantages of the two methodologies can be fully utilized.
visual learning given sparse data of unknown complexity. this study addresses the problem of unsupervised visual learning. it examines existing popular model order selection criteria before proposes two novel criteria for improving visual learning given sparse data and without any knowledge about model complexity. in particular, a rectified bayesian information criterion (bicr) and a completed likelihood akaike¿s information criterion (cl-aic) are formulated to estimate the optimal model order (complexity) for learning the dynamic structure of a visual scene. both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from very small to large. extensive experiments on learning a dynamic scene structure are carried out to demonstrate the effectiveness of bicr and cl-aic, compared to that of bic [15], aic [1], icl [3] and a mml based criterion [7].
video behaviour profiling and abnormality detection without manual labelling. a novel framework is developed for automatic behaviour profiling and abnormality sampling/detection without any manual labelling of the training dataset. natural grouping of behaviour patterns is discovered through unsupervised model selection and feature selection on the eigen-vectors of a normalised affinity matrix. our experiments demonstrate that a behaviour model trained using an unlabelled dataset is superior to those trained using the same but labelled dataset in detecting abnormality from an unseen video.
uncalibrated perspective reconstruction of deformable structures. reconstruction of 3d structures from uncalibrated image sequences has a wealthy history. most work has been focused on rigid objects or static scenes. this paper studies the problem of perspective reconstruction of deformable structures such as dynamic scenes from an uncalibrated image sequence. the task requires decomposing the image measurements into a composition of three factors: 3d deformable structures, rigid rotations and translations, and intrinsic camera parameters. we develop a factorization algorithm that consists of two steps. in the first step we recover the projective depths iteratively using the sub-space constraints embedded in the image measurements of the deformable structures. in the second step, we scale the image measurements by the reconstructed projective depths. we then extend the linear closed-form solution for weakperspective reconstruction [23] to factorize the scaled measurements and simultaneously reconstruct the deformable shapes and underlying shape model, the rigid motions, and the varying camera parameters such as focal lengths. the accuracy and robustness of the proposed method is demonstrated quantitatively on synthetic data and qualitatively on real image sequences.
two-frame wide baseline matching. this paper describes a novel approach to automatically recover corresponding feature points and epipolar geometry over two wide baseline frames. our contributions consist of several aspects: first, the use of an affine invariant feature, edge-corner, is introduced to provide a robust and consistent matching primitives. second, based on svd decomposition of affine matrix, the affine matching space between two corners can be approximately divided into two independent spaces by rotation angle and scaling factor. employing this property, a two-stage affine matching algorithm is designed to obtain robust matches over two frames. third, using the epipolar geometry estimated by these matches, more corresponding feature points are determined. based on these robust correspondences, the fundamental matrix is refined, and a series of virtual views of the scene are synthesized. finally, several experiments are presented to illustrate that a number of robust correspondences can be stably determined for two wide baseline images under significant camera motions with illumination changes, occlusions, and self-similarities. after testing a number of examples and comparing with the existing methods, the experimental results strongly demonstrate that our matching method outperforms the state-of-art algorithms for all of the test cases.
boosting chain learning for object detection. a general classification framework, called boostingchain, is proposed for learning boosting cascade. in thisframework, a "chain" structure is introduced to integratehistorical knowledge into successive boosting learning.moreover, a linear optimization scheme is proposed toaddress the problems of redundancy in boosting learningand threshold adjusting in cascade coupling. by thismeans, the resulting classifier consists of fewer weakclassifiers yet achieves lower error rates than boostingcascade in both training and test. experimentalcomparisons of boosting chain and boosting cascade areprovided through a face detection problem. thepromising results clearly demonstrate the effectivenessmade by boosting chain.
automatic 3d face modeling from video. in this paper, we develop an efficient technique for fully automatic recovery of accurate 3d face shape from videos captured by a low cost camera. the method is designed to work with a short video containing a face rotating from frontal view to profile view. the whole approach consists of three components. first, automatic initialization is performed in the first frame with approximately frontal face. then, to handle the case of low quality image captured by low cost camera, the 2d feature matching, head poses and underlying 3d face shape are estimated and refined iteratively in an efficient way based on image sequence segmentation. finally, to take advantage of the sparse structure of the proposed algorithm, sparse bundle adjustment technique is further employed to speed up the computation. we demonstrate the accuracy and robustness of the algorithm using a set of experiments.
hypergeometric filters for optical flow and affine matching. this paper proposes new "hypergeometric" filters for the problem of image matching under the translational and affine model. this new set of filters has the following advantages: (1) high-precision registration of two images under the translational and affine model. because the window effects are eliminated, we are able to achieve superb performance in both translational and affine matching. (2) affine matching without exhaustive search or image warping. due to the recursiveness of the filters in the spatial domain, we are able to analytically express the relation between filter outputs and the six affine parameters. this analytical relation enables us to directly compute these affine parameters. (3) generality. the approach we demonstrate here can be applied to a broad class of matching problems, as long as the transformation between the two image patches can be mathematically represented in the frequency domain.
tracking articulated hand motion with eigen dynamics analysis. this paper introduces the concept of eigen-dynamics andproposes an eigen dynamics analysis (eda) method to learnthe dynamics of natural hand motion from labelled sets ofmotion captured with a data glove. the result is parameterizedwith a high-order stochastic linear dynamic system(lds) consisting of five lower-order lds. each correspondingto one eigen-dynamics. based on the eda model, weconstruct a dynamic bayesian network (dbn) to analyzethe generative process of a image sequence of natural handmotion. using the dbn, a hand tracking system is implemented.experiments on both synthesized and real-worlddata demonstrate the robustness and effectiveness of thesetechniques.
parameterized modeling and recognition of activities. a framework for modeling and recognition of temporal activities is proposed. the modeling of sets of exemplar activities is achieved by parameterizing their representation in the form of principal components. recognition of spatio-temporal variants of modeled activities is achieved by parameterizing the search in the space of admissible transformations that the activities can undergo. experiments on recognition of articulated and deformable object motion from image motion parameters are presented.
okapi-chamfer matching for articulated object recognition. recent years have witnessed the rise of many effective text information retrieval systems. by treating local visual features as terms, training images as documents and input images as queries, we formulate the problem of object recognition into that of text retrieval. our formulation opens up the opportunity to integrate some powerful text retrieval tools with computer vision techniques. in this paper, we propose to improve the efficiency of articulated object recognition by an okapi-chamfer matching algorithm. the algorithm is based on the inverted index technique. the inverted index is a widely used way to effectively organize a collection of text documents. with the inverted index, only documents that contain query terms are accessed and used for matching. to enable inverted indexing in an image database, we build a lexicon of local visual features by clustering the features extracted from the training images. given a query image, we extract visual features and quantize them based on the lexicon, and then look up the inverted index to identify the subset of training images with non-zero matching score. to evaluate the matching scores in the subset, we combined the modified okapi weighting formula with the chamfer distance. the performance of the okapi-chamfer matching algorithm is evaluated on a hand posture recognition system. we test the system with both synthesized and real world images. quantitative results demonstrate the accuracy and efficiency of our system.