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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

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Statistical analysis on Stiefel and Grassmann Manifolds with applications in computer vision

Statistical analysis on Stiefel and Grassmann Manifolds with...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Pavan Turaga Ashok Veeraraghavan Rama Chellappa Center for Automation Research University of Maryland College Park USA

Many applications in computer vision and pattern recognition involve drawing inferences on certain manifold-valued parameters. In order to develop accurate inference algorithms on these manifolds we need to a) understand the geometric structure of these manifolds b) derive appropriate distance measures and c) develop probability distribution functions (pdf) and estimation techniques that are consistent with the geometric structure of these manifolds. In this paper, we consider two related manifolds - the Stiefel manifold and the Grassmann manifold, which arise naturally in several vision applications such as spatio-temporal modeling, affine invariant shape analysis, image matching and learning theory. We show how accurate statistical characterization that reflects the geometry of these manifolds allows us to design efficient algorithms that compare favorably to the state of the art in these very different applications. In particular, we describe appropriate distance measures and parametric and non-parametric density estimators on these manifolds. these methods are then used to learn class conditional densities for applications such as activity recognition, video based face recognition and shape classification.

关键词： Statistical analysis Application software computer vision Shape Face recognition pattern recognition Inference algorithms Probability distribution Image analysis Image matching

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Photometric Stereo With Non-Parametric and Spatially-Varying Reflectance

Photometric Stereo With Non-Parametric and Spatially-Varying...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.8

作者： Neil Alldrin Todd Zickler David Kriegman University of California San Diego USA Harvard University USA

We present a method for simultaneously recovering shape and spatially varying reflectance of a surface from photometric stereo images. the distinguishing feature of our approach is its generality;it does not rely on a specific parametric reflectance model and is therefore purely "data-driven". this is achieved by employing novel bi-variate approximations of isotropic reflectance functions. By combining this new approximation with recent developments in photometric stereo, we are able to simultaneously estimate an independent surface normal at each point, a global set of non-parametric "basis material" BRDFs, and per-point material weights. Our experimental results validate the approach and demonstrate the utility of bi-variate reflectance functions for general non-parametric appearance capture.

关键词： Photometry Reflectivity Shape Parametric statistics computer graphics Lighting Power system modeling Stereo vision Rendering (computer graphics) Face recognition

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Radiometric Calibration with Illumination Change for Outdoor Scene Analysis

Radiometric Calibration with Illumination Change for Outdoor...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.8

作者： Seon Joo Kim Jan-Michael Frahm Marc Pollefeys Department of Computer Science North Carolina State University Chapel Hill USA Department of Computer Science ETH Zurich Switzerland

the images of an outdoor scene collected over time are valuable in studying the scene appearance variation which can lead to novel applications and help enhance existing methods that were constrained to controlled environments. However, the images do not reflect the true appearance of the scene in many cases due to the radiometric properties of the camera: the radiometric response function and the changing exposure. We introduce a new algorithm to compute the radiometric response function and the exposure of images given a sequence of images of a static outdoor scene where the illumination is changing. We use groups of pixels with constant behaviors towards the illumination change for the response estimation and introduce a sinusoidal lighting variation model representing the daily motion of the sun to compute the exposures.

关键词： Radiometry Calibration Lighting Image analysis Layout Cameras computer vision Application software Sun Pixel

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Cost-Sensitive Face recognition

Cost-Sensitive Face Recognition

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Yin Zhang Zhi-Hua Zhou National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Traditional face recognition systems attempt to achieve a high recognition accuracy, which implicitly assumes that the losses of all misclassifications are the same. However, in many real-world tasks this assumption is not always reasonable. For example, it will be troublesome if a face-recognition-based door-locker misclassifies a family member as a stranger such that s/he were not allowed to enter the house;but it will be a much more serious disaster if a stranger were misclassified as a family member and allowed to enter the house. In this paper, we propose a framework which formulates the problem as a multi-class cost-sensitive learning task, and propose a theoretically sound method based on Bayes decision theory to solve this problem. Experimental results demonstrate the effectiveness and efficiency of the proposed method.

关键词： Face recognition Costs Machine learning Laboratories Decision theory Image recognition Data mining Classification algorithms Machine learning algorithms Loss measurement

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pattern Discovery in Motion Time Series via Structure-based Spectral Clustering

Pattern Discovery in Motion Time Series via Structure-based ...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.2

作者： Xiaozhe Wang Liang Wang Anthony Wirth Department of Computer Science and Software Engineering University of Melbourne Parkville VIC Australia

this paper proposes an approach called 'structure-based spectral clustering' to identify clusters in motion time series for sequential pattern discovery. the proposed approach deploys a 'statistical feature-based distance computation' for spectral clustering algorithm. Compared to traditional spectral clustering approaches, in which the similarity matrix is constructed from the original data points by applying some similarity functions, the proposed approach builds the matrix based on a finite set of feature vectors. When the proposed approach uses less data points and simpler similarity function to computing the similarity matrix input for spectral clustering, it can improve the computational efficiency in constructing the similarity graph in spectral clustering compared to conventional approach. Promising experimental results with high accuracy on real world data sets demonstrate the capability and effectiveness of the proposed approach for pattern discovery in motion video sequences.

关键词： Clustering algorithms Data mining Computational efficiency Time measurement Feature extraction Hidden Markov models Video sequences pattern recognition computer vision Indexing

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Motion Estimation for Multi-Camera Systems using Global Optimization

Motion Estimation for Multi-Camera Systems using Global Opti...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.9

作者： Jae-Hak Kim Hongdong Li Richard Hartley NICTA Australian National University Australia

We present a motion estimation algorithm for multi-camera systems consisting of more than one calibrated camera securely attached on a moving object. So, they move all together, but do not require to have overlapping views across the cameras. the geometrically optimal solution of the motion for the multi-camera systems under L{sub}∞ norm is provided in this paper using a global optimization technique which has been introduced recently in the computer vision research field. Taking advantage of an optimal estimate of the essential matrix through searching rotation space, we provide the optimal solution for translation by using linear programming and Branch & Bound algorithm. Synthetic and real data experiments are conducted, and they show more robust and improved performance than the previous methods.

关键词： Motion estimation Cameras Linear programming Vehicles computer vision Robustness Large-scale systems Broadband communication Australia Council Image reconstruction

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Image Partial Blur Detection and Classification

Image Partial Blur Detection and Classification

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.3

作者： Renting Liu Zhaorong Li Jiaya Jia Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong China

In this paper, we propose a partially-blurred-image classification and analysis framework for automatically detecting images containing blurred regions and recognizing the blur types for those regions without needing to perform blur kernel estimation and image deblurring. We develop several blur features modeled by image color, gradient, and spectrum information, and use feature parameter training to robustly classify blurred images. Our blur detection is based on image patches, making region-wise training and classification in one image efficient. Extensive experiments show that our method works satisfactorily on challenging image data, which establishes a technical foundation for solving several computer vision problems, such as motion analysis and image restoration, using the blur information.

关键词： Data mining Image analysis Kernel Image restoration Motion analysis Motion detection Image segmentation Information analysis Image color analysis computer science

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Integrated Feature Selection and Higher-order Spatial Feature Extraction for Object Categorization

Integrated Feature Selection and Higher-order Spatial Featur...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.2

作者： David Liu Gang Hua Paul Viola Tsuhan Chen Department of ECE Carnegie Mellon University USA Department of ECE Microsoft Live Laboratories Research USA

In computer vision, the bag-of-visual words image representation has been shown to yield good results. Recent work has shown that modeling the spatial relationship between visual words further improves performance. Previous work extracts higher-order spatial features exhaustively. However, these spatial features are expensive to compute. We propose a novel method that simultaneously performs feature selection and feature extraction. Higher-order spatial features are progressively extracted based on selected lower order ones, thereby avoiding exhaustive computation. the method can be based on any additive feature selection algorithm such as boosting. Experimental results show that the method is computationally much more efficient than previous approaches, without sacrificing accuracy.

关键词： Feature extraction Boosting Image representation Pipelines Pixel computer vision pattern recognition Random access memory Object recognition Testing

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Learning the Viewpoint Manifold for Action recognition

Learning the Viewpoint Manifold for Action Recognition

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.5

作者： Richard Souvenir Justin Babbs Department of Computer Science North Carolina State University Charlotte USA

Researchers are increasingly interested in providing video-based, view-invariant action recognition for human motion. Addressing this problem will lead to more accurate modeling and analysis of the type of unconstrained video commonly collected in the areas of athletics and medicine. Previous viewpoint-invariant methods use multiple cameras in both the training and testing phases of action recognition or require storing many examples of a single action from multiple viewpoints. In this paper, we present a framework for learning a compact representation of primitive actions (e.g., walk, punch, kick, sit) that can be used for video obtained from a single camera for simultaneous action recognition and viewpoint estimation. Using our method, which models the low-dimensional structure of these actions relative to viewpoint, we show recognition rates on a publicly available data set previously only acheieved using multiple simultaneous views.

关键词： Cameras Humans Testing Motion analysis Robustness computer science Biomedical imaging Image motion analysis computer vision Application software

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On Handling Uncertainty in the Fundamental Matrix for Scene and Motion Adaptive Pose Recovery

On Handling Uncertainty in the Fundamental Matrix for Scene ...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.6

作者： Sreenivas R. Sukumar Hamparsum Bozdogan David L. Page Andreas F. Koschan Mongi A. Abidi Imaging Robotics and Intelligent Systems Laboratory University of Tennessee Knoxville USA

the estimation of the fundamental matrix is the key step in feature-based camera ego-motion estimation for applications in scene modeling and vehicle navigation. In this paper, we present a new method of analyzing and further reducing the risk in the fundamental matrix due to the choice of a particular feature detector, the choice of the matching algorithm, the motion model, iterative hypothesis generation and verification paradigms. Our scheme makes use of model-selection theory to guide the switch to optimal methods for fundamental matrix estimation within the hypothesis-and-test architecture. We demonstrate our proposed method for vision-based robot localization in large-scale environments where the environment is constantly changing and navigation within the environment is unpredictable.

关键词： Uncertainty Layout Navigation Switches Cameras Vehicles Risk analysis Algorithm design and analysis computer vision Motion detection

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