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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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Edge preserving spatially varying mixtures for image segmentation

Edge preserving spatially varying mixtures for image segment...

引用

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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Graph Commute Times for Image Representation

Graph Commute Times for Image Representation

引用

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

作者： Regis Behmo Nikos Paragios Veronique Prinet Ecole Centrale Paris Malabry France NLPR/LIAMA Institute of Automation Chinese Academy and Sciences Beijing China

We introduce a new image representation that encompasses both the general layout of groups of quantized local invariant descriptors as well as their relative frequency. A graph of interest points clusters is constructed and we use the matrix of commute times between the different nodes of the graph to obtain a description of their relative arrangement that is robust to large intra class variation. the obtained high dimensional representation is then embedded in a space of lower dimension by exploiting the spectral properties of the graph made of the different images. Classification tasks can be performed in this embedding space. We expose classification and labelling results obtained on three different datasets, including the challenging PASCAL VOC2007 dataset. the performances of our approach compare favorably with the standard bag of features, which is a particular case of our representation.

关键词： Image representation Labeling Frequency Robustness computer vision Laplace equations Automation Shape Application software Graph theory

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Finding People in Archive Films through Tracking

Finding People in Archive Films through Tracking

引用

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

作者： Xiaofeng Ren Toyota Technological Institute Chicago IL USA

the goal of this work is to find all people in archive films. Challenges include low image quality, motion blur, partial occlusion, non-standard poses and crowded scenes. We base our approach on face detection and take a tracking/temporal approach to detection. Our tracker operates in two modes, following face detections whenever possible, switching to low-level tracking if face detection fails. With temporal correspondences established by tracking, we formulate detection as an inference problem in one-dimensional chains/tracks. We use a conditional random field model to integrate information across frames and to re-score tentative detections in tracks. Quantitative evaluations on full-length films show that the CRF-based temporal detector greatly improves face detection, increasing precision for about 30% (suppressing isolated false positives) and at the same time boosting recall for over 10% (recovering difficult cases where face detectors fail).

关键词： Face detection Detectors Image quality Layout Switches Boosting Visualization computer vision Clothing Lighting

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Optimizing Discrimination-Efficiency Tradeoff in Integrating Heterogeneous Local Features for Object Detection

Optimizing Discrimination-Efficiency Tradeoff in Integrating...

引用

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

作者： Bo Wu Ram Nevatia Institute for Robotics and Intelligent Systems University of Southern California Los Angeles CA USA

A large variety of image features has been invented for detection of objects of a known class. We propose a framework to optimize the discrimination-efficiency tradeoff in integrating multiple, heterogeneous features for object detection. Cascade structured detectors are learned by boosting local feature based weak classifiers. Each weak classifier corresponds to a local image region, from which several different types of features are extracted. the weak classifier makes predictions by examining the features one by one;this classifier goes to the next feature only when the prediction from the already examined features is not confident enough. the order in which the features are evaluated is determined based on their computational cost normalized classification powers. We apply our approach to two object classes, pedestrians and cars. the experimental results show that our approach outperforms the state-of-the-art methods.

关键词： computer vision Object detection Histograms Intelligent robots Detectors Boosting Face detection Support vector machines Support vector machine classification Kernel

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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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High-arity Interactions, Polyhedral Relaxations, and Cutting Plane Algorithm for Soft Constraint Optimisation (MAP-MRF)

High-arity Interactions, Polyhedral Relaxations, and Cutting...

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

作者： Tomas Werner Center for Machine Perception Department of Cybernetics Czech Technical University Praque Czech Republic

LP relaxation approach to soft constraint optimisation (i.e. MAP-MRF) has been mostly considered only for binary problems. We present its generalisation to n-ary problems, including a simple algorithm to optimise the LP bound, n-ary max-sum diffusion. As applications, we show that a hierarchy of gradually tighter polyhedral relaxations of MAP-MRF is obtained by adding zero interactions. We propose a cutting plane algorithm, where cuts correspond to adding zero interactions and the separation problem to finding an unsatisfiable constraint satisfaction subproblem. Next, we show that certain high-arity interactions, e.g. certain global constraints, can be included into the framework in a principled way. Finally, we prove that n-ary max-sum diffusion finds global optimum for n-ary supermodular problems.

关键词： Constraint optimization computer vision Cybernetics Partitioning algorithms Convergence Distributed computing Probability distribution Linear programming Inference algorithms Upper bound

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Background Subtraction in Highly Dynamic Scenes

Background Subtraction in Highly Dynamic Scenes

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

作者： Vijay Mahadevan Nuno Vasconcelos Department of Electrical and Computer Engineering University of California San Diego USA

A new algorithm is proposed for background subtraction in highly dynamic scenes. Background subtraction is equated to the dual problem of saliency detection: background points are those considered not salient by suitable comparison of object and background appearance and dynamics. Drawing inspiration from biological vision, saliency is defined locally, using center-surround computations that measure local feature contrast. A discriminant formulation is adopted, where the saliency of a location is the discriminant power of a set of features with respect to the binary classification problem which opposes center to surround. To account for both motion and appearance, and achieve robustness to highly dynamic backgrounds, these features are spatiotemporal patches, which are modeled as dynamic textures. the resulting background subtraction algorithm is fully unsupervised, requires no training stage to learn background parameters, and depends only on the relative disparity of motion between the center and surround regions. this makes it insensitive to camera motion. the algorithm is tested on challenging video sequences, and shown to outperform various state-of-the-art techniques for background subtraction.

关键词： Layout Cameras Robot vision systems computer vision Robustness Object recognition Object detection Engineering drawings Biology computing Spatiotemporal phenomena

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Optical Flow Estimation with Uncertainties through Dynamic MRFs

Optical Flow Estimation with Uncertainties through Dynamic M...

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

作者： Ben Glocker Nikos Paragios Nikos Komodakis Georgios Tziritas Nassir Navab Computer Aided Medical Procedures (CAMP) Technische Universität München Germany Mathématiques Appliquées aux Systémes (MAS) GALEN Group Paris France Computer Science Department University of Crete Greece

In this paper, we propose a novel dynamic discrete framework to address image morphing with application to optical flow estimation. We reformulate the problem using a number of discrete displacements, and therefore the estimation of the morphing parameters becomes a tractable matching criteria independent combinatorial problem which is solved through the FastPD algorithm. In order to overcome the main limitation of discrete approaches (low dimensionality of the label space is unable to capture the continuous nature of the expected solution), we introduce a dynamic behavior in the model where the plausible discrete deformations (displacements) are varying in space (across the domain) and time (different states of the process - successive morphing states) according to the local uncertainty of the obtained solution.

关键词： Image motion analysis Uncertainty Biomedical optical imaging Optical sensors Optical computing Biomedical imaging Deformable models Constraint optimization Covariance matrix computer science

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Object image retrieval by exploiting online knowledge resources

Object image retrieval by exploiting online knowledge resour...

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

作者： Gang Wang David Forsyth Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign USA Department of Computer Science University of Illinois Urbana-Champaign USA

We describe a method to retrieve images found on web pages with specified object class labels, using an analysis of text around the image and of image appearance. Our method determines whether an object is both described in text and appears in a image using a discriminative image model and a generative text model. Our models are learnt by exploiting established online knowledge resources (Wikipedia pages for text;Flickr and Caltech data sets for image). these resources provide rich text and object appearance information. We describe results on two data sets. the first is Berg's collection of ten animal categories;on this data set, we outperform previous approaches [7, 33]. We have also collected five more categories. Experimental results show the effectiveness of our approach on this new data set.

关键词： Image retrieval Web pages Wikipedia Animals Search engines computer science Image analysis Labeling Displays Bicycles

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Constrained Image Segmentation from Hierarchical Boundaries

Constrained Image Segmentation from Hierarchical Boundaries

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

作者： Pablo Arbelaez Laurent Cohen University of California Berkeley Berkeley CA USA CEREMADE UMR CNRS 7534 Université Paris Dauphine Paris France

In this paper, we address the problem of constrained segmentation of natural images, in which a human user places one seed point inside each object of interest in the image and the task is to determine the object boundaries. For this purpose, we study the connection between seed-based and hierarchical segmentation. We consider an Ultrametric Contour Map (UCM), the representation of a hierarchy of segmentations as a real-valued boundary image. Starting from a set of seed points, we propose an algorithm for constructing Voronoi tessellations with respect to a distance defined by the UCM. As a result, the main contribution of the paper is a method that allows exploiting the information of any hierarchical scheme for constrained segmentation. Our algorithm is parameter-free, computationally efficient and robust. We prove the interest of the approach proposed by evaluating quantitatively the results with respect to ground-truth data.

关键词： Image segmentation Humans Robustness computer vision Detectors Biomedical imaging Application software Surface morphology Surface topography Markov random fields

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