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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1341-1350 订阅

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Bayesian Color Constancy Revisited

Bayesian Color Constancy Revisited

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.11

作者： Peter Vincent Gehler Carsten Rother Andrew Blake Tom Minka Toby Sharp Max-Planck Institute Tubingen Germany Microsoft Research Cambridge Cambridge UK

Computational color constancy is the task of estimating the true reflectances of visible surfaces in an image. In this paper we follow a line of research that assumes uniform illumination of a scene, and that the principal step in estimating reflectances is the estimation of the scene illuminant. We review recent approaches to illuminant estimation, firstly those based on formulae for normalisation of the reflectance distribution in an image - so-called grey-world algorithms, and those based on a Bayesian formulation of image formation. In evaluating these previous approaches we introduce a new tool in the form of a database of 568 high-quality, indoor and outdoor images, accurately labelled with illuminant, and preserved in their raw form, free of correction or normalisation. this has enabled us to establish several properties experimentally. Firstly automatic selection of grey-world algorithms according to image properties is not nearly so effective as has been thought. Secondly, it is shown that Bayesian illuminant estimation is significantly improved by the improved accuracy of priors for illuminant and reflectance that are obtained from the new dataset.

关键词： Bayesian methods Layout Reflectivity Lighting Testing Cameras Image databases Object recognition Image resolution Yield estimation

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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...

引用

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...

引用

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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Using Contours to Detect and Localize Junctions in Natural Images

Using Contours to Detect and Localize Junctions in Natural I...

引用

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

作者： Michael Maire Pablo Arbelaez Charless Fowlkes Jitendra Malik University of California Berkeley CA USA University of California Irvine CA USA

Contours and junctions are important cues for perceptual organization and shape recognition. Detecting junctions locally has proved problematic because the image intensity surface is confusing in the neighborhood of a junction. Edge detectors also do not perform well near junctions. Current leading approaches to junction detection, such as the Harris operator, are based on 2D variation in the intensity signal. However, a drawback of this strategy is that it confuses textured regions with junctions. We believe that the right approach to junction detection should take advantage of the contours that are incident at a junction;contours themselves can be detected by processes that use more global approaches. In this paper, we develop a new high-performance contour detector using a combination of local and global cues. this contour detector provides the best performance to date (F=0.70) on the Berkeley Segmentation Dataset (BSDS) benchmark. From the resulting contours, we detect and localize candidate junctions, taking into account both contour salience and geometric configuration. We show that improvements in our contour model lead to better junctions. Our contour and junction detectors both provide state of the art performance.

关键词： Detectors Image edge detection Semiconductor counters computer vision Brightness Filters Shape Image recognition Humans Object detection

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Video Falsifying by Motion Interpolation and Inpainting

Video Falsifying by Motion Interpolation and Inpainting

引用

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

作者： Timothy K. Shih Nick C. Tan Joseph C. Tsai Hsing-Ying Zhong Department of Computer Science and Information Engineering Tamkang University Taiwan

We change the behavior of actors in a video. For instance, the outcome of a 100-meter race in the Olympic game can be falsified. We track objects and segment motions using a modified mean shift mechanism. the resulting video layers can be played in different speeds and at different reference points with respect to the original video. In order to obtain a smooth movement of target objects, a motion interpolation mechanism is proposed based on continuous stick figures (i.e., a video of human skeleton) and video inpainting. the video inpainting mechanism is performed in a quasi-3D space via guided 3D patch matching for filling. Interpolated target objects and background layers are fused by using graph cut. It is hard to tell whether a falsified video is the original. We demonstrate the original and the falsified videos in our website at ). the proposed technique can be used to create special effects in movie industry.

关键词： Motion interpolation Video inpainting Image completion Graph cut Mean shift Video special effect

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Global Pose Estimation Using Non-Tree Models

Global Pose Estimation Using Non-Tree Models

引用

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

作者： Hao Jiang David R. Martin Computer Science Department Boston College Graduate School Chestnut Hill MA USA

We propose a novel global pose estimation method to detect body parts of articulated objects in images based on non-tree graph models. there are two kinds of edges defined in the body part relation graph: Strong (tree) edges corresponding to the body plan that can enforce any type of constraint, and weak (non-tree) edges that express exclusion constraints arising from inter-part occlusion and symmetry conditions. We express optimal part localization as a multiple shortest path problem in a set of correlated trellises constructed from the graph model. Strong model edges generate the trellises, while weak model edges prohibit implausible poses by generating exclusion constraints among trellis nodes and edges. the optimization may be expressed as an integer linear program and solved using a novel two-stage relaxation scheme. Experiments show that the proposed method has a high chance of obtaining the globally optimal pose at low computational cost.

关键词： Tree graphs Optimization methods Assembly Image edge detection Shortest path problem Computational efficiency Leg Object detection Shape Face detection

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Estimating Age, Gender, and Identity using First Name Priors

Estimating Age, Gender, and Identity using First Name Priors

引用

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...

引用

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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Robust Motion Estimation and Structure Recovery from Endoscopic Image Sequences With an Adaptive Scale Kernel Consensus Estimator

Robust Motion Estimation and Structure Recovery from Endosco...

引用

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

作者： Hanzi Wang Daniel Mirota Masaru Ishii Gregory D. Hager Computer Science Department Johns Hopkins University Baltimore MD USA Department of Otolaryngology-Head and Neck Surgery Johns Hopkins Bayview Medical Center Baltimore MD USA

To correctly estimate the camera motion parameters and reconstruct the structure of the surrounding tissues from endoscopic image sequences, we need not only to deal with outliers (e.g., mismatches), which may involve more than 50% of the data, but also to accurately distinguish inliers (correct matches) from outliers. In this paper, we propose a new robust estimator, Adaptive Scale Kernel Consensus (ASKC), which can tolerate more than 50 percent outliers while automatically estimating the scale of inliers. With ASKC, we develop a reliable feature tracking algorithm. this, in turn, allows us to develop a complete system for estimating endoscopic camera motion and reconstructing anatomical structures from endoscopic image sequences. Preliminary experiments on endoscopic sinus imagery have achieved promising results.

关键词： Motion estimation Robustness Image sequences Kernel Surgery Cameras Anatomy Biomedical imaging Image reconstruction Statistics

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Image Decomposition into Structure and Texture Subcomponents with Multifrequency Modulation Constraints

Image Decomposition into Structure and Texture Subcomponents...

引用

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

作者： Georgios Evangelopoulos Petros Maragos School of Electrical and Computer Engineering National and Technical University of Athens Greece

Texture information in images is coupled with geometric macrostructures and piecewise-smooth intensity variations. Decomposing an image f into a geometric structure component u and a texture component v is an inverse estimation problem, essential for understanding and analyzing images depending on their content. In this paper, we present a novel combined approach for simultaneous texture from structure separation and multiband texture modeling. First, we formulate a new, variational decomposition scheme, involving an explicit texture reconstruction constraint (prior) formed by the responses of selected frequency-tuned linear filters. this forms a 'u + Kv' image model of K + 1 components. Subsequent texture modeling is applied to the estimated v component and its consistency is compared to using the complete, initial image f. the decomposition step, functioning as an advanced texture-front end, improves clustering and classification performance, for various multiband features. the proposed method can be generalized to other texture models or applications.

关键词： Image decomposition Image texture analysis Frequency Shape Image analysis Image reconstruction Nonlinear filters Solid modeling Image edge detection Image segmentation

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