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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321-330 订阅

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Joint Tracking of Features and Edges

Joint Tracking of Features and Edges

引用

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

作者： Stanley T. Birchfield Shrinivas J. Pundlik Electrical and Computer Engineering Department Clemson University Clemson SC USA

Sparse features have traditionally been tracked from frame to frame independently of one another. We propose a framework in which features are tracked jointly. Combining ideas from Lucas-Kanade and Horn-Schunck, the estimated motion of a feature is influenced by the estimated motion of neighboring features. the approach also handles the problem of tracking edges in a unified way by estimating motion perpendicular to the edge, using the motion of neighboring features to resolve the aperture problem. Results are shown on several image sequences to demonstrate the improved results obtained by the approach.

关键词： Tracking Motion estimation Apertures Optical variables control Image motion analysis computer vision US Department of Transportation Image sequences Focusing Fitting

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Recognising faces in unseen modes: a tensor based approach

Recognising faces in unseen modes: a tensor based approach

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Santu Rana Wanquan Liu Mihai Lazarescu Svetha Venkatesh Department of Computing Curtin University of Technology Perth WA Australia

this paper addresses the limitation of current multilinear techniques (multilinear PCA, multilinear ICA) when applied to face recognition for handling faces in unseen illumination and viewpoints. We propose a new recognition method, exploiting the interaction of all the subspaces resulting from multilinear decomposition (for both multilinear PCA and ICA), to produce a new basis called multilinear-eigenmodes. this basis offers the flexibility to handle face images at unseen illumination or viewpoints. Experiments on benchmarked datasets yield superior performance in terms of both accuracy and computational cost.

关键词： Face recognition Tensile stress Testing Independent component analysis Computational efficiency Principal component analysis Lighting Vectors Australia Biometrics

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Adaptive and Compact Shape Descriptor by Progressive Feature Combination and Selection with Boosting

Adaptive and Compact Shape Descriptor by Progressive Feature...

引用

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

作者： Cheng Chen Yueting Zhuang Jun Xiao Fei Wu Institution of Artificial Intelligence University of Zhejiang Hangzhou China

Many types of shape descriptors have been proposed for 2D shape analysis, but most of them consist of component features that are not adapted to specific problems. this has two drawbacks. First, computation is wasted on the irrelevant components;second, the accuracy is impaired. this paper proposes an effective method that generates compact descriptors adapted to specific problems in hand, where each component of the new descriptor is a linear combination of the components in some classic descriptors. A progressive strategy is used to construct and select the most suitable linear combinations in successive rounds, where a variant of Adaboost is employed to ensure the optimum of the selected combinations in each round. Experiments show that our method effectively generates adaptive and compact descriptors for typical applications such as shape classification and retrieval.

关键词： Shape Boosting Design for manufacture Application software Frequency Artificial intelligence pattern analysis computer vision pattern recognition Object recognition

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Recognizing Primitive Interactions by Exploring Actor-Object States

Recognizing Primitive Interactions by Exploring Actor-Object...

引用

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

In this paper, we present a solution to the novel problem of recognizing primitive actor-object interactions from videos. Here, we introduce the concept of actor-object states. Our method is based on the observation that at the moment of physical contact, both the motion and the appearance of actors are constrained by the target object. We propose a probabilistic framework that automatically learns models in such constrained states. We use joint probability distributions to represent both actor and object appearances as well as their intrinsic spatio-temporal configurations. Finally, we demonstrate the applicability of our approach on series of human-object interaction classification experiments.

关键词： computer vision Humans Graphical models Video sequences Laboratories Probability distribution Natural languages Context modeling Bayesian methods Object recognition

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Learning Coupled Conditional Random Field for Image Decomposition with Application on Object Categorization

Learning Coupled Conditional Random Field for Image Decompos...

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

作者： Xiaoxu Ma W. Eric L. Grimson Massachusetts Institute of Technology Cambridge MA USA

this paper proposes a computational system of object categorization based on decomposition and adaptive fusion of visual information. A coupled Conditional Random Field is developed to model the interaction between low level cues of contour and texture, and to decompose contour and texture in natural images. the advantages of using coupled rather than single-layer Random Fields are demonstrated with model learning and evaluation. Multiple decomposed visual cues are adaptively combined for object categorization to fully leverage different discriminative cues for different classes. Experimental results show that the proposed computational model of "recognition-through-decomposition-and-fusion" achieves better performance than most of the state-of-the-art methods, especially when only a limited number of training samples are available.

关键词： Image decomposition Object recognition Computational modeling Shape Portable computers Image recognition Humans Labeling Keyboards computer vision

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Branch-and-bound Hypothesis Selection for Two-view Multiple Structure and Motion Segmentation

Branch-and-bound Hypothesis Selection for Two-view Multiple ...

引用

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

作者： Ninad thakoor Jean Gao Electrical Engineering Department University of Technology Arlington USA Computer Science and Engineering Department University of Technology Arlington USA

An efficient and robust framework for two-view multiple structure and motion segmentation is proposed. To handle this otherwise recursive problem, hypotheses for the models are generated by local sampling. Once these hypotheses are available, a model selection problem is formulated which takes into account the hypotheses likelihoods and model complexity. An explicit model for outliers is also added for robust model selection. the model selection criterion is optimized through branch-and-bound technique of combinatorial optimization which guaranties optimality over current set of hypotheses by efficient search of solution space.

关键词： Motion segmentation computer vision Layout Sampling methods Cameras Cost function Robustness computer science Image segmentation Motion compensation

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Action recognition by Learning Mid-level Motion Features

Action Recognition by Learning Mid-level Motion Features

引用

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

作者： Alireza Fathi Greg Mori School of Computing Science Simon Fraser University Burnaby BC Canada

this paper presents a method for human action recognition based on patterns of motion. Previous approaches to action recognition use either local features describing small patches or large-scale features describing the entire human figure. We develop a method constructing mid-level motion features which are built from low-level optical flow information. these features are focused on local regions of the image sequence and are created using a variant of AdaBoost. these features are tuned to discriminate between different classes of action, and are efficient to compute at run-time. A battery of classifiers based on these mid-level features is created and used to classify input sequences. State-of-the-art results are presented on a variety of standard datasets.

关键词： Shape Humans Image motion analysis Image sequences pattern recognition Video sequences Object recognition Biomedical optical imaging Large-scale systems Runtime

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A NURBS-Based Spectral Reflectance Descriptor with Applications in computer vision and pattern recognition

A NURBS-Based Spectral Reflectance Descriptor with Applicati...

引用

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

作者： Cong Phuoc Huynh Antonio Robles-Kelly RSISE Australian National University Canberra ACT Australia NICTA Canberra ACT Australia

In this paper, we present a surface reflectance descriptor based on the control points resulting from the interpolation of Non-Uniform Rational B-Spline (NURBS) curves to multispectral reflectance data. the interpolation is based upon a knot removal scheme in the parameter domain. thus, we exploit the local support of NURBS so as to recover a compact descriptor robust to noise and local perturbation of the spectra. We demonstrate the utility of our NURBS-based descriptor for material identification. To this end, we perform skin spectra recognition making use of a Support Vector Machine classifier. We also provide results on hyperspectral imagery and elaborate on the preprocessing step for skin segmentation. We compare our results with those obtained using an alternative descriptor.

关键词： Reflectivity Application software computer vision pattern recognition Spline Interpolation Surface topography Surface reconstruction Skin Noise robustness

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Classification and evaluation of cost aggregation methods for stereo correspondence

Classification and evaluation of cost aggregation methods fo...

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

作者： Federico Tombari Stefano Mattoccia Luigi Di Stefano Elisa Addimanda Advanced Research Center on Electronic Systems (ARCES) University of Bologna Italy Universita di Bologna Bologna Emilia-Romagna IT

In the last decades several cost aggregation methods aimed at improving the robustness of stereo correspondence within local and global algorithms have been proposed. Given the recent developments and the lack of an appropriate comparison, in this paper we survey, classify and compare experimentally on a standard data set the main cost aggregation approaches proposed in literature. the experimental evaluation addresses both accuracy and computational requirements, so as to outline the best performing methods under these two criteria.

关键词： Cost function Computational efficiency Robustness Performance evaluation Computational complexity computer science Standards development Pixel Taxonomy Belief propagation

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Robust Estimation of Gaussian Mixtures from Noisy Input Data

Robust Estimation of Gaussian Mixtures from Noisy Input Data

引用

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

作者： Shaobo Hou Aphrodite Galata School of Computer Science University of Manchester Institute of Science and Technology UK

We propose a variational bayes approach to the problem of robust estimation of gaussian mixtures from noisy input data. the proposed algorithm explicitly takes into account the uncertainty associated with each data point, makes no assumptions about the structure of the covariance matrices and is able to automatically determine the number of the gaussian mixture components. through the use of both synthetic and real world data examples, we show that by incorporating uncertainty information into the clustering algorithm, we get better results at recovering the true distribution of the training data compared to other variational bayesian clustering algorithms.

关键词： Robustness Gaussian noise Clustering algorithms Uncertainty Bayesian methods Covariance matrix Maximum likelihood estimation Measurement errors Maximum likelihood detection Partitioning algorithms

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