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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013"

共 656 条记录，以下是461-470 订阅

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Accurate Camera Calibration from Multi-View Stereo and Bundle Adjustment

Accurate Camera Calibration from Multi-View Stereo and Bundl...

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

作者： Yasutaka Furukawa Jean Ponce Department of Computer Science and Beckman Institute University of Illinois Urbana-Champaign USA Willow Team Ecole Normale Supérieure LIENS (CNRS/ENS/INRIA UMR 8548) Paris France

the advent of high-resolution digital cameras and sophisticated multi-view stereo algorithms offers the promises of unprecedented geometric fidelity in image-based modeling tasks, but it also puts unprecedented demands on camera calibration to fulfill these promises. this paper presents a novel approach to camera calibration where top-down information from rough camera parameter estimates and the output of a publicly available multiview-stereo system [6] on scaled-down input images are used to effectively guide the search for additional image correspondences and significantly improve camera calibration parameters using a standard bundle adjustment algorithm [14]. the proposed method has been tested on several real datasets - including objects without salient features for which image correspondences cannot be found in a purely bottom-up fashion, and image-based modeling tasks - including the construction of visual hulls where thin structures are lost without our calibration procedure.

关键词： Calibration Layout Digital cameras Solid modeling Nonlinear distortion Robustness computer science Parameter estimation Testing Lenses

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Unsupervised Learning of Finite Mixtures Using Entropy Regularization and Its Application to Image Segmentation

Unsupervised Learning of Finite Mixtures Using Entropy Regul...

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

作者： Zhiwu Lu Yuxin Peng Jianguo Xiao Institute of Computer Science and Technology Peking University Beijing China

When fitting finite mixtures to multivariate data, it is crucial to select the appropriate number of components. Under regularization theory, we aim to resolve this "unsupervised" learning problem via regularizing the likelihood by the full entropy of posterior probabilities for finite mixture fitting. Two deterministic annealing implementations are further proposed for this entropy regularized likelihood (ERL) learning. through some asymptotic analysis of the deterministic annealing ERL (DAERL) learning, we find that the global minimization of the ERL function in an annealing way can lead to automatic model selection on finite mixtures and also make our DAERL algorithms less sensitive to initialization than the standard EM algorithm. the simulation experiments then demonstrate that our algorithms can provide some promising results just as our theoretic analysis. Moreover, our algorithms are evaluated in the application of unsupervised image segmentation and shown to outperform other state-of-the-art methods.

关键词： Unsupervised learning Entropy Image segmentation Annealing Application software Algorithm design and analysis Parameter estimation Minimization methods computer science Appropriate technology

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Learning A Geometry Integrated Image Appearance Manifold From A Small Training Set

Learning A Geometry Integrated Image Appearance Manifold Fro...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.1

作者： Yilei Xu Amit K. Roy-Chowdhury Department of Electrical Engineering University of California Riverside USA

While low-dimensional image representations have been very popular in computer vision, they suffer from two limitations: (i) they require collecting a large and varied training set to learn a low-dimensional set of basis functions, and (ii) they do not retain information about the 3D geometry of the object being imaged. In this paper, we show that it is possible to estimate low-dimensional manifolds that describe object appearance while retaining the geometrical information about the 3D structure of the object. By using a combination of analytically derived geometrical models and statistical learning methods, this can be achieved using a much smaller training set than most of the existing approaches. Specifically, we derive a quadrilinear manifold of object appearance that can represent the effects of illumination, pose, identity and deformation, and the basis functions of the tangent space to this manifold depend on the 3D surface normals of the objects. We show experimental results on constructing this manifold and how to efficiently track on it using an inverse compositional algorithm.

关键词： Computational geometry Information geometry computer vision Solid modeling Statistical learning Lighting Active appearance model Application software Data analysis Manifolds

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A Deformable Local Image Descriptor

A Deformable Local Image Descriptor

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.1

作者： Hong Cheng Zicheng Liu Nanning Zheng Jie Yang Carnegie Mellon University Pittsburgh USA Xi''an Jiaotong University Xi'an China Microsoft Research Limited Redmond USA

this paper presents a novel local image descriptor that is robust to general image deformations. A limitation with traditional image descriptors is that they use a single support region for each interest point. For general image deformations, the amount of deformation for each location varies and is unpredictable such that it is difficult to choose the best scale of the support region. To overcome this difficulty, we propose to use multiple support regions of different sizes surrounding an interest point. A feature vector is computed for each support region, and the concatenation of these feature vectors forms the descriptor for this interest point. Furthermore, we propose a new similarity measure model, Local-to-Global Similarity (LGS) model, for point matching that takes advantage of the multi-size support regions. Each support region acts as a 'weak' classifier and the weights of these classifiers are learned in an unsupervised manner. the proposed approach is evaluated on a number of images with real and synthetic deformations. the experiment results show that our method outperforms existing techniques under different deformations.

关键词： Lenses Image matching computer vision Object recognition Histograms Shape Matched filters Robustness Object detection Image recognition

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Video Segmentation: Propagation, Validation and Aggregation of a Preceding Graph

Video Segmentation: Propagation, Validation and Aggregation ...

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

作者： Siying Liu Guo Dong Chye Hwang Yan Sim Heng Ong Electrical and Computer Engineering National University of Singapore Singapore DSO National Laboratories Singapore

In this work, video segmentation is viewed as an efficient intra-frame grouping temporally reinforced by a strong inter-frame coherence. Traditional approaches simply regard pixel motions as another prior in the MRF-MAP framework. Since pixel pre-grouping is inefficiently performed on every frame, the strong correlation between inter-frame groupings is largely underutilized. We exploit the inter-frame correlation to propagate trustworthy groupings from the previous frame. A preceding graph is constructed and labeled for the previous frame. It is temporally propagated to the current frame and validated by similarity measures. All unlabeled subgraphs are spatially aggregated for the final grouping. Experimental results show that the proposed approach is highly efficient for spatio-temporal segmentation. It makes good use of temporal correlation and produces satisfactory grouping results.

关键词： Image segmentation Pixel Drives Labeling Equations Costs Partitioning algorithms Laboratories Current measurement Context modeling

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Using Circular Statistics for Trajectory Shape Analysis

Using Circular Statistics for Trajectory Shape Analysis

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

作者： Andrea Prati Simone Calderara Rita Cucchiara Di.S.M.I University of Modena Reggio Emilia Italy D.I.I University of Modena Reggio Emilia Italy

the analysis of patterns of movement is a crucial task for several surveillance applications, for instance to classify normal or abnormal people trajectories on the basis of their occurrence. this paper proposes to model the shape of a single trajectory as a sequence of angles described using a Mixture of Von Mises (MoVM) distribution. A complete EM (Expectation Maximization) algorithm is derived for MoVM parameters estimation and an on-line version proposed to meet real time requirement. Maximum-A-Posteriori is used to encode the trajectory as a sequence of symbols corresponding to the MoVM components. Iterative k-medoids clustering groups trajectories in a variable number of similarity classes. the similarity is computed aligning (with dynamic programming) two sequences and considering as symbol-to-symbol distance the Bhattacharyya distance between von Mises distributions. Extensive experiments have been performed on both synthetic and real data.

关键词： Statistical analysis pattern analysis Clustering algorithms Distributed computing Shape measurement Probability US Department of Transportation Surveillance Iterative algorithms Parameter estimation

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Visual Synset: Towards a Higher-level Visual Representation

Visual Synset: Towards a Higher-level Visual Representation

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.7

作者： Yan-Tao Zheng Ming Zhao Shi-Yong Neo Tat-Seng Chua Qi Tian National University of Singapore Singapore Google Inc. USA Institute for Infocomm Research Singapore

We present a higher-level visual representation, visual synset, for object categorization. the visual synset improves the traditional bag of words representation with better discrimination and invariance power. First, the approach strengthens the inter-class discrimination power by constructing an intermediate visual descriptor, delta visual phrase, from frequently co-occurring visual word-set with similar spatial context. Second, the approach achieves better intra-class invariance power, by clustering delta visual phrases into visual synset, based their probabilistic 'semantics', i.e. class probability distribution. Hence, the resulting visual synset can partially bridge the visual differences of images of same class. the tests on Caltech-101 and Pascal-VOC 05 dataset demonstrated that the proposed image representation can achieve good accuracies.

关键词： Probability distribution Object recognition Bridges Vector quantization Robustness Motorcycles Bicycles Testing Image representation Solid modeling

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A Parallel Decomposition Solver for SVM: Distributed Dual Ascend using Fenchel Duality

A Parallel Decomposition Solver for SVM: Distributed Dual As...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.1

作者： Tamir Hazan Amit Man Amnon Shashua School of Engineering & Computer Science Hebrew University of Jerusalem Israel

We introduce a distributed algorithm for solving large scale Support Vector Machines (SVM) problems. the algorithm divides the training set into a number of processing nodes each running independently an SVM sub-problem associated with its subset of training data. the algorithm is a parallel (Jacobi) block-update scheme derived from the convex conjugate (Fenchel Duality) form of the original SVM problem. Each update step consists of a modified SVM solver running in parallel over the sub-problems followed by a simple global update. We derive bounds on the number of updates showing that the number of iterations (independent SVM applications on sub-problems) required to obtain a solution of accuracy ε is O(log(1/ε)). We demonstrate the efficiency and applicability of our algorithms by running on large scale experiments on standardized datasets while comparing the results to the state-of-the-art SVM solvers.

关键词： Support vector machines Convergence Large-scale systems Kernel Concurrent computing Support vector machine classification Matrix decomposition Distributed algorithms Distributed computing Workstations

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Estimating Age, Gender, and Identity using First Name Priors

Estimating Age, Gender, and Identity using First Name Priors

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.7

作者： Andrew C. Gallagher Tsuhan Chen Carnegie Mellon University Pittsburgh PA USA

Recognizing people in images is one of the foremost challenges in computer vision. It is important to remember that consumer photography has a highly social aspect. the photographer captures images not in a random fashion, but rather to remember or document meaningful events in her life. the culture of the society of which the photographer is a part provides a strong context for recognizing the content of the captured images. We demonstrate one aspect of this cultural context by recognizing people from first names. the distribution of first names chosen for newborn babies evolves with time and is gender-specific. As a result, a first name provides a strong prior for describing the individual. Specifically, we use the U.S. Social Security Administration baby name database to learn priors for gender and age for 6693 first names. Most face recognition methods do not even consider the name of the individual of interest, or the name is treated merely as an identifier that provides no information about appearance. In contrast, we combine image-based gender and age classifiers with the cultural context information provided by first names to recognize people with no labeled examples. Our model uses image-based age and gender estimates for assigning first names to people and in turn, the age and gender estimates are improved.

关键词： Image recognition Face recognition Humans Cultural differences Pediatrics computer vision Image databases Photography Data security Information security

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A Mixed Generative-Discriminative Framework for Pedestrian Classification

A Mixed Generative-Discriminative Framework for Pedestrian C...

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

作者： Markus Enzweiler Dariu M. Gavrila Department of Math. University of Heidelberg Germany Environment Perception Daimler AG Ulm Germany Intelligent Systems Lab Faculty of Science University of Amsterdam Netherlands

this paper presents a novel approach to pedestrian classification which involves utilizing the synthesized virtual samples of a learned generative model to enhance the classification performance of a discriminative model. Our generative model captures prior knowledge about the pedestrian class in terms of a number of probabilistic shape and texture models, each attuned to a particular pedestrian pose. Active learning provides the link between the generative and discriminative model, in the sense that the former is selectively sampled such that the training process is guided towards the most informative samples of the latter. In large-scale experiments on real-world datasets of tens of thousands of samples, we demonstrate a significant improvement in classification performance of the combined generative-discriminative approach over the discriminative-only approach (the latter exemplified by a neural network with local receptive fields and a support vector machine using Haar wavelet features).

关键词： Shape Motion detection Principal component analysis pattern analysis Intelligent systems Large-scale systems Neural networks Support vector machines Support vector machine classification Network synthesis

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