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Probabilistic spatial context models for scene content understanding

Probabilistic spatial context models for scene content under...

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Singhal Jiebo Luo Weiyu Zhu Electronic Imaging Products R&D Eastman Kodak Company USA Electronic Imaging Products R&D Eastman Kodak Company Department of Elec. Engineering And Computer Science University of Illinois Urbana-Champaign USA Dept. of Elec. Engg. And Compo. Science Urbana-Champaign

Scene content understanding facilitates a large number of applications, ranging from content-based image retrieval to other multimedia applications. Material detection refers to the problem of identifying key semantic material types (such as sky, grass, foliage, water, and snow in images). In this paper, we present a holistic approach to determining scene content, based on a set of individual material detection algorithms, as well as probabilistic spatial context models. A major limitation of individual material detectors is the significant number of misclassifications that occur because of the similarities in color and texture characteristics of various material types. We have developed a spatial context-aware material detection system that reduces misclassification by constraining the beliefs to conform to the probabilistic spatial context models. Experimental results show that the accuracy of materials detection is improved by 13% using the spatial context models over the individual material detectors themselves.

关键词： Context modeling Layout Snow Image retrieval Content based retrieval Detectors Object detection Humans Image recognition Image databases

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Object segmentation using graph cuts based active contours

Object segmentation using graph cuts based active contours

引用

conference on computer vision and pattern recognition (cvpr)

作者： Ning Xu R. Bansal N. Ahuja Beckman Institute & ECE Department University of Illinois Urbana-Champaign IL USA Department of Psychiatry Columbia University New York NY USA

In this paper we present a graph cuts based active contours (GCBAC) approach to object segmentation problems. Our method is a combination of active contours and the optimization tool of graph cuts and differs fundamentally from traditional active contours in that it uses graph cuts to iteratively deform the contour. Consequently, it has the following advantages. (1) It has the ability to jump over local minima and provide a more global result. (2) Graph cuts guarantee continuity and lead to smooth contours free of self-crossing and uneven spacing problems. Therefore, the internal force, which is commonly used in traditional energy functions to control the smoothness, is no longer needed, and hence the number of parameters is greatly reduced. (3). Our approach easily extends to the segmentation of three and higher dimensional objects. In addition, the algorithm is suitable for interactive correction and is shown to always converge. Experimental results and analyses are provided.

关键词： Object segmentation Active contours Optimization methods Image segmentation Robust stability computer vision Psychiatry Force control Background noise Automatic control

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Video-based face recognition using probabilistic appearance manifolds

Video-based face recognition using probabilistic appearance ...

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conference on computer vision and pattern recognition (cvpr)

作者： Kuang-Chih Lee J. Ho Ming-Hsuan Yang D. Kriegman Computer Science University of Illinois Urbana-Champaign Urbana-Champaign IL USA Computer Science & Engineering University of California San Diego La Jolla CA USA Honda Research Institute Mountain View CA USA

This paper presents a method to model and recognize human faces in video sequences. Each registered person is represented by a low-dimensional appearance manifold in the ambient image space, the complex nonlinear appearance manifold expressed as a collection of subsets (named pose manifolds), and the connectivity among them. Each pose manifold is approximated by an affine plane. To construct this representation, exemplars are sampled from videos, and these exemplars are clustered with a K-means algorithm; each cluster is represented as a plane computed through principal component analysis (PCA). The connectivity between the pose manifolds encodes the transition probability between images in each of the pose manifold and is learned from a training video sequences. A maximum a posteriori formulation is presented for face recognition in test video sequences by integrating the likelihood that the input image comes from a particular pose manifold and the transition probability to this pose manifold from the previous frame. To recognize faces with partial occlusion, we introduce a weight mask into the process. Extensive experiments demonstrate that the proposed algorithm outperforms existing frame-based face recognition methods with temporal voting schemes.

关键词： Face recognition Video sequences Image recognition computer science Principal component analysis Head Manifolds Humans Clustering algorithms Testing

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Tracking appearances with occlusions

Tracking appearances with occlusions

引用

conference on computer vision and pattern recognition (cvpr)

作者： Ying Wu Ting Yu Gang Hua Department of Electrical & Computer Engineering Northwestern University Evanston IL USA

ISBN: (纸本)0769519008

Occlusion is a difficult problem for appearance-based target tracking, especially when we need to track multiple targets simultaneously and maintain the target identities during tracking. To cope with the occlusion problem explicitly, this paper proposes a dynamic Bayesian network, which accommodates an extra hidden process for occlusion and stipulates the conditions on which the image observation likelihood is calculated. The statistical inference of such a hidden process can reveal the occlusion relations among different targets, which makes the tracker more robust against partial even complete occlusions. In addition, considering the fact that target appearances change with views, another generative model for multiple view representation is proposed by adding a switching variable to select from different view templates. The integration of the occlusion model and multiple view model results in a complex dynamic Bayesian network, where extra hidden processes describe the switch of targets' templates, the targets' dynamics, and the occlusions among different targets. The tracking and inferring algorithms are implemented by the sampling-based sequential Monte Carlo strategies. Our experiments show the effectiveness of the proposed probabilistic models and the algorithms.

关键词： Target tracking Bayesian methods Hidden Markov models Switches Monte Carlo methods Inference algorithms Maintenance engineering Robustness Application software Human computer interaction

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Continuous tracking within and across camera streams

Continuous tracking within and across camera streams

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conference on computer vision and pattern recognition (cvpr)

作者： Jinman Kang I. Cohen G. Medioni IRIS Computer Vision Group University of Southern California Los Angeles CA USA

This paper presents a new approach for continuous tracking of moving objects observed by multiple, heterogeneous cameras. Our approach simultaneously processes video streams from stationary and pan-tilt-zoom cameras. The detection of moving objects from moving camera streams is performed by defining an adaptive background model that takes into account the camera motion approximated by an affine transformation. We address the tracking problem by separately modeling motion and appearance of the moving objects using two probabilistic models. For the appearance model, multiple color distribution components are proposed for ensuring a more detailed description of the object being tracked. The motion model is obtained using a Kalman filter (KF) process, which predicts the position of the moving object. The tracking is performed by the maximization of a joint probability model. The novelty of our approach consists in modeling the multiple trajectories observed by the moving and stationary cameras in the same KF framework. It allows deriving a more accurate motion measurement for objects simultaneously viewed by the two cameras and an automatic handling of occlusions, errors in the detection and camera handoff. We demonstrate the performances of the system on several video surveillance sequences.

关键词： Cameras Streaming media Object detection Layout computer vision Motion detection Tracking Video surveillance Image resolution computerized monitoring

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Learning epipolar geometry from image sequences

Learning epipolar geometry from image sequences

引用

conference on computer vision and pattern recognition (cvpr)

作者： Y. Wexler A.W. Fitzgibbon A. Zisserman Visual Geometry Group Department of Engineering Science University of Oxford UK

We wish to determine the epipolar geometry of a stereo camera pair from image measurements alone. This paper describes a solution to this problem, which does not require a parametric model of the camera system, and consequently applies equally well to a wide class of stereo configurations. Examples in the paper range from a standard pinhole stereo configuration to more exotic systems combining curved mirrors and wide-angle lenses. The method described here allows epipolar curves to be learnt from multiple image pairs acquired by stereo cameras with fixed configuration. By aggregating information over the multiple image pairs, a dense map of the epipolar curves can be determined on the images. The algorithm requires a large number of images, but has the distinct benefit that the correspondence problem does not have to be explicitly solved. We show that for standard stereo configurations the results are comparable to those obtained from a state of the art parametric model method, despite the significantly weaker constraints on the non-parametric model. The new algorithm is simple to implement, so it may easily be employed on a new and possibly complex camera system.

关键词： Image sequences Cameras Parametric statistics Stereo vision Mirrors Lenses Computational geometry Solid modeling Optical distortion Optical noise

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Deformable object tracking using the boundary element method

Deformable object tracking using the boundary element method

引用

conference on computer vision and pattern recognition (cvpr)

作者： M.A. Greminger B.J. Nelson Department of Mechanical Engineering University of Minnesota Minneapolis MN USA Institute of Robotics and Intelligent Systems Swiss Federal Institute of Technology Zurich Switzerland

This paper presents a method to perform 2D (two-dimensional) deformable object tracking using the boundary element method (BEM). BEM, like the finite element method (FEM), is a technique to model an elastic solid. BEM differs from FEM in that only the contour of an object needs to be meshed for BEM, making this method attractive for computer vision problems. For FEM, the interior of the object must be meshed also. In order to track deformable objects, a deformable template is defined that uses BEM to model displacements. The template is registered to the image by applying a force field that deforms the template to match the image. This force field is found using an energy minimization approach. Even though the deformable template uses a linear elastic model, it can be used to track the deformations of objects with nonlinear material properties or in cases where there are large deformations. We demonstrate the performance of this method on objects with linear and nonlinear elastic properties. In addition, it is discussed how this method can be readily extended to 3D (three-dimensional) deformable object tracking.

关键词： Boundary element methods Deformable models Finite element methods computer vision Partitioning algorithms Integral equations Intelligent robots Image edge detection Stress Mechanical engineering

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Activity recognition using the dynamics of the configuration of interacting objects

Activity recognition using the dynamics of the configuration...

引用

conference on computer vision and pattern recognition (cvpr)

作者： N. Vaswani A. Roy Chowdhury R. Chellappa Department of Electrical and Computer Engineering Center for Automation Research University of Maryland College Park MD USA

Monitoring activities using video data is an important surveillance problem. A special scenario is to learn the pattern of normal activities and detect abnormal events from a very low resolution video where the moving objects are small enough to be modeled as point objects in a 2D plane. Instead of tracking each point separately, we propose to model an activity by the polygonal 'shape' of the configuration of these point masses at any time t, and its deformation over time. We learn the mean shape and the dynamics of the shape change using hand-picked location data (no observation noise) and define an abnormality detection statistic for the simple case of a test sequence with negligible observation noise. For the more practical case where observation (point locations) noise is large and cannot be ignored, we use a particle filter to estimate the probability distribution of the shape given the noisy observations up to the current time. Abnormality detection in this case is formulated as a change detection problem. We propose a detection strategy that can detect both 'drastic' and 'slow' abnormalities. Our framework can be directly applied for object location data obtained using any type of sensors - visible, radar, infrared or acoustic.

关键词： Noise shaping Radar detection Acoustic signal detection Shape Infrared sensors Monitoring Surveillance Object detection Event detection Radar tracking

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Motion deblurring using hybrid imaging

Motion deblurring using hybrid imaging

引用

conference on computer vision and pattern recognition (cvpr)

作者： M. Ben-Ezra S.K. Nayar Computer Science Department Columbia University New York NY USA

Motion blur due to camera motion can significantly degrade the quality of an image. Since the path of the camera motion can be arbitrary, deblurring of motion blurred images is a hard problem. Previous methods to deal with this problem have included blind restoration of motion blurred images, optical correction using stabilized lenses, and special CMOS sensors that limit the exposure time in the presence of motion. In this paper, we exploit the fundamental tradeoff between spatial resolution and temporal resolution to construct a hybrid camera that can measure its own motion during image integration. The acquired motion information is used to compute a point spread function (PSF) that represents the path of the camera during integration. This PSF is then used to deblur the image. To verify the feasibility of hybrid imaging for motion deblurring, we have implemented a prototype hybrid camera. This prototype system was evaluated in different indoor and outdoor scenes using long exposures and complex camera motion paths. The results show that, with minimal resources, hybrid imaging outperforms previous approaches to the motion blur problem.

关键词： Cameras Optical imaging Optical sensors CMOS image sensors Spatial resolution Prototypes Degradation Image restoration Lenses Image resolution

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Face relighting with radiance environment maps

Face relighting with radiance environment maps

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conference on computer vision and pattern recognition (cvpr)

作者： Zhen Wen Zicheng Liu T.S. Huang University of Illinois Urbana-Champaign IL USA Microsoft Research Limited Redmond WA USA

A radiance environment map pre-integrates a constant surface reflectance with the lighting environment. It has been used to generate photo-realistic rendering at interactive speed. However, one of its limitations is that each radiance environment map can only render the object, which has the same surface reflectance as what it integrates. We present a ratio-image based technique to use a radiance environment map to render diffuse objects with different surface reflectance properties. This method has the advantage that it does not require the separation of illumination from reflectance, and it is simple to implement and runs at interactive speed. In order to use this technique for human face relighting, we have developed a technique that uses spherical harmonics to approximate the radiance environment map for any given image of a face. Thus we are able to relight face images when the lighting environment rotates. Another benefit of the radiance environment map is that we can interactively modify lighting by changing the coefficients of the spherical harmonics basis. Finally we can modify the lighting condition of one person's face so that it matches the new lighting condition of a different person's face image assuming the two faces have similar skin albedos.

关键词： Reflectivity Humans Rendering (computer graphics) Face detection Skin Image generation computer graphics computer vision Lighting Harmonic analysis

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