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检索条件"任意字段=2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006"

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MML-Based Approach for High-Dimensional Unsupervised Learning Using the Generalized Dirichlet Mixture

MML-Based Approach for High-Dimensional Unsupervised Learnin...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： N. Bouguila D. Ziou Département d'Informatique Université de Sherbrook Sherbrooke QUE Canada

We consider the problem of determining the structure of high-dimensional data, without prior knowledge of the number of clusters. Data are represented by a finite mixture model based on the generalized Dirichlet distribution. The generalized Dirichlet distribution has a more general covariance structure than the Dirichlet distribution and offers high flexibility and ease of use for the approximation of both symmetric and asymmetric distributions. In addition, the mathematical properties of this distribution allow highdimensional modeling without requiring dimensionality reduction and thus without a loss of information. The number of clusters is determined using the Minimum Message length (MML) principle. Parameters estimation is done by a hybrid stochastic expectation-maximization (HSEM) algorithm. The model is compared with results obtained by other selection criteria (AIC, MDL and MMDL). The performance of our method is tested by real data clustering and by applying it to an image object recognition problem.

关键词： Unsupervised learning pattern recognition Covariance matrix Mathematical model computer vision Face detection Support vector machines Parameter estimation Stochastic processes Clustering algorithms

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A tensor decomposition for geometric grouping and segmentation

A tensor decomposition for geometric grouping and segmentati...

引用

conference on computer vision and pattern recognition (cvpr)

作者： V.M. Govindu Visakhapatnam Andhra Pradesh India

While spectral clustering has been applied successfully to problems in computer vision, their applicability is limited to pairwise similarity measures that form a probability matrix. However many geometric problems with parametric forms require more than two observations to estimate a similarity measure, e.g. epipolar geometry. In such cases we can only define the probability of belonging to the same cluster for an n-tuple of points and not just a pair, leading to an n-dimensional probability tensor. However spectral clustering methods are not available for tensors. In this paper we present an algorithm to infer a similarity matrix by decomposing the n-dimensional probability tensor. Our method exploits the super-symmetry of the probability tensor to provide a randomised scheme that does not require the explicit computation of the probability tensor. Our approach is fast and accurate and its applicability is illustrated on two significant problems, namely perceptually salient geometric grouping and parametric motion segmentation (like affine, epipolar etc).

关键词： Tensile stress computer vision Clustering methods Matrix decomposition Information geometry Clustering algorithms Motion segmentation Laplace equations Venus Data analysis

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A semi-supervised active learning framework for image retrieval

A semi-supervised active learning framework for image retrie...

引用

conference on computer vision and pattern recognition (cvpr)

作者： S.C.H. Hoi M.R. Lyu Department of Computer Science & Engineeringineering and Shun Hing Institute of Advanced Engineering Chinese University of Hong Kong New Territories Hong Kong China

Although recent studies have shown that unlabeled data are beneficial to boosting the image retrieval performance, very few approaches for image retrieval can learn with labeled and unlabeled data effectively. This paper proposes a novel semi-supervised active learning framework comprising a fusion of semi-supervised learning and support vector machines. We provide theoretical analysis of the active learning framework and present a simple yet effective active learning algorithm for image retrieval. Experiments are conducted on real-world color images to compare with traditional methods. The promising experimental results show that our proposed scheme significantly outperforms the previous approaches.

关键词： Image retrieval Support vector machines Semisupervised learning Information retrieval Machine learning Feedback Training data Support vector machine classification Supervised learning Boosting

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A flow-based approach to vehicle detection and background mosaicking in airborne video

A flow-based approach to vehicle detection and background mo...

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conference on computer vision and pattern recognition (cvpr)

作者： H. Yalcin M. Hebert R. Collins M.J. Black Robotics Institute Carnegie Mellon University Pittsburgh PA USA CSE Department Pennslyvania State University University Park PA USA Computer Science Brown University Providence RI USA

In this work, we address the detection of vehicles in a video stream obtained from a moving airborne platform. We propose a Bayesian framework for estimating dense optical flow over time that explicitly estimates a persistent model of background appearance. The approach assumes that the scene can be described by background and occlusion layers, estimated within an expectation-maximization framework. The mathematical formulation of the paper is an extension of the work in (H. Yalcin et al., 2005) where motion and appearance models for foreground and background layers are estimated simultaneously in a Bayesian framework.

关键词： Vehicle detection Image motion analysis Bayesian methods Optical sensors Optical signal processing Optical computing Motion estimation Robustness Streaming media Layout

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Synchronization and calibration of a camera network for 3D event reconstruction from live video

Synchronization and calibration of a camera network for 3D e...

引用

conference on computer vision and pattern recognition (cvpr)

作者： S.N. Sinha M. Pollefeys Department of Computer Science UNC Chapel Hill USA

We present an approach for automatic reconstruction of a dynamic event using multiple video cameras recording from different viewpoints. Our approach recovers all the necessary information by analyzing the motion of the silhouettes in the multiple video streams. The first step consists of computing the calibration and synchronization for pairs of cameras. We compute the temporal offset and epipolar geometry using an efficient RANSAC-based algorithm to search for the epipoles as well as for robustness. In the next stage the calibration and synchronization for the complete camera network is recovered and then refined through maximum likelihood estimation. Finally, a visual hull algorithm is used to the recover the dynamic shape of the observed object.

关键词： Calibration Cameras Video recording Information analysis Motion analysis Streaming media Computational geometry Robustness Maximum likelihood estimation Shape

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Full body tracking from multiple views using stochastic sampling

Full body tracking from multiple views using stochastic samp...

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conference on computer vision and pattern recognition (cvpr)

作者： R. Kehl M. Bray L. Van Gool Computer Vision Laboratory (BIWI) ETH Zurich Switzerland Department of Computing Oxford Brookes University Oxford UK ESAT-PSI University of Leuven Belgium

We present a novel approach for full body pose tracking using stochastic sampling. A volumetric reconstruction of a person is extracted from silhouettes in multiple video images. Then, an articulated body model is fitted to the data with stochastic meta descent (SMD) optimization. By comparing even a simplified version of SMD to the commonly used Levenberg-Marquardt method, we demonstrate the power of stochastic compared to deterministic sampling, especially in cases of noisy and incomplete data. Moreover, color information is added to improve the speed and robustness of the tracking. Results are shown for several challenging sequences, with tracking of 24 degrees of freedom in less than 1 second per frame.

关键词： Stochastic processes Sampling methods Robustness Hardware Cameras computer vision Image sampling Humans Application software Target tracking

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Learning with constrained and unlabelled data

Learning with constrained and unlabelled data

引用

conference on computer vision and pattern recognition (cvpr)

作者： T. Lange M.H.C. Law A.K. Jain J.M. Buhmann Institute of Computational Science ETH Zurich Zurich Switzerland Department of Computer Science and Engineering Michigan State University East Lansing MI USA

Classification problems abundantly arise in many computer vision tasks eing of supervised, semi-supervised or unsupervised nature. Even when class labels are not available, a user still might favor certain grouping solutions over others. This bias can be expressed either by providing a clustering criterion or cost function and, in addition to that, by specifying pairwise constraints on the assignment of objects to classes. In this work, we discuss a unifying formulation for labelled and unlabelled data that can incorporate constrained data for model fitting. Our approach models the constraint information by the maximum entropy principle. This modeling strategy allows us (i) to handle constraint violations and soft constraints, and, at the same time, (ii) to speed up the optimization process. Experimental results on face classification and image segmentation indicates that the proposed algorithm is computationally efficient and generates superior groupings when compared with alternative techniques.

关键词： computer vision Cost function Image segmentation Labeling computer science Fitting Entropy Constraint optimization Face detection Object recognition

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UMD Experiments with FRGC Data

UMD Experiments with FRGC Data

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： G. Aggarwal S. Biswas R. Chellappa Center for Automation Research University of Maryland College Park MD USA

Although significant work has been done in the field of face recognition, the performance of state-of-the art face recognition algorithms is not good enough to be effective in operational systems. Though most algorithms work well for controlled images, they are quite susceptible to changes in illumination and pose. Face recognition Grand Challenge (FRGC) is an effort to examine such issues to suitably guide future research in the area. This paper describes the efforts made at UMD in this direction. We present our results on several experiments suggested in FRGC. We believe that though pattern classification techniques play an extremely significant role in automatic face recognition under controlled conditions, physical modeling is required to generalize across varying situations. Accordingly, we describe a generative approach to recognize faces across varying illumination. Unlike most current methods, our method does not ignore shadows. Instead we use them to our benefit by modeling attached shadows in our formulation.

关键词： Face recognition Lighting Shape Automatic control Reflectivity Automation Educational institutions Art pattern classification Humans

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Tracking multiple mouse contours (without too many samples)

Tracking multiple mouse contours (without too many samples)

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conference on computer vision and pattern recognition (cvpr)

作者： K. Branson S. Belongie Department of Computer Science and Engineering University of California San Diego La Jolla CA USA

We present a particle filtering algorithm for robustly tracking the contours of multiple deformable objects through severe occlusions. Our algorithm combines a multiple blob tracker with a contour tracker in a manner that keeps the required number of samples small. This is a natural combination because both algorithms have complementary strengths. The multiple blob tracker uses a natural multi-target model and searches a smaller and simpler space. On the other hand, contour tracking gives more fine-tuned results and relies on cues that are available during severe occlusions. Our choice of combination of these two algorithms accentuates the advantages of each. We demonstrate good performance on challenging video of three identical mice that contains multiple instances of severe occlusion.

关键词： Mice computer vision Video sequences computer science Filtering algorithms Robustness Particle tracking Biomedical monitoring computerized monitoring Animals

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Structured Light Based Depth Edge Detection for Object Shape Recovery

Structured Light Based Depth Edge Detection for Object Shape...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Cheolhwon Kim Jiyoung Park Juneho Yi M. Turk School of Information and Communication Engineering Biometrics Engineering Research Center Sungkyunkwan University South Korea Computer Science Department University of California Santa Barbara CA USA

This research features a novel approach that efficiently detects depth edges in real world scenes. Depth edges play a very important role in many computer vision problems because they represent object contours. We strategically project structured light and exploit distortion of light pattern in the structured light image along depth discontinuities to reliably detect depth edges. Distortion along depth discontinuities may not occur or be large enough to detect depending on the distance from the camera or projector. For practical application of the proposed approach, we have presented methods that guarantee the occurrence of the distortion along depth discontinuities for a continuous range of object location. Experimental results show that the proposed method accurately detect depth edges of human hand and body shapes as well as general objects.

关键词： Image edge detection Shape Layout Gabor filters Cameras computer vision Object detection Biometrics computer science Humans

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