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

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Smoothing-based Optimization

Smoothing-based Optimization

引用

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

作者： Marius Leordeanu Martial Hebert Carnegie Mellon University Pittsburgh PA USA

We propose an efficient method for complex optimization problems that often arise in computer vision. While our method is general and could be applied to various tasks, it was mainly inspired from problems in computer vision, and it borrows ideas from scale space theory. One of the main motivations for our approach is that searching for the global maximum through the scale space of a function is equivalent to looking for the maximum of the original function, with the advantage of having to handle fewer local optima. Our method works with any non-negative, possibly non-smooth function, and requires only the ability of evaluating the function at any specific point. the algorithm is based on a growth transformation, which is guaranteed to increase the value of the scale space function at every step, unlike gradient methods. To demonstrate its effectiveness we present its performance on a few computer vision applications, and show that in our experiments it is more effective than some well established methods such as MCMC, Simulated Annealing and the more local Nelder-Mead optimization method.

关键词： Smoothing methods Optimization methods computer vision Computational modeling Simulated annealing Kernel Gradient methods Application software computer simulation Monte Carlo methods

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Robust Null Space Representation and Sampling for View-Invariant Motion Trajectory Analysis

Robust Null Space Representation and Sampling for View-Invar...

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

作者： Xu Chen Dan Schonfeld Ashfaq Khokhar Department of Electrical and Computer Engineering University of Illinois Chicago USA

In this paper, we propose a novel robust retrieval and classification system for video and motion events based on null space representation. In order to analyze the robust-ness of the system, the perturbed null operators have been derived with the first order perturbation theory. Subsequently, the sensitivity of the null operators is discussed in terms of the error ratio and the SNR respectively. Meanwhile, the normwise bounds and componentwise bounds based on classical matrix perturbation theory are presented and discussed. Given the perturbation, uniform sampling are proposed for the convergence of the SNR and Poisson sampling are proposed for the convergence of the error ratio in the mean sense by choosing the rate parameter the same order as the number of samples. the simulation results are provided to demonstrate the effectiveness and robustness of our system in motion event indexing, retrieval and classification that is invariant to affine transformation due to camera motions.

关键词： Robustness Null space Sampling methods Motion analysis Cameras Convergence computer vision Signal to noise ratio Vectors Computational modeling

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Recovery of relative depth from a single observation using an uncalibrated (real-aperture) camera

Recovery of relative depth from a single observation using a...

引用

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

作者： Vinay P. Namboodiri Subhasis Chaudhuri Department of Electrical Engineering Indian Institute of Technology Bombay Mumbai India

In this paper we investigate the challenging problem of recovering the depth layers in a scene from a single defocused observation. the problem is definitely solvable if there are multiple observations. In this paper we show that one can perceive the depth in the scene even from a single observation. We use the inhomogeneous reverse heat equation to obtain an estimate of the blur, thereby preserving the depth information characterized by the defocus. However, the reverse heat equation, due to its parabolic nature, is divergent. We stabilize the reverse heat equation by considering the gradient degeneration as an effective stopping criterion. the amount of (inverse) diffusion is actually a measure of relative depth. Because of ill-posedness we propose a graph-cuts based method for inferring the depth in the scene using the amount of diffusion as a data likelihood and a smoothness condition on the depth in the scene. the method is verified experimentally on a varied set of test cases.

关键词： Cameras Layout Equations computer vision Humans Testing Focusing Shape Lenses Virtual private networks

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the Statistical Modelling of Fingerprint Minutiae Distribution with Implications for Fingerprint Individuality Studies

The Statistical Modelling of Fingerprint Minutiae Distributi...

引用

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

作者： Jiansheng Chen Yiu-Sang Moon Department of Computer Science and Engineering Chinese University of Hong Kong New Territories Hong Kong China

the spatial distribution of fingerprint minutiae is a core problem in the fingerprint individuality study, the cornerstone of the fingerprint authentication technology. Previously, the assumption in most research that minutiae distribution is random has been proved to be inaccurate and may lead to significant overestimates of fingerprint uniqueness. In this paper, we propose a stochastic model for describing and simulating fingerprint minutiae patterns. through coupling a pair potential Markov point process with a thinned process, this model successfully depicts the complex statistical behavior of fingerprint minutiae. Parameters of this model can be determined by nonlinear minimization. Furthermore, experiment results show that the statistical properties of our proposed model dovetails nicely with real minutiae data in terms of the false fingerprint correspondence probability. Such evidences indicate that the proposed model is a more accurate foundation for minutiae based fingerprint individuality studies as well as the artificial fingerprint synthesis when compared to the model of random distribution.

关键词： Fingerprint recognition Authentication Stochastic processes Moon computer science Probability Biometrics Humans Stability analysis Stress

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Matching Non-rigidly Deformable Shapes Across Images: A Globally Optimal Solution

Matching Non-rigidly Deformable Shapes Across Images: A Glob...

引用

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

作者： thomas Schoenemann Daniel Cremers Department of Computer Science University of Bonn Germany

While global methods for matching shapes to images have recently been proposed, so far research has focused on small deformations of a fixed template. In this paper we present the first global method able to pixel-accurately match non-rigidly deformable shapes across images at amenable run-times. By finding cycles of optimal ratio in a four-dimensional graph - spanned by the image, the prior shape and a set of rotation angles - we simultaneously compute a segmentation of the image plane, a matching of points on the template to points on the segmenting boundary, and a decomposition of the template into a set of deformable parts. In particular, the interpretation of the shape template as a collection of an a priori unknown number of deformable parts - an important aspect of higher-level shape representations - emerges as a byproduct of our matching algorithm. On real-world data of running people and walking animals, we demonstrate that the proposed method can match strongly deformed shapes, even in cases where simple shape measures and optic flow methods fail.

关键词： Image segmentation Shape measurement Partitioning algorithms Pixel Animals computer vision Legged locomotion computer Society pattern recognition computer science

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Discriminative Local Binary patterns for Human Detection in Personal Album

Discriminative Local Binary Patterns for Human Detection in ...

引用

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

作者： Yadong Mu Shuicheng Yan Yi Liu thomas Huang Bingfeng Zhou Peking University China National University of Singapore Singapore University of Illinois Urbana-Champaign USA

In recent years, local pattern based object detection and recognition have attracted increasing interest in computer vision research community. However, to our best knowledge no previous work has focused on utilizing local patterns for the task of human detection. In this paper we develop a novel human detection system in personal albums based on LBP (local binary pattern) descriptor. Firstly we review the existing gradient based local features widely used in human detection, analyze their limitations and argue that LBP is more discriminative. Secondly, original LBP descriptor does not suit the human detecting problem well due to its high complexity and lack of semantic consistency, thus we propose two variants of LBP: Semantic-LBP and Fourier-LBP. Carefully designed experiments demonstrate the superiority of LBP over other traditional features for human detection. Especially we adopt a random ensemble algorithm for better comparison between different descriptors. All experiments are conducted on INRIA human database.

关键词： Humans computer vision Face detection Object detection pattern recognition Support vector machines Spatial databases Acceleration Tensile stress Boosting

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Learning for Stereo vision Using the Structured Support Vector Machine

Learning for Stereo Vision Using the Structured Support Vect...

引用

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

作者： Yunpeng Li Daniel P. Huttenlocher Department of Computer Science Cornell University Ithaca NY USA

We present a random field based model for stereo vision with explicit occlusion labeling in a probabilistic framework. the model employs non-parametric cost functions that can be learnt automatically using the structured support vector machine. the learning algorithm enables the training of models that are steered towards optimizing for a particular desired loss function, such as the metric used to evaluate the quality of the stereo labeling. Experimental results demonstrate that the performance of our method surpasses that of previous learning approaches and is comparable to the state-of-the-art for pixel-based stereo. Moreover, our method achieves good results even when trained on different image sets, in contrast with the common practice of hand tuning to specific benchmark images. In addition, we investigate the impact of graph structure on model performance. Our study shows that random field models with longer-range edges generally outperform the 4-connected grid and that this advantage is especially pronounced for noisy images.

关键词： Machine learning Stereo vision Support vector machines Cost function Labeling Maximum likelihood estimation Kernel Character generation computer science computer vision

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3-D Tracking of Shoes for Virtual Mirror Applications

3-D Tracking of Shoes for Virtual Mirror Applications

引用

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

作者： P. Eisert P. Fechteler J. Rurainsky Fraunhofer Institute for Telecommunications Heinrich-Hertz-Institute Berlin Germany

In this paper, augmented reality techniques are used in order to create a Virtual Mirror for the real-time visualization of customized sports shoes. Similar to looking into a mirror when trying on new shoes in a shop, we create the same impression but for virtual shoes that the customer can design individually. For that purpose, we replace the real mirror by a large display that shows the mirrored input of a camera capturing the legs and shoes of a person. 3-D Tracking of both feet and exchanging the real shoes by computer graphics models gives the impression of actually wearing the virtual shoes. the 3-D motion tracker presented in this paper, exploits mainly silhouette information to achieve robust estimates for both shoes from a single camera view. the use of a hierarchical approach in an image pyramid enables real-time estimation at frame rates of more than 30 frames per second.

关键词： Footwear Mirrors Cameras Augmented reality Visualization computer displays Leg computer graphics Tracking Robustness

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Minimal Local Reconstruction Error Measure Based Discriminant Feature Extraction and Classification

Minimal Local Reconstruction Error Measure Based Discriminan...

引用

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

作者： Jian Yang Zhen Lou Zhong Jin Jing-yu Yang School of Computer Science and Technology Nanjing University of Science and Technology of PLA Nanjing China

this paper introduces the minimal local reconstruction error (MLRE) as a similarity measure and presents a MLRE-based classifier. From the geometric meaning of the minimal local reconstruction error, we derive that the MLRE-based classifier is a generalization of the conventional nearest neighbor classifier and the nearest neighbor line and plane classifiers. We further apply the MLRE measure to characterize the within-class and between-class local scatters and then develop a MLRE measure based discriminant feature extraction method. the proposed MLRE-based feature extraction method is in line with the MLRE-based classification method in spirit, thus the two methods can be seamlessly combined in applications. the experimental results on the CENPARMI handwritten numeral database and the FERET face image database show effectiveness of the proposed MLRE-based feature extraction and classification method.

关键词： Feature extraction Nearest neighbor searches Prototypes Scattering Linear discriminant analysis Image reconstruction computer errors Neural networks Image databases pattern classification

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On the use of Independent Tasks for Face recognition

On the use of Independent Tasks for Face Recognition

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

作者： Agata Lapedriza David Masip Jordi Vitria Computer Vision Center (CVC) Universitat Autònoma Barcelona Barcelona Spain Universitat Oberta de Catalunya Barcelona Spain Computer Vision Center (CVC) Universidad de Barcelona Barcelona Spain

We present a method for learning discriminative linear feature extraction using independent tasks. More concretely, given a target classification task, we consider a complementary classification task that is independent of the target one. For example, in face classification field, subject recognition can be a target task while facial expression classification can be a complementary task. then, we use labels of the complementary task in order to obtain a more robust feature extraction, being the new feature space less sensitive to the complementary classification. To learn the proposed feature extraction we use the mutual information measure between the projected data and both labels from the target and the complementary tasks. In our experiments, this framework has been applied to a face recognition problem, in order to inhibit this classification task from environmental artifacts, and to mitigate the effects of the small sample size problem. Our classification experiments show an improved feature extraction process using the proposed method.

关键词： Face recognition Feature extraction Robustness Training data computer vision Lighting Data mining Target recognition Mutual information Classification algorithms

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