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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441-450 订阅

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Markerless Motion Capture of Man-Machine Interaction

Markerless Motion Capture of Man-Machine Interaction

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

作者： Bodo Rosenhahn Christian Schmaltz thomas Brox Joachim Weickert Daniel Cremers Hans-Peter Seidel MPI Computer Science Saarbrucken Germany Mathematical Image Analysis University of Saarland Saarbrucken Germany Intelligent Systems University of Dresden Dresden Germany Computer Vision University of Bonn Bonn Germany

this work deals with modeling and markerless tracking of athletes interacting with sports gear. In contrast to classical markerless tracking, the interaction with sports gear comes along with joint movement restrictions due to additional constraints: while humans can generally use all their joints, interaction with the equipment imposes a coupling between certain joints. A cyclist who performs a cycling pattern is one example: the feet are supposed to stay on the pedals, which are again restricted to move along a circular trajectory in 3D-space. In this paper, we present a markerless motion capture system that takes the lower-dimensional pose manifold into account by modeling the motion restrictions via soft constraints during pose optimization. Experiments with two different models, a cyclist and a snowboarder, demonstrate the applicability of the method. Moreover, we present motion capture results for challenging outdoor scenes including shadows and strong illumination changes.

关键词： Man machine systems Kinematics Mathematical model Tracking Humans Layout Joints Biological system modeling computer vision Gears

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Optimizing Discrimination-Efficiency Tradeoff in Integrating Heterogeneous Local Features for Object Detection

Optimizing Discrimination-Efficiency Tradeoff in Integrating...

引用

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

作者： Bo Wu Ram Nevatia Institute for Robotics and Intelligent Systems University of Southern California Los Angeles CA USA

A large variety of image features has been invented for detection of objects of a known class. We propose a framework to optimize the discrimination-efficiency tradeoff in integrating multiple, heterogeneous features for object detection. Cascade structured detectors are learned by boosting local feature based weak classifiers. Each weak classifier corresponds to a local image region, from which several different types of features are extracted. the weak classifier makes predictions by examining the features one by one;this classifier goes to the next feature only when the prediction from the already examined features is not confident enough. the order in which the features are evaluated is determined based on their computational cost normalized classification powers. We apply our approach to two object classes, pedestrians and cars. the experimental results show that our approach outperforms the state-of-the-art methods.

关键词： computer vision Object detection Histograms Intelligent robots Detectors Boosting Face detection Support vector machines Support vector machine classification Kernel

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Minimal Solutions for Generic Imaging Models

Minimal Solutions for Generic Imaging Models

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

作者： Srikumar Ramalingam Peter Sturm Oxford Brookes University UK INRIA Grenoble Rhône-Alpes France

A generic imaging model refers to a non-parametric camera model where every camera is treated as a set of unconstrained projection rays. Calibration would simply be a method to map the projection rays to image pixels;such a mapping can be computed using plane based calibration grids. However, existing algorithms for generic calibration use more point correspondences than the theoretical minimum. It has been well-established that non-minimal solutions for calibration and structure-from-motion algorithms are generally noise-prone compared to minimal solutions. In this work we derive minimal solutions for generic calibration algorithms. Our algorithms for generally central cameras use 4 point correspondences in three calibration grids to compute the motion between the grids. Using simulations we show that our minimal solutions are more robust to noise compared to non-minimal solutions. We also show very accurate distortion correction results on fisheye images.

关键词： Calibration Cameras Pixel Grid computing Noise robustness Computational modeling computer vision Optical distortion Noise generators Optical imaging

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High-arity Interactions, Polyhedral Relaxations, and Cutting Plane Algorithm for Soft Constraint Optimisation (MAP-MRF)

High-arity Interactions, Polyhedral Relaxations, and Cutting...

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

作者： Tomas Werner Center for Machine Perception Department of Cybernetics Czech Technical University Praque Czech Republic

LP relaxation approach to soft constraint optimisation (i.e. MAP-MRF) has been mostly considered only for binary problems. We present its generalisation to n-ary problems, including a simple algorithm to optimise the LP bound, n-ary max-sum diffusion. As applications, we show that a hierarchy of gradually tighter polyhedral relaxations of MAP-MRF is obtained by adding zero interactions. We propose a cutting plane algorithm, where cuts correspond to adding zero interactions and the separation problem to finding an unsatisfiable constraint satisfaction subproblem. Next, we show that certain high-arity interactions, e.g. certain global constraints, can be included into the framework in a principled way. Finally, we prove that n-ary max-sum diffusion finds global optimum for n-ary supermodular problems.

关键词： Constraint optimization computer vision Cybernetics Partitioning algorithms Convergence Distributed computing Probability distribution Linear programming Inference algorithms Upper bound

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Optical Flow Estimation with Uncertainties through Dynamic MRFs

Optical Flow Estimation with Uncertainties through Dynamic M...

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

作者： Ben Glocker Nikos Paragios Nikos Komodakis Georgios Tziritas Nassir Navab Computer Aided Medical Procedures (CAMP) Technische Universität München Germany Mathématiques Appliquées aux Systémes (MAS) GALEN Group Paris France Computer Science Department University of Crete Greece

In this paper, we propose a novel dynamic discrete framework to address image morphing with application to optical flow estimation. We reformulate the problem using a number of discrete displacements, and therefore the estimation of the morphing parameters becomes a tractable matching criteria independent combinatorial problem which is solved through the FastPD algorithm. In order to overcome the main limitation of discrete approaches (low dimensionality of the label space is unable to capture the continuous nature of the expected solution), we introduce a dynamic behavior in the model where the plausible discrete deformations (displacements) are varying in space (across the domain) and time (different states of the process - successive morphing states) according to the local uncertainty of the obtained solution.

关键词： Image motion analysis Uncertainty Biomedical optical imaging Optical sensors Optical computing Biomedical imaging Deformable models Constraint optimization Covariance matrix computer science

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Where am I: Place instance and category recognition using spatial PACT

Where am I: Place instance and category recognition using sp...

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

作者： Jianixn Wu James M. Rehg Georgia Institute of Technology USA

We introduce spatial PACT (Principal component Analysis of Census Transform histograms), a new representation for recognizing instances and categories of places or scenes. Both place instance recognition ("I am in Room 113") and category recognition ("I am in an office") have been widely researched. Features that have different discriminative power/invariance tradeoff have been used separately for the two tasks. PACT captures local structures of an image through the Census Transform (CT), while large-scale structures are captured by the strong correlation between neighboring CT values and the histogram. the PCA operation ignores noise in the histogram distribution, computes important "primitive shapes", and results in a compact representation. Spatial PACT, a spatial pyramid of PACT, further incorporates global structures in the image. Our experiments demonstrate that spatial PACT outperforms the current state-of-the-art in several place and scene recognition, and shape matching datasets. Besides, spatial PACT is easy to implement. It has nearly no parameter to tune, and evaluates extremely fast.

关键词： Layout Principal component analysis Histograms Computed tomography Shape Robot localization Image recognition Sensor phenomena and characterization Robot vision systems Cameras

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HISTOGRAM-BASED SEARCH: A COMPARATIVE STUDY

HISTOGRAM-BASED SEARCH: A COMPARATIVE STUDY

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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

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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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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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Silhouette-Based Camera Calibration from Sparse Views under Circular Motion

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

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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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