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检索条件"任意字段=2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005"

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An efficient approach to learning inhomogeneous Gibbs model

An efficient approach to learning inhomogeneous Gibbs model

引用

2003 ieee computer society conference on computer vision and pattern recognition, cvpr 2003

作者： Liu, Ziqiang Chen, Hong Shum, Heung-Yeung Vision Lab. Dept. of Inf. Science Zhejiang University Zhejiang China

Inhomogeneous Gibbs model (IGM) [4] is an effective maximum entropy model in characterizing complex high-dimensional distributions. However, its training process is so slow that the applicability of IGM has been greatly restricted. In this paper, we propose an approach for fast parameter learning of IGM. In IGM learning, features are incrementally constructed to constrain the learnt distribution. When a new feature is added, Markov-chain Monte Carlo (MCMC) sampling is repeated to draw samples for parameter learning. In contrast, our new approach constructs a closed-form reference distribution using approximate information gain criteria. Because our reference distribution is very close to the optimal one, importance sampling can be used to accelerate the parameter optimization process. For problems with high-dimensional distributions, our approach typically achieves a speedup of two orders of magnitude compared to the original IGM. We further demonstrate the efficiency of our approach by learning a high-dimensional joint distribution of face images and their corresponding caricatures.

关键词： Face recognition

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Markerless kinematic model and motion capture from volume sequences

Markerless kinematic model and motion capture from volume se...

引用

2003 ieee computer society conference on computer vision and pattern recognition, cvpr 2003

作者： Chu, Chi-Wei Jenkins, Odest Chadwicke Matarić, Maja J. Ctr. for Robotics and Embedded Syst. Department of Computer Science University of Southern California Los Angeles CA 90089-0781 United States

We present an approach for model-free markerless motion capture of articulated kinematic structures. This approach is centered on our method for generating underlying nonlinear axes (or a skeleton curve) of a volume of genus zero (i.e., without holes). We describe the use of skeleton curves for deriving a kinematic model and motion (in the form of joint angles over time) from a captured volume sequence. Our motion capture method uses a skeleton curve, found in each frame of a volume sequence, to automatically determine kinematic postures. These postures are aligned to determine a common kinematic model for the volume sequence. The derived kinematic model is then reapplied to each frame in the volume sequence to find the motion sequence suited to this model. We demonstrate our method on several types of motion, from synthetically generated volume sequences with an arbitrary kinematic topology, to human volume sequences captured from a set of multiple calibrated cameras.

关键词： computer vision

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Nearest neighbor search for relevance feedback

Nearest neighbor search for relevance feedback

引用

2003 ieee computer society conference on computer vision and pattern recognition, cvpr 2003

作者： Tešić, Jelena Manjunath, B.S. Electrical and Computer Eng. Dept. Uni. of California Santa Barbara Santa Barbara CA 93106-9560 United States

We introduce the problem of repetitive nearest neighbor search in relevance feedback and propose an efficient search scheme for high dimensional feature spaces. Relevance feedback learning is a popular scheme used in content based image and video retrieval to support high-level concept queries. This paper addresses those scenarios in which a similarity or distance matrix is updated during each iteration of the relevance feedback search and a new set of nearest neighbors is computed. This repetitive nearest neighbor computation in high dimensional feature spaces is expensive, particularly when the number of items in the data set is large. In this context, we suggest a search algorithm that supports relevance feedback for the general quadratic distance metric. The scheme exploits correlations between consecutive nearest neighbor sets thus significantly reducing the overall search complexity. Detailed experimental results are provided using 60 dimensional texture feature data set.

关键词： Content based retrieval

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Shedding light on the weather

Shedding light on the weather

引用

2003 ieee computer society conference on computer vision and pattern recognition, cvpr 2003

作者： Narasimhan, Srinivasa G. Nayar, Shree K. Computer Science Dept. Columbia University New York NY United States

Virtually all methods in image processing and computer vision, for removing weather effects from images, assume single scattering of light by particles in the atmosphere. In reality, multiple scattering effects are significant. A common manifestation of multiple scattering is the appearance of glows around light sources in bad weather. Modeling multiple scattering is critical to understanding the complex effects of weather on images, and hence essential for improving the performance of outdoor vision systems. We develop a new physics-based model for the multiple scattering of light rays as they travel from a source to an observer. This model is valid for various weather conditions including fog, haze, mist and rain. Our model enables us to recover from a single image the shapes and depths of sources in the scene. In addition, the weather condition and the visibility of the atmosphere can be estimated. These quantities can, in turn, be used to remove the glows of sources to obtain a clear picture of the scene. Based on these results, we demonstrate that a camera observing-a distant source can serve as a "visual weather meter". The model and techniques described in this paper can also be used to analyze scattering in other media, such as fluids and tissues. Therefore, in addition to vision in bad weather, our work has implications for medical and underwater imaging.

关键词： Light scattering

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Estimating surface characteristics using physical reflectance models

Estimating surface characteristics using physical reflectanc...

引用

2003 ieee computer society conference on computer vision and pattern recognition, cvpr 2003

作者： Ragheb, Hossein Hancock, Edwin R. Department of Computer Science University of York York YO1 5DD United Kingdom

In this paper, we make use of the Beckmann-Kirchhoff and Davies scattering models to estimate surface properties for both dielectric and metallic surfaces based on reflectance measurements. In the case of metallic surfaces, we consider two refinements of the Davies theory which apply under different restrictions concerning the reflectance geometry. The first of these is due to Bennett and Porteus and applies for normal incidence and reflectance. The second is due to Torrance and applies when the incidence radiation is off normal. We then suggest three classes of materials for which the appropriate approximations may be used to estimate the surface roughness, the correlation length and the surface slope. Finally, we use the surface slope estimates to fit the Beckmann-Kirchhoff model to reflectance data. In contrast to previous methods which work at long wavelengths and use special purpose instrumentation, our methods can be performed using visible light and a digital camera.

关键词： Image analysis

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Continuous tracking within and across camera streams

Continuous tracking within and across camera streams

引用

2003 ieee computer society conference on computer vision and pattern recognition, cvpr 2003

作者： Kang, Jinman Cohen, Isaac Medioni, Gerard IRIS Computer Vision Group University of Southern California Los Angeles CA 90089-0273 United States

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： Video cameras

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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

2003 ieee computer society conference on computer vision and pattern recognition, cvpr 2003

作者： Vaswani, Namrata Chowdhury, Amit Roy Chellappa, Rama Dept. of Electrical and Comp. Eng. Center for Automation Research University of Maryland College Park MD 20742 United States

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 upto 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, infra-red or acoustic.

关键词： Object recognition

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Image repairing: Robust image synthesis by adaptive ND tensor voting

Image repairing: Robust image synthesis by adaptive ND tenso...

引用

2003 ieee computer society conference on computer vision and pattern recognition, cvpr 2003

作者： Jia, Jiaya Tang, Chi-Keung Vision and Graphics Group Computer Science Department Hong Kong Univ. of Sci. and Technol. Kowloon Hong Kong

We present a robust image synthesis method to automatically infer missing information from a damaged 2D image by tensor voting. Our method translates image color and texture information into an adaptive ND tensor, followed by a voting process that infers non-iteratively the optimal color values in the ND texture space for each defective pixel. ND tensor voting can be applied to images consisting of roughly homogeneous and periodic textures (e.g. a brick wall), as well as difficult images of natural scenes which contain complex color and texture information. To effectively tackle the latter type of difficult images, a two-step method is proposed. First, we perform texture-based segmentation in the input image, and extrapolate partitioning curves to generate a complete segmentation for the image. Then, missing colors are synthesized using ND tensor voting. Automatic tensor scale analysis is used to adapt to different feature scales inherent in the input. We demonstrate the effectiveness of our approach using a difficult set of real images.

关键词： Image reconstruction

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Document image enhancement using directional wavelet

Document image enhancement using directional wavelet

引用

2003 ieee computer society conference on computer vision and pattern recognition, cvpr 2003

作者： Wang, Qian Xia, Tao Li, Lida Tan, Chew Lim Department of Computer Science School of Computing Kent Ridge Singapore 117543 Singapore Centre for WAIP Department of Mathematics National University of Singapore Kent Ridge Singapore 117543 Singapore

This paper proposes a novel algorithm to clean up a large collection of historical handwritten documents kept in the National Archives of Singapore. Due to the seepage of ink over long period of storage, the front page of each document has been severely marred by the reverse side writing. Earlier attempts have been made to match both sides of a page to identify the offending strokes originating from the back so as to eliminate them with the aid of a wavelet transform. Perfect matching, however, is difficult due to document skews, differing resolutions, inadvertently missing out reverse side and warped pages during image capture. A new approach is now proposed to do away with double side mapping by using a directional wavelet transform that is able to distinguish the foreground and reverse side strokes much better than the conventional wavelet transform. Experiments have shown that the method indeed enhances the readability of each document significantly after the directional wavelet operation without the need for mapping with its reverse side.

关键词： Character recognition

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Independent component analysis in a facial local residue space

Independent component analysis in a facial local residue spa...

引用

2003 ieee computer society conference on computer vision and pattern recognition, cvpr 2003

作者： Kim, Tae-Kyun Kim, Hyunwoo Whang, Wonjun Kee, Seok-Cheol Kittler, Josef Human Computer Interaction Lab. Samsung Advanced Inst. of Technology Suwon Korea Republic of CVSSP University of Surrey Surrey United Kingdom

In this paper, we propose an ICA (Independent Component Analysis) based face recognition algorithm, which is robust to illumination and pose variation. Generally, it is well known that the first few eigenfaces represent illumination variation rather than identity. Most PCA (Principal Component Analysis)-based methods have overcome illumination variation by discarding the projection to a few leading eigenfaces. The space spanned after removing a few leading eigenfaces is called the "residual face space". We found that ICA in the residual face space provides more efficient encoding in terms of redundancy reduction and robustness to pose variation as well as illumination variation, owing to its ability to represent non-Gaussian statistics. Moreover, a face image is separated into several facial components, local spaces, and each local space is represented by the ICA bases (independent components) of its corresponding residual space. The statistical models of face images in local spaces are relatively simple and facilitate classification by a linear encoding. Various experimental results show that the accuracy of face recognition is significantly improved by the proposed method under large illumination and pose variations.

关键词： Face recognition

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