waashk/dblp huggingface dataset:
statistical background subtraction for a mobile observer. statistical background modelling and subtraction has proved to be a popular and effective class of algorithms for segmenting independently moving foreground objects outfrom a static background, without requiring any a priori information of the properties of foreground objects. this paper presents two contributions on this topic, aimed towards robotics where an active head is mounted on a mobile vehicle. in periods when the vehicle's wheels are not driven, camera translation is virtually zero, and background subtraction techniques are applicable. parts of this work are also highly relevant to surveillance and video conferencing. the first part of the paper presents an efficient probabilistic framework for when the camera pans and tilts. a unified approach is developed for handling various sources of error, including motion blur, sub-pixel camera motion, mixed pixels at object boundaries, and also uncertainty in background stabilisation caused by noise, unmodelled radial distortion and small translations of the camera. the second contribution regards a bayesian approach to specifically incorporate uncertainty concerning whether the background has yet been uncovered by moving foreground objects. this is an important requirement during initialisation of a system. we cannot assume that a background model is available in advance since that would involve storing models for each possible position, in every room, of the robot's operating environment. instead the background model must be generated online, very possibly in the presence of moving objects.
sparse image coding using a 3d non-negative tensor factorization. we introduce an algorithm for a non-negative 3d tensor factorization for the purpose of establishing a local parts feature decomposition from an object class of images. in the past such a decomposition was obtained using non-negative matrix factorization (nmf) where images were vectorized before being factored by nmf. a tensor factorization (ntf) on the other hand preserves the 2d representations of images and provides a unique factorization (unlike nmf which is not unique). the resulting "factors" from the ntf factorization are both sparse (like with nmf) but also separable allowing efficient convolution with the test image. results show a superior decomposition to what an nmf can provide on all fronts ¿ degree of sparsity, lack of ghost residue due to invariant parts and efficiency of coding of around an order of magnitude better. experiments on using the local parts decomposition for face detection using svm and adaboost classifiers demonstrate that the recovered features are discriminatory and highly effective for classification.
neighborhood preserving embedding. recently there has been a lot of interest in geometrically motivated approaches to data analysis in high dimensional spaces. we consider the case where data is drawn from sampling a probability distribution that has support on or near a submanifold of euclidean space. in this paper, we propose a novel subspace learning algorithm called neighborhood preserving embedding (npe). different from principal component analysis (pca) which aims at preserving the global euclidean structure, npe aims at preserving the local neighborhood structure on the data manifold. therefore, npe is less sensitive to outliers than pca. also, comparing to the recently proposed manifold learning algorithms such as isomap and locally linear embedding, npe is defined everywhere, rather than only on the training data points. furthermore, npe may be conducted in the original space or in the reproducing kernel hilbert space into which data points are mapped. this gives rise to kernel npe. several experiments on face database demonstrate the effectiveness of our algorithm.
learning a locality preserving subspace for visual recognition. previous works have demonstrated that the face recognition performance can be improved significantly in low dimensional linear subspaces. conventionally, principal component analysis (pca) and linear discriminant analysis (lda) are considered effective in deriving such a face subspace. however, both of them effectively see only the euclidean structure of face space. in this paper, we propose a new approach to mapping face images into a sub-space obtained by locality preserving projections (lpp) for face analysis. we call this laplacian face approach. different from pca and lda, lpp finds an embedding that preserves local information, and obtains a face space that best detects the essential manifold structure. in this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. we compare the proposed laplacian face approach with eigenface and fisherface methods on three test datasets. experimental results show that the proposed laplacianface approach provides a better representation and achieves lower error rates in face recognition.
wormholes in shape space: tracking through discontinuous changes in shape. existing object tracking algorithms generally use some form of local optimisation, assuming that an object's position and shape change smoothly over time. in some situations this assumption is not valid: the trackable shape of an object may change discontinuously, for example if it is the 2d silhouette of a 3d object.in this paper we propose a novel method for modelling temporal shape discontinuities explicitly. allowable shapes are represented as a union of (learned) bounded regions within a shape space. discontinuous shape changes are described in terms of transitions between these regions. transition probabilities are learned from training sequences and stored in a markov model. in this way we can create "wormholes" in shape space. tracking with such models is via an adaptation of the condensation algorithm.
natural image statistics for natural image segmentation. building on recent progress in modeling £lter responsestatistics of natural images we integrate a statistical modelinto a variational framework for image segmentation. incorporatedin a sound probabilistic distance measure themodel drives level sets toward meaningful segmentationsof complex textures and natural scenes. since each regioncomprises two model parameters only the approachis computationally ef£cient and enables the application ofvariational segmentation to a considerably larger class ofreal-world images. we validate the statistical basis of ourapproach on thousands of natural images and demonstratethat our model outperforms recent variational segmentationmethods based on second-order statistics.
learning non-negative sparse image codes by convex programming. example-based learning of codes that statistically encode general image classes is of vital importance for computational vision. recently, non-negative matrix factorization (nmf) was suggested to provide image codes that are both sparse and localized, in contrast to established non-local methods like pca. in this paper we adopt and generalize this approach to develop a novel learning framework that allows to efficiently compute sparsity-controlled invariant image codes by a well-defined sequence of convex conic programs. applying the corresponding parameter-free algorithm to various image classes results in semantically relevant and transformation-invariant image representations that are remarkably robust against noise and quantization.
real time pattern matching using projection kernels. a novel approach to pattern matching is presented, whichreduces time complexity by two orders of magnitude comparedto traditional approaches. the suggested approachuses an efficient projection scheme which bounds the distancebetween a pattern and an image window using veryfew operations. the projection framework is combined witha rejection scheme which allows rapid rejection of imagewindows that are distant from the pattern. experimentsshow that the approach is effective even under very noisyconditions. the approach described here can also be usedin classification schemes where the projection values serveas input features that are informative and fast to extract.
fast stereovision with subpixel-precision. a fast stereo algorithm based on aliasing effects of simple disparity estimators within a coherence-detection scheme is presented. the algorithm calculates dense disparity maps with subpixel-precision by performing local spatial filter operations and simple arithmetic transformations. performance similar to classical area-based approaches is achieved, but without the complicated hierarchical search structure typical for these approaches. the algorithm is completely parallel, the disparity values are calculated independently for each pixel. in addition, local validation counts for the disparity estimates and a fused cyclopean view of the scene are available within the proposed network structure for coherence-based stereo. 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
reconstruction from image sequences by means of relative depths. the paper deals with the problem of reconstructing the locations of n points in space from m different images without camera calibration. it shows how these problems can be put into a similar theoretical framework. a new concept, the reduced fundamental matrix, is introduced. it contains just 4 parameters and can be used to predict locations of points in the images and to make reconstruction. we also introduce the concept of reduced fundamental tensor which describes the relations between points in 3 images. it has 15 components and depends on 9 parameters. necessary and sufficient conditions for a tensor to be a reduced fundamental tensor are derived. this framework can be generalised to a sequence of images. the dependencies between the different representations are investigated. furthermore a canonical form of the camera matrices in a sequence are presented.
bayesian clustering of optical flow fields. we present a method for unsupervised learning of classesof motions in video. we project optical flow fields to a complete,orthogonal, a-priori set of basis functions in a probabilisticfashion, which improves the estimation of the projectionsby incorporating uncertainties in the flows. we thencluster the projections using a mixture of feature-weightedgaussians over optical flow fields. the resulting modelextracts a concise probabilistic description of the majorclasses of optical flow present. the method is demonstratedon a video of a person's facial expressions.
3d shape recognition and reconstruction based on line element geometry. this paper presents a new method for the recognition and reconstruction of surfaces from 3d data. line element geometry, which generalizes both line geometry and the laguerre geometry of oriented planes, enables us to recognize a wide class of surfaces (spiral surfaces, cones, helical surfaces, rotational surfaces, cylinders, etc.) by fitting linear subspaces in an appropriate seven-dimensional image space. in combination with standard techniques such as pca and ransac, line element geometry is employed to effectively perform the segmentation of complex objects according to surface type. examples show applications in reverse engineering of cad models and testing mathematical hypotheses concerning the exponential growth of sea shells.
geometric context from a single image. many computer vision algorithms limit their performance by ignoring the underlying 3d geometric structure in the image. we show that we can estimate the coarse geometric properties of a scene by learning appearance-based models of geometric classes, even in cluttered natural scenes. geometric classes describe the 3d orientation of an image region with respect to the camera. we provide a multiple-hypothesis framework for robustly estimating scene structure from a single image and obtaining confidences for each geometric label. these confidences can then be used to improve the performance of many other applications. we provide a thorough quantitative evaluation of our algorithm on a set of outdoor images and demonstrate its usefulness in two applications: object detection and automatic single-view reconstruction.
combining generative models and fisher kernels for object recognition. learning models for detecting and classifying object categories is a challenging problem in machine vision. while discriminative approaches to learning and classification have, in principle, superior performance, generative approaches provide many useful features, one of which is the ability to naturally establish explicit correspondence between model components and scene features ¿ this, in turn, allows for the handling of missing data and unsupervised learning in clutter. we explore a hybrid generative/discriminativeapproach using ¿fisher kernels¿ [1] which retains most of the desirable properties of generative methods, while increasing the classification performance through a discriminative setting. furthermore, we demonstrate how this kernel framework can be used to combine different types of features and models into a single classifier. our experiments, conducted on a number of popular benchmarks, show strong performance improvements over the corresponding generative approach and are competitive with the best results reported in the literature.
a multi-scale hybrid linear model for lossy image representation. this paper introduces a simple and efficient representation for natural images. we partition an image into blocks and treat the blocks as vectors in a high-dimensional space. we then fit a piece-wise linear model (i.e. a union of affine subspaces) to the vectors at each down-sampling scale. we call this a multi-scale hybrid linear model of the image. the hybrid and hierarchical structure of this model allows us effectively to extract and exploit multi-modal correlations among the imagery data at different scales. it conceptually and computationally remedies limitations of many existing image representation methods that are based on either a fixed linear transformation (e.g. dct, wavelets), an adaptive uni-modal linear transformation (e.g. pca), or a multi-modal model at a single scale. we will justify both analytically and experimentally why and how such a simple multi-scale hybrid model is able to reduce simultaneously the model complexity and computational cost. despite a small overhead for the model, our results show that this new model gives more compact representations for a wide variety of natural images under a wide range of signal-to-noise ratio than many existing methods, including wavelets.
large-scale event detection using semi-hidden markov models. we present a new approach to recognizing events invideos.we first detect and track moving objects in thescene.based on the shape and motion properties ofthese objects, we infer probabilities of primitive eventsframe-by-frame by using bayesian networks.compositeevents, consisting of multiple primitive events, overextended periods of time are analyzed by using a hidden,semi-markov finite state model.this results in morereliable event segmentation compared to the use of standardhmms in noisy video sequences at the cost of someincrease in computational complexity.we describe ourapproach to reducing this complexity.we demonstratethe effectiveness of our algorithm using both real-worldand pertubed data.
self-calibration and euclidean reconstruction using motions of a stereo rig. this paper describes a method to upgrade projective reconstruction to affine and to metric reconstructions using rigid gener almotions of a stereo rig. we make clear the algebraic relationships between projective reconstruction, the plane at infinity (affine reconstruction), camera calibration, and metric reconstruction. we show that all the computations can be carried out using standard linear resolution methods and that these methods compare favorably with nonlinear optimization methods in the presence of gaussian noise. we carry out a theoretical error analysis which quantify the relative importance of the accuracies of projective-to-affine conversion and affine-to-euclidean conversion. experiments with real data are consistent with the theoretical error analysis and with a sensitivity analysis performed with simulated data.
object pose: links between paraperspective and perspective. d.f. dementhon and l.s. davis (1995) proposed a method for determining the pose of a 3d object with respect to a camera from 3d to 2d point correspondences. the method consists of iteratively improving the pose computed with a weak perspective camera model to converge at the limit, to a pose estimation computed with a perspective camera model. we show that the method of dementhon and davis can be extended to paraperspective. the iterative paraperspective pose algorithm that we describe in detail has interesting properties both in terms of speed and rate of convergence. moreover, we introduce a simple way of taking into account the orthogonality constraint associated with the rotation matrix and we define the optimal experimental setup to be used in the presence of camera calibration errors.
statistical learning, localization, and identification of objects. this work describes a statistical approach to deal with learning and recognition problems in the field of computer vision. an abstract theoretical framework is provided, which is suitable for automatic model generation from examples, identification, and localization of objects. both, the learning and localization stage are formalized as parameter estimation tasks. the statistical learning phase is unsupervised with respect to the matching of model and scene features. the general mathematical description yields algorithms which can even treat parameter estimation problems from projected data. the experiments show that this probabilistic approach is suitable for solving 2d and 3d object recognition problems using grey-level images. the method can also be applied to 3d image processing issues using range images, i.e. 3d input data.
vector boosting for rotation invariant multi-view face detection. in this paper, we propose a novel tree-structured multi-view face detector (mvfd), which adopts the coarse-to-fine strategy to divide the entire face space into smaller and smaller subspaces. for this purpose, a newly extended boosting algorithm named vector boosting is developed to train the predictors for the branching nodes of the tree that have multi-components outputs as vectors. our mvfd covers a large range of the face space, say, +/- 45° rotation in plane (rip) and +/- 90° rotation off plane (rop), and achieves high accuracy and amazing speed (about 40 ms per frame on a 320 × 240 video sequence) compared with previous published works. as a result, by simply rotating the detector 90°, 180° and 270°, a rotation invariant (360° rip) mvfd is implemented that achieves real time performance (11 fps on a 320 × 240video sequence) with high accuracy.
spatial color indexing and applications. we suggest the use of the color correlogram as a generic indexing tool to tackle various computer vision problems. correlograms were shown to be very effective for content-basedimage retrieval [4]. we adapt the correlogram to handle the problems of image subregion querying, object localization, object tracking, and cut detection. experimental results suggest that the color correlogram is much more effective than the histogram for these applications, with insignificant additional computational, storage, or processing cost. we also provide a technique to cut down the storage requirement of correlograms so that it is the same as that of histograms, with only negligible performance penalty compared to the original correlogram.
detecting changes in aerial views of man-made structures. many applications require detecting structural changes in a scene over a period of time. comparing intensity values of successive images is not effective as such changes don't necessarily reflect actual changes at a site but might be caused by changes in the view point, illumination and seasons. we take the approach of comparing a 3-d model of the site, prepared from previous images, with new images to infer significant changes. this task is difficult as the images and the models have very different levels of abstract representations. our approach consists of several steps: registering a site model to a new image, model validation to confirm the presence of model objects in the image; structural change detection seeks to resolve matching problems and indicate possibly changed structures; and finally updating models to reflect the changes. our system is able to detect missing (or mis-modeled) buildings, changes in model dimensions, and new buildings under some conditions.
relational histograms for shape indexing. this paper is concerned with the retrieval of images from large databases based on their shape similarity to a query image. our approach is based on two dimensional histograms that encode both the local and global geometric properties of the shapes. the pairwise attributes are the directed segment relative angle and directed relative position the novelty of the proposed approach is to simultaneously use the relational and structural constraints, derived from an adjacency graph, to gate histogram contributions. we investiguate the retrieval cap abilities of the method for various queries. we also investigate the robustness of the method to segmentation errors. we conclude that a relational histo gram of pairwise segment attributespresents a very efficient way of indexing into large databases. the optimal configuration is obtained when the local features are constructed from six neighbouring segments pairs. moreover, a sensitivity analysis reveals that segmentation errors do not affect the retrieval performances.
sensitivity analysis for object recognition from large structural libraries. this paper studies the structural sensitivity of line-pattern recognition using shape-graphs. we compare the recognition performance for four different algorithms. each algorithm uses a set of pair wise geometric attributes and a neighborhood graph to represent the structure of the line patterns. the first algorithm uses a pair-wise geometric histogram, the second uses a relational histogram on the edges of the shape graph, the third compares the set of attributes on the edges of the shape graph and the final algorithm compares the arrangement of line correspondences using graph-matching. the different algorithms are compared under line deletion, line addition, line fragmentation and line end-point measurement errors. it is the graph-matching algorithm which proves to be the most effective.
computing visual correspondence: incorporating the probability of a false match. we describe a method for computing visual correspondence which employs a formal model of the probability of a false match. this model estimates the chance that the best match for each point could have occurred at random. the model is effective at identifying points in one image for which there is no corresponding point in the other image, as occurs at depth boundaries in stereo and at motion boundaries in optical flow. more generally, the model can be used to identify points where the best match is of poor quality, as occurs in regions of uniform texture. we describe the similarity measure used in the method and present the formal model of a false match. we also show examples of using the method to compute stereo disparity.