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Learning Temporal Sequence Model from Partially Labeled Data

Learning Temporal Sequence Model from Partially Labeled Data

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yifan Shi A. Bobick I. Essa CPL GVU Georgia Institute of Technology Atlanta GA USA

Graphical models are often used to represent and recognize activities. Purely unsupervised methods (such as HMMs) can be trained automatically but yield models whose internal structure - the nodes - are difficult to interpret semantically. Manually constructed networks typically have nodes corresponding to sub-events, but the programming and training of these networks is tedious and requires extensive domain expertise. In this paper, we propose a semi-supervised approach in which a manually structured, Propagation Network (a form of a DBN) is initialized from a small amount of fully annotated data, and then refined by an EM-based learning method in an unsupervised fashion. During node refinement (the M step) a boosting-based algorithm is employed to train the evidence detectors of individual nodes. Experiments on a variety of data types - vision and inertial measurements - in several tasks demonstrate the ability to learn from as little as one fully annotated example accompanied by a small number of positive but non-annotated training examples. The system is applied to both recognition and anomaly detection tasks.

关键词： Hidden Markov models Detectors computer vision Application software Semisupervised learning Boosting State-space methods Graphical models Learning systems Surveillance

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Image Denoising with Shrinkage and Redundant Representations

Image Denoising with Shrinkage and Redundant Representations

引用

conference on computer vision and pattern recognition (cvpr)

作者： M. Elad B. Matalon M. Zibulevsky Department of Computer Science Technion-Israel Institute of Technology Haifa Israel Department of Electrical Engineering Israel

Shrinkage is a well known and appealing denoising technique. The use of shrinkage is known to be optimal for Gaussian white noise, provided that the sparsity on the signal’s representation is enforced using a unitary transform. Still, shrinkage is also practiced successfully with nonunitary, and even redundant representations. In this paper we shed some light on this behavior. We show that simple shrinkage could be interpreted as the first iteration of an algorithm that solves the basis pursuit denoising (BPDN) problem. Thus, this work leads to a novel iterative shrinkage algorithm that can be considered as an effective pursuit method. We demonstrate this algorithm, both on synthetic data, and for the image denoising problem, where we learn the image prior parameters directly from the given image. The results in both cases are superior to several popular alternatives.

关键词： Image denoising Wavelet transforms Noise reduction Iterative algorithms Table lookup White noise Pursuit algorithms computer science Iterative methods Maximum a posteriori estimation

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Putting Objects in Perspective

Putting Objects in Perspective

引用

conference on computer vision and pattern recognition (cvpr)

作者： D. Hoiem A.A. Efros M. Hebert Robotics Institute Carnegie Mellon University USA

Image understanding requires not only individually estimating elements of the visual world but also capturing the interplay among them. In this paper, we provide a framework for placing local object detection in the context of the overall 3D scene by modeling the interdependence of objects, surface orientations, and camera viewpoint. Most object detection methods consider all scales and locations in the image as equally likely. We show that with probabilistic estimates of 3D geometry, both in terms of surfaces and world coordinates, we can put objects into perspective and model the scale and location variance in the image. Our approach reflects the cyclical nature of the problem by allowing probabilistic object hypotheses to refine geometry and vice-versa. Our framework allows painless substitution of almost any object detector and is easily extended to include other aspects of image understanding. Our results confirm the benefits of our integrated approach.

关键词： Layout Object detection Cameras Roads Context modeling Geometry Detectors computer vision Robot kinematics Solid modeling

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Outdoor vehicle navigation using passive 3D vision

Outdoor vehicle navigation using passive 3D vision

引用

conference on computer vision and pattern recognition (cvpr)

作者： M.J. Stephens R.J. Blissett D. Charnley E.P. Sparks J.M. Pike Pleassey Research and Technology Romsey Hampshire UK

A prototype 3D vision system which uses the structure-from-motion principle is described. The system is based on the extraction and matching of image tokens such as edges and corners. The 3D locations of the causative features in the viewed scene are deduced by triangulation and are updated with each observation by application of Kalman filters. The operation of the vision system is illustrated with results obtained from sequences of natural outdoor imagery. The output of the vision-system kernel is a rich 3D representation of feature points and edges in the scene. A method is described by which surfaces can be simply constructed from this information. Results indicate that the inferred surfaces can be readily segmented into navigable and nonnavigable regions. This segmentation can be used by a path planner to select a safe and economical route for subsequent vehicle travel. A hardware realization of the vision system is discussed.< >

关键词： Navigation Machine vision Road vehicles Remotely operated vehicles Layout Mobile robots Vehicle driving Sparks Prototypes Image segmentation

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Estimating Intrinsic Component Images using Non-Linear Regression

Estimating Intrinsic Component Images using Non-Linear Regre...

引用

conference on computer vision and pattern recognition (cvpr)

作者： M.F. Tappen E.H. Adelson W.T. Freeman MIT Computer Science and Artificial Intelligence Laboratory Cambridge MA USA

Images can be represented as the composition of multiple intrinsic component images, such as shading, albedo, and noise images. In this paper, we present a method for estimating intrinsic component images from a single image, which we apply to the problems of estimating shading and albedo images and image denoising. Our method is based on learning estimators that predict filtered versions of the desired image. Unlike previous approaches, our method does not require unnatural discretizations of the problem. We also demonstrate how to learn a weighting function that properly weights the local estimates when constructing the estimated image. For shading estimation, we introduce a new training set of real-world images. The accuracy of our method is measured both qualitatively and quantitatively, showing better performance on the shading/albedo separation problem than previous approaches. The performance on denoising is competitive with the current state of the art.

关键词： Layout Noise reduction Shape Surface treatment computer science Artificial intelligence Laboratories Image denoising Noise level Lighting

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Dynamic Appearance Modeling for Human Tracking

Dynamic Appearance Modeling for Human Tracking

引用

conference on computer vision and pattern recognition (cvpr)

作者： Hwasup Lim O.I. Camps M. Sznaier V.I. Morariu Department of Electrical Engineering Pennsylvania State University University Park PA USA Department of Computer Science University of Maryland College Park MD USA

Dynamic appearance is one of the most important cues for tracking and identifying moving people. However, direct modeling spatio-temporal variations of such appearance is often a difficult problem due to their high dimensionality and nonlinearities. In this paper we present a human tracking system that uses a dynamic appearance and motion modeling framework based on the use of robust system dynamics identification and nonlinear dimensionality reduction techniques. The proposed system learns dynamic appearance and motion models from a small set of initial frames and does not require prior knowledge such as gender or type of activity. The advantages of the proposed tracking system are illustrated with several examples where the learned dynamics accurately predict the location and appearance of the targets in future frames, preventing tracking failures due to model drifting, target occlusion and scene clutter.

关键词： Humans Target tracking Nonlinear dynamical systems Predictive models computer science Robustness Histograms Educational institutions Biological system modeling Layout

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Computing oriented texture fields

Computing oriented texture fields

引用

conference on computer vision and pattern recognition (cvpr)

作者： A.R. Rao B.G. Schunck Artificial Intelligence Laboratory Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA

A novel algorithm for computing the orientation field for a flowlike texture is presented. The basic idea behind the algorithm is to use an oriented filter, namely the gradient of the Gaussian, and perform manipulations on the resulting gradient vector field. The most important aspect of the algorithm is that it is provably optimal in estimating the local orientations of an oriented texture. An added strength of the algorithm is that it is simpler and has a better signal-to-noise ratio than previous approaches, because it utilizes fewer derivative operations. Also proposed is a measure of coherence which is based on the use of the angle and coherence images as intrinsic images. An analysis of oriented textures requires the computation of these intrinsic images as a first step. In this sense, the computation of the orientation field, resulting in the intrinsic images, is indispensable in the analysis of oriented textures. Results from a number of experiments indicate the usefulness of the angle and coherence intrinsic images.< >

关键词： Anisotropic magnetoresistance Image texture analysis Filters Image analysis Taxonomy Laplace equations Artificial intelligence Laboratories computer science Signal to noise ratio

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Properties and applications of shape recipes

Properties and applications of shape recipes

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Torralba W.T. Freeman MIT Artificial Intelligence Laboratory Cambridge MA USA

In low-level vision, the representation of scene properties such as shape, albedo, etc., are very high dimensional as they have to describe complicated structures. The approach proposed here is to let the image itself bear as much of the representational burden as possible. In many situations, scene and image are closely related and it is possible to find a functional relationship between them. The scene information can be represented in reference to the image where the functional specifies how to translate the image into the associated scene. We illustrate the use of this representation for encoding shape information. We show how this representation has appealing properties such as locality and slow variation across space and scale. These properties provide a way of improving shape estimates coming from other sources of information like stereo.

关键词： Shape Application software computer society computer vision pattern recognition

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Camera network calibration from dynamic silhouettes

Camera network calibration from dynamic silhouettes

引用

conference on computer vision and pattern recognition (cvpr)

作者： S.N. Sinha M. Pollefeys L. McMillan Department of Computer Science University of North Carolina Chapel Hill USA

In this paper we present an automatic method for calibrating a network of cameras from only silhouettes. This is particularly useful for shape-from-silhouette or visual-hull systems, as no additional data is needed for calibration. The key novel contribution of this work is an algorithm to robustly compute the epipolar geometry from dynamic silhouettes. We use the fundamental matrices computed by this method to determine the projective reconstruction of the complete camera configuration. This is refined into a metric reconstruction using self-calibration. We validate our approach by calibrating a four camera visual-hull system from archive data where the dynamic object is a moving person. Once the calibration parameters have been computed, we use a visual-hull algorithm to reconstruct the dynamic object from its silhouettes.

关键词： Cameras Calibration Shape Image reconstruction Video sequences Robustness Computational geometry computer science Layout Humans

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Inferring 3D body pose from silhouettes using activity manifold learning

Inferring 3D body pose from silhouettes using activity manif...

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Elgammal Chan-Su Lee Department of Computer Science Rutgers University New Brunswick NJ USA

We aim to infer 3D body pose directly from human silhouettes. Given a visual input (silhouette), the objective is to recover the intrinsic body configuration, recover the viewpoint, reconstruct the input and detect any spatial or temporal outliers. In order to recover intrinsic body configuration (pose) from the visual input (silhouette), we explicitly learn view-based representations of activity manifolds as well as learn mapping functions between such central representations and both the visual input space and the 3D body pose space. The body pose can be recovered in a closed form in two steps by projecting the visual input to the learned representations of the activity manifold, i.e., finding the point on the learned manifold representation corresponding to the visual input, followed by interpolating 3D pose.

关键词： Humans Image reconstruction Motion analysis Solid modeling Kinematics Cameras computer science Surveillance Biological system modeling Geometry

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