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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431-440 订阅

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High Resolution Motion Layer Decomposition using Dual-space Graph Cuts

High Resolution Motion Layer Decomposition using Dual-space ...

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

We introduce a novel energy minimization method to decompose a video into a set of super-resolved moving layers. the proposed energy corresponds to the cost of coding the sequence. It consists of a data term and two terms imposing regularity of the geometry and the intensity of each layer. In contrast to existing motion layer methods, we perform graph cut optimization in the (dual) layer space to determine which layer is visible at which video position. In particular, we show how arising higher-order terms can be accounted for by a generalization of alpha expansions. Moreover, our model accurately captures long-term temporal consistency. To the best of our knowledge, this is the first work which aims at modeling details of the image formation process (such as camera blur and downsampling) in the context of motion layer decomposition. the experimental results demonstrate that energy minimization leads to a reconstruction of a video in terms of a superposition of multiple high-resolution motion layers.

关键词： Energy resolution Spatial resolution Costs Constraint optimization computer vision Geometry Image reconstruction Motion estimation Minimization methods Video compression

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Moving Shape Dynamics: A Signal Processing Perspective

Moving Shape Dynamics: A Signal Processing Perspective

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

作者： Liang Wang Xin Geng Christopher Leckie Ramamohanarao Kotagiri Department of Computer Science & Software Engineering University of Melbourne VIC Australia School of Engineering & Information Technology Deakin University VIC Australia

this paper provides a new perspective on human motion analysis, namely regarding human motions in video as general discrete time signals. While this seems an intuitive idea, research on human motion analysis has attracted little attention from the signal processing community. Sophisticated signal processing techniques create important opportunities for new solutions to the problem of human motion analysis. this paper investigates how the deformations of human silhouettes (or shapes) during articulated motion can be used as discriminating features to implicitly capture motion dynamics. In particular, we demonstrate the applicability of two widely used signal transform methods, namely the Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT), for characterization and recognition of human motion sequences. Experimental results show the effectiveness of the proposed method on two state-of-the-art data sets.

关键词： Shape Signal processing Humans Discrete Fourier transforms Discrete wavelet transforms Motion analysis Fourier transforms Character recognition Hidden Markov models Video signal processing

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

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

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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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A Scalable Graph-Cut Algorithm for N-D Grids

A Scalable Graph-Cut Algorithm for N-D Grids

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

作者： Andrew Delong Yuri Boykov University of Western Ontario UK

Global optimisation via s-t graph cuts is widely used in computer vision and graphics. To obtain high-resolution output, graph cut methods must construct massive N-D grid-graphs containing billions of vertices. We show that when these graphs do not fit into physical memory, current max-flow/min-cut algorithms - the workhorse of graph cut methods - are totally impractical. Others have resorted to banded or hierarchical approximation methods that get trapped in local minima, which loses the main benefit of global optimisation. We enhance the push-relabel algorithm for maximum flow [14] with two practical contributions. First, true global minima can now be computed on immense grid-like graphs too large for physical memory. these graphs are ubiquitous in computer vision, medical imaging and graphics. Second, for commodity multi-core platforms our algorithm attains near-linear speedup with respect to number of processors. To achieve these goals, we generalised the standard relabeling operations associated with push-relabel.

关键词： Optimization methods Image segmentation computer vision Image reconstruction computer graphics Biomedical imaging Approximation methods Grid computing Pervasive computing Physics computing

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Decomposition, Discovery and Detection of Visual Categories Using Topic Models

Decomposition, Discovery and Detection of Visual Categories ...

引用

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

作者： Mario Fritz Bernt Schiele Comuter Science Department Technical University of Darmstadt Germany

We present a novel method for the discovery and detection of visual object categories based on decompositions using topic models. the approach is capable of learning a compact and low dimensional representation for multiple visual categories from multiple view points without labeling of the training instances. the learnt object components range from local structures over line segments to global silhouette-like descriptions. this representation can be used to discover object categories in a totally unsupervised fashion. Furthermore we employ the representation as the basis for building a supervised multi-category detection system making efficient use of training examples and outperforming pure features-based representations. the proposed speed-ups make the system scale to large databases. Experiments on three databases show that the approach improves the state-of-the-art in unsupervised learning as well as supervised detection. In particular we improve the state-of-the-art on the challenging PASCAL'06 multi-class detection tasks for several categories.

关键词： Object detection Spatial databases Unsupervised learning Histograms Supervised learning Labeling Buildings Shape Statistics computer vision

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Minimal Local Reconstruction Error Measure Based Discriminant Feature Extraction and Classification

Minimal Local Reconstruction Error Measure Based Discriminan...

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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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3-D Tracking of Shoes for Virtual Mirror Applications

3-D Tracking of Shoes for Virtual Mirror Applications

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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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Edge preserving spatially varying mixtures for image segmentation

Edge preserving spatially varying mixtures for image segment...

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

作者： Giorgos Sfikas Christophoros Nikou Nikolaos Galatsanos Department of Computer Science University of Ioannina (UoI) Ioannina Greece Department of Electrical and Computer Engineering University of Patras Rio Greece

A new hierarchical Bayesian model is proposed for image segmentation based on Gaussian mixture models (GMM) with a prior enforcing spatial smoothness. According to this prior, the local differences of the contextual mixing proportions (i.e. the probabilities of class labels) are Student's t-distributed. the generative properties of the Student's t-pdf allow this prior to impose smoothness and simultaneously model the edges between the segments of the image. A maximum a posteriori (MAP) expectation-maximization (EM) based algorithm is used for Bayesian inference. An important feature of this algorithm is that all the parameters are automatically estimated from the data in closed form. Numerical experiments are presented that demonstrate the superiority of the proposed model for image segmentation as compared to standard GMM-based approaches and to GMM segmentation techniques with "standard" spatial smoothness constraints.

关键词： Image segmentation Inference algorithms Bayesian methods Clustering algorithms computer science Parameter estimation computer science education Educational programs Maximum likelihood estimation Context modeling

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Fast and Robust Numerical Solutions to Minimal Problems for Cameras with Radial Distortion

Fast and Robust Numerical Solutions to Minimal Problems for ...

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

作者： Martin Byrod Zuzana Kukelova Klas Josephson Tomas Pajdla Kalle Astrom Lund University Lund Sweden Czech Technical University Prague Czech Republic

A number of minimal problems of structure from motion for cameras with radial distortion have recently been studied and solved in some cases. these problems are known to be numerically very challenging and in several cases there exist no known practical algorithm yielding solutions in floating point arithmetic. We make some crucial observations concerning the floating point implementation of Grobner basis computations and use these new insights to formulate fast and stable algorithms for two minimal problems with radial distortion previously solved in exact rational arithmetic only: (i) simultaneous estimation of essential matrix and a common radial distortion parameter for two partially calibrated views and six image point correspondences and (ii) estimation of fundamental matrix and two different radial distortion parameters for two uncalibrated views and nine image point correspondences. We demonstrate on simulated and real experiments that these two problems can be efficiently solved in floating point arithmetic.

关键词： Robustness Cameras Floating-point arithmetic Polynomials Equations Calibration computer vision Application software Computational modeling Motion estimation

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