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

共 1569 条记录，以下是1321-1330 订阅

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HISTOGRAM-BASED SEARCH: A COMPARATIVE STUDY

HISTOGRAM-BASED SEARCH: A COMPARATIVE STUDY

引用

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

作者： Mikhail Sizintsev Konstantinos G. Derpanis Andrew Hogue Department of Computer Science and Engineering York University Toronto ONT Canada Faculty of Business and Information Technology University of Ontario Institute of Technology (UOIT) Oshawa ONT Canada

Histograms represent a popular means for feature representation. this paper is concerned with the problem of exhaustive histogram-based image search. Several standard histogram construction methods are explored, including the conventional approach, Huang's method, and the state-of-the-art integral histogram. In addition, we present a novel multi-scale histogram-based search algorithm, termed the distributive histogram, that can be evaluated exhaustively in a fast and memory efficient manner. An extensive systematic empirical evaluation is presented that explores the computational and storage consequences of altering the search image and histogram bin sizes. Experiments reveal up to an eight-fold decrease in computation time and hundreds- to thousands-fold decrease of memory use of the proposed distributive histogram in comparison to the integral histogram. Finally, we conclude with a discussion on the relative merits between the various approaches considered in the paper.

关键词： Histograms Image storage Concurrent computing Filtering Hardware Object detection computer science Distributed computing Information technology Paper technology

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Radiometric Calibration Using Temporal Irradiance Mixtures

Radiometric Calibration Using Temporal Irradiance Mixtures

引用

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

作者： Bennett Wilburn Hui Xu Yasuyuki Matsushita Microsoft Research Asia Beijing China TsingHua University Beijing China

We propose a new method for sampling camera response functions: temporally mixing two uncalibrated irradiances within a single camera exposure. Calibration methods rely on some known relationship between irradiance at the camera image plane and measured pixel intensities. Prior approaches use a color checker chart with known reflectances, registered images with different exposure ratios, or even the irradiance distribution along edges in images. We show that temporally blending irradiances allows us to densely sample the camera response function with known relative irradiances. Our first method computes the camera response curve using temporal mixtures of two pixel intensities on an uncalibrated computer display. the second approach makes use of temporal irradiance mixtures caused by motion blur. Both methods require only one input image, although more images can be used for improved robustness to noise or to cover more of the response curve. We show that our methods compute accurate response functions for a variety of cameras.

关键词： Radiometry Calibration Cameras Layout Brightness Asia Pixel computer displays Histograms Distributed computing

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Randomized Trees for Human Pose Detection

Randomized Trees for Human Pose Detection

引用

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

作者： Gregory Rogez Jonathan Rihan Srikumar Ramalingam Carlos Orrite Philip H. S. Torr Computer Vision Lab-I3A University of Zaragoza Spain Department of Computing Oxford Brookes University UK

this paper addresses human pose recognition from video sequences by formulating it as a classification problem. Unlike much previous work we do not make any assumptions on the availability of clean segmentation. the first step of this work consists in a novel method of aligning the training images using 3D Mocap data. Next we define classes by discretizing a 2D manifold whose two dimensions are camera viewpoint and actions. Our main contribution is a pose detection algorithm based on random forests. A bottom-up approach is followed to build a decision tree by recursively clustering and merging the classes at each level. For each node of the decision tree we build a list of potentially discriminative features using the alignment of training images;in this paper we consider Histograms of Orientated Gradient (HOG). We finally grow an ensemble of trees by randomly sampling one of the selected HOG blocks at each node. Our proposed approach gives promising results with both fixed and moving cameras.

关键词： Humans computer vision Cameras Detection algorithms Decision trees Histograms Detectors Shape Motion detection Video sequences

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Spectral Methods for Semi-supervised Manifold Learning

Spectral Methods for Semi-supervised Manifold Learning

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

作者： Zhenyue Zhang Hongyuan Zha Min Zhang Department of Mathematics University of Zhejiang Hangzhou China College of Computing Georgia Tech Atlanta GA USA

Given a finite number of data points sampled from a low-dimensional manifold embedded in a high dimensional space together with the parameter vectors for a subset of the data points, we need to determine the parameter vectors for the rest of the data points. this problem is known as semi-supervised manifold learning, and in this paper we propose methods to handle this problem by solving certain eigenvalue-problems. Our proposed methods address two key issues in semi-supervised manifold learning: 1) fitting of the local affine geometric structures, and 2) preserving the global manifold structures embodied in the overlapping neighborhoods around each data points. We augment the alignment matrix of local tangent space alignment (LTSA) with the orthogonal projection based on the known parameter vectors, giving rise to the eigenvalue problem that characterizes the semi-supervised manifold learning problem. We also discuss the roles of different types of neighborhoods and their influence on the learning process. We illustrate the performance of the proposed methods using both synthetic data sets as well as data sets arising from applications in video annotations.

关键词： pattern recognition computer vision Mathematics Educational institutions Embedded computing Eigenvalues and eigenfunctions Character generation Unsupervised learning

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An Adaptive Learning Method for Target Tracking across Multiple Cameras

An Adaptive Learning Method for Target Tracking across Multi...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.4

作者： Kuan-Wen Chen Chih-Chuan Lai Yi-Ping Hung Chu-Song Chen Department of Computer Science and Information Engineering National Taiwan University Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taiwan Institute of Information Science Academia Sinica Taiwan

this paper proposes an adaptive learning method for tracking targets across multiple cameras with disjoint views. Two visual cues are usually employed for tracking targets across cameras: spatio-temporal cue and appearance cue. To learn the relationships among cameras, traditional methods used batch-learning procedures or hand-labeled correspondence, which can work well only within a short period of time. In this paper, we propose an unsupervised method which learns both spatio-temporal relationships and appearance relationships adaptively and can be applied to long-term monitoring. Our method performs target tracking across multiple cameras while also considering the environment changes, such as sudden lighting changes. Also, we improve the estimation of spatio-temporal relationships by using the prior knowledge of camera network topology.

关键词： Learning systems Target tracking Cameras Network topology Monitoring Brightness Transfer functions Training data computer science Information science

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Drift-free Tracking of Rigid and Articulated Objects

Drift-free Tracking of Rigid and Articulated Objects

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

作者： Juergen Gall Bodo Rosenhahn Hans-Peter Seidel Max-Planck-Institute of Computer Science Saarbrucken Germany

Model-based 3D tracker estimate the position, rotation, and joint angles of a given model from video data of one or multiple cameras. they often rely on image features that are tracked over time but the accumulation of small errors results in a drift away from the target object. In this work, we address the drift problem for the challenging task of human motion capture and tracking in the presence of multiple moving objects where the error accumulation becomes even more problematic due to occlusions. To this end, we propose an analysis-by-synthesis framework for articulated models. It combines the complementary concepts of patch-based and region-based matching to track both structured and homogeneous body parts. the performance of our method is demonstrated for rigid bodies, body parts, and full human bodies where the sequences contain fast movements, self-occlusions, multiple moving objects, and clutter. We also provide a quantitative error analysis and comparison with other model-based approaches.

关键词： Humans Target tracking Image motion analysis Cameras Biological system modeling Motion analysis Biomedical optical imaging computer science Error analysis Medical diagnosis

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On Errors-In-Variables Regression with Arbitrary Covariance and its Application to Optical Flow Estimation

On Errors-In-Variables Regression with Arbitrary Covariance ...

引用

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

作者： Bjorn Andres Claudia Kondermann Daniel Kondermann Ullrich Kothe Fred A. Hamprecht Christoph S. Garbe Interdisciplinary Center for Scientific Computing University of Heidelberg Germany

Linear inverse problems in computer vision, including motion estimation, shape fitting and image reconstruction, give rise to parameter estimation problems with highly correlated errors in variables. Established total least squares methods estimate the most likely corrections A and b to a given data matrix [A, b] perturbed by additive Gaussian noise, such that there exists a solution y with [A + A, b + b]y = 0. In practice, regression imposes a more restrictive constraint namely the existence of a solution x with [A + A]x = [b + b]. In addition, more complicated correlations arise canonically from the use of linear filters. We, therefore, propose a maximum likelihood estimator for regression in the general case of arbitrary positive definite covariance matrices. We show that A, b and x can be found simultaneously by the unconstrained minimization of a multivariate polynomial which can, in principle, be carried out by means of a Grobner basis. Results for plane fitting and optical flow computation indicate the superiority of the proposed method.

关键词： Image motion analysis computer errors Optical filters Optical computing Application software Inverse problems computer vision Motion estimation Shape Image reconstruction

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Unsupervised Modeling of Object Categories Using Link Analysis Techniques

Unsupervised Modeling of Object Categories Using Link Analys...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.4

作者： Gunhee Kim Christos Faloutsos Martial Hebert School of Computer Science Carnegie Mellon University Pittsburgh PA USA

We propose an approach for learning visual models of object categories in an unsupervised manner in which we first build a large-scale complex network which captures the interactions of all unit visual features across the entire training set and we infer information, such as which features are in which categories, directly from the graph by using link analysis techniques. the link analysis techniques are based on well-established graph mining techniques used in diverse applications such as WWW, bioinformatics, and social networks. the techniques operate directly on the patterns of connections between features in the graph rather than on statistical properties, e.g., from clustering in feature space. We argue that the resulting techniques are simpler, and we show that they perform similarly or better compared to state of the art techniques on common data sets. We also show results on more challenging data sets than those that have been used in prior work on unsupervised modeling.

关键词： Information analysis Complex networks World Wide Web Social network services Data mining computer science Large-scale systems Bioinformatics Visual perception Web search

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Silhouette-Based Camera Calibration from Sparse Views under Circular Motion

Silhouette-Based Camera Calibration from Sparse Views under ...

引用

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

作者： Po-Hao Huang Shang-Hong Lai Computer Vision Laboratory Department of Computer Science National Tsing Hua University Hsinchu Taiwan

In this paper, we propose a new approach to camera calibration from silhouettes under circular motion with minimal data. We exploit the mirror symmetry property and derive a common homography that relates silhouettes with epipoles under circular motion. With the epipoles determined, the homography can be computed from the frontier points induced by epipolar tangencies. On the other hand, given the homography, the epipoles can be located directly from the bi-tangent lines of silhouettes. With the homography recovered, the image invariants under circular motion and camera parameters can be determined. If the epipoles are not available, camera parameters can be determined by a low-dimensional search of the optimal homography in a bounded region. In the degenerate case, when the camera optical axes intersect at one point, we derive a closed-form solution for the focal length to solve the problem. By using the proposed algorithm, we can achieve camera calibration simply from silhouettes of three images captured under circular motion. Experimental results on synthetic and real images are presented to show its performance.

关键词： Cameras Calibration Mirrors Closed-form solution computer vision Laboratories computer science Image sequences Robustness Computational geometry

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Model-Based Hand Tracking with Texture, Shading and Self-occlusions

Model-Based Hand Tracking with Texture, Shading and Self-occ...

引用

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

作者： Martin de La Gorce Nikos Paragios David J. Fleet Laboratoire MAS Paris France INRIA Saclay Equipe GALEN Orsay France Department of Computer Science University of Toronto Canada

A novel model-based approach to 3D hand tracking from monocular video is presented. the 3D hand pose, the hand texture and the illuminant are dynamically estimated through minimization of an objective function. Derived from an inverse problem formulation, the objective function enables explicit use of texture temporal continuity and shading information, while handling important self-occlusions and time-varying illumination. the minimization is done efficiently using a quasi-Newton method, for which we propose a rigorous derivation of the objective function gradient. Particular attention is given to terms related to the change of visibility near self-occlusion boundaries that are neglected in existing formulations. In doing so we introduce new occlusion forces and show that using all gradient terms greatly improves the performance of the method. Experimental results demonstrate the potential of the formulation.

关键词： Image motion analysis Optical sensors Lighting Tracking Surface texture Shape computer science Inverse problems Minimization methods Man machine systems

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