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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331-340 订阅

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Detecting and Matching Repeated patterns for Automatic Geo-tagging in Urban Environments

Detecting and Matching Repeated Patterns for Automatic Geo-t...

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

作者： Grant Schindler Panchapagesan Krishnamurthy Roberto Lublinerman Yanxi Liu Frank Dellaert Georgia Institute of Technology USA Pennsylvania State University USA

We present a novel method for automatically geo-tagging photographs of man-made environments via detection and matching of repeated patterns. Highly repetitive environments introduce numerous correspondence ambiguities and are problematic for traditional wide-baseline matching methods. Our method exploits the highly repetitive nature of urban environments, detecting multiple perspectively distorted periodic 2D patterns in an image and matching them to a 3D database of textured facades by reasoning about the underlying canonical forms of each pattern. Multiple 2D-to-3D pattern correspondences enable robust recovery of camera orientation and location. We demonstrate the success of this method in a large urban environment.

关键词： pattern matching Image databases Layout Lattices computer vision Cities and towns Robustness Tagging Digital cameras Social network services

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Fuzzy Chamfer Distance and its Probabilistic Formulation for Visual Tracking

Fuzzy Chamfer Distance and its Probabilistic Formulation for...

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

作者： Yonggang Jin Farzin Mokhtarian Miroslaw Bober John Illingworth Centre for Vision Speech and Signal Processing University of Surrey UK

the paper presents a fuzzy chamfer distance and its probabilistic formulation for edge-based visual tracking. First, connections of the chamfer distance and the Hausdorff distance with fuzzy objective functions for clustering are shown using a reformulation theorem. A fuzzy chamfer distance (FCD) based on fuzzy objective functions and a probabilistic formulation of the fuzzy chamfer distance (PFCD) based on data association methods are then presented for tracking, which can all be regarded as reformulated fuzzy objective functions and minimized with iterative algorithms. Results on challenging sequences demonstrate the performance of the proposed tracking method.

关键词： Iterative algorithms High definition video Particle tracking Particle filters Signal processing algorithms Laboratories Speech processing Signal processing Object recognition Object detection

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Learning Human Motion Models from Unsegmented Videos

Learning Human Motion Models from Unsegmented Videos

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

作者： Roman Filipovych Eraldo Ribeiro Computer Vision and Bio-Inspired Computing Laboratory Department of Computer Sciences Florida Institute of Technology Melbourne FL USA

We present a novel method for learning human motion models from unsegmented videos. We propose a unified framework that encodes spatio-temporal relationships between descriptive motion parts and the appearance of individual poses. Sparse sets of spatial and spatio-temporal features are used. the method automatically learns static pose models and spatio-temporal motion parts. Neither motion cycles nor human figures need to be segmented for learning. We test the model on a publicly available action dataset and demonstrate that our new method performs well on a number of classification tasks. We also show that classification rates are improved by increasing the number of pose models in the framework.

关键词： Humans Videos computer vision Spatiotemporal phenomena Laboratories Testing Performance evaluation Image analysis Image sequence analysis Information analysis

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Real Time Object Tracking based on Dynamic Feature Grouping with Background Subtraction

Real Time Object Tracking based on Dynamic Feature Grouping ...

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

作者： ZuWhan Kim California PATH University of California Berkeley CA USA

Object detection and tracking has various application areas including intelligent transportation systems. We introduce an object detection and tracking approach that combines the background subtraction algorithm and the feature tracking and grouping algorithm. We first present an augmented background subtraction algorithm which uses a low-level feature tracking as a cue. the resulting background subtraction cues are used to improve the feature detection and grouping result. We then present a dynamic multi-level feature grouping approach that can be used in real time applications and also provides high-quality trajectories. Experimental results from video clips of a challenging transportation application are presented.

关键词： Vehicle detection Vehicles Object detection Robustness Lighting Trajectory Vehicle dynamics computer vision Application software Vehicle safety

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Correspondences between Parts of Shapes with Particle Filters

Correspondences between Parts of Shapes with Particle Filter...

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

作者： Rolf Lakaemper Marc Sobel Temple University Philadelphia USA

Given two shapes, the correspondence between distinct visual features is the basis for most alignment processes and shape similarity measures. this paper presents an approach introducing particle filters to establish perceptually correct correspondences between point sets representing shapes. Local shape feature descriptors are used to establish correspondence probabilities. the global correspondence structure is calculated using additional constraints based on domain knowledge. Domain knowledge is characterized as prior distributions expressing hypotheses about the global relationships between shapes. these hypotheses are generated during the iterative particle filtering process. Experiments using standard alignment techniques, based on the given correspondence relationships, demonstrate the advantages of this approach.

关键词： Particle filters Shape measurement Probability Labeling Filtering computer vision Terminology Particle measurements Humans Poisson equations

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Robust Learning of Discriminative Projection for Multicategory Classification on the Stiefel Manifold

Robust Learning of Discriminative Projection for Multicatego...

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

作者： Duc-Son Pham Svetha Venkatesh Department of Computing Curtin University of Technology Perth WA Australia

Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in pose, illumination, and facial expression. To address this problem, we propose a framework formulated under statistical learning theory that facilitates robust learning of a discriminative projection. Dimensionality reduction using the projection matrix is combined with a linear classifier in the regularized framework of lasso regression. the projection matrix in conjunction with the classifier parameters are then found by solving an optimization problem over the Stiefel manifold. the experimental results on standard face databases suggest that the proposed method outperforms some recent regularized techniques when the number of training samples is small.

关键词： Robustness Databases Principal component analysis Linear discriminant analysis Face recognition Nearest neighbor searches Statistical learning Australia Lighting pattern recognition

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Extracting Smooth and Transparent Layers from a Single Image

Extracting Smooth and Transparent Layers from a Single Image

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

作者： Sai-Kit Yeung Tai-Pang Wu Chi-Keung Tang Vision and Graphics Group Hong Kong University of Science and Technology Hong Kong China

Layer decomposition from a single image is an under-constrained problem, because there are more unknowns than equations. this paper studies a slightly easier but very useful alternative where only the background layer has substantial image gradients and structures. We propose to solve this useful alternative by an expectation-maximization (EM) algorithm that employs the hidden markov model (HMM), which maintains spatial coherency of smooth and overlapping layers, and helps to preserve image details of the textured background layer. We demonstrate that, using a small amount of user input, various seemingly unrelated problems in computational photography can be effectively addressed by solving this alternative using our EM-HMM algorithm.

关键词： Image decomposition Computational photography vision for graphics

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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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