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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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FuzzyMatte: A Computationally Efficient Scheme for Interactive Matting

FuzzyMatte: A Computationally Efficient Scheme for Interacti...

引用

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

作者： Yuanjie Zheng Chandra Kambhamettu Jingyi Yu thomas Bauer Karl Steiner Department of Computer Science University of Delaware Newark DE USA Christiana Care Health Services Helen F. Graham Cancer Center Newark DE USA Delaware Biotechnology Institute University of Delaware Newark DE USA

In this paper, we propose an online interactive matting algorithm, which we call FuzzyMatte. Our framework is based on computing the fuzzy connectedness (FC) [20] from each unknown pixel to the known foreground and background. FC effectively captures the adjacency and similarity between image elements and can be efficiently computed using the strongest connected path searching algorithm. the final alpha value at each pixel can then be calculated from its FC. While many previous methods need to completely recompute the matte when new inputs are provided, FuzzyMatte effectively integrates these new inputs with the previously estimated matte by efficiently recomputing the FC value for a small subset of pixels. thus, the computational overhead between each iteration of the refinement is significantly reduced. We demonstrate FuzzyMatte on a wide range of images. We show that FuzzyMatte updates the matte in an online interactive setting and generates high quality matte for complex images.

关键词： computer science Cancer Biotechnology Image resolution Biomedical imaging Image segmentation Pixel Computational efficiency

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Variable Baseline/Resolution Stereo

Variable Baseline/Resolution Stereo

引用

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

作者： David Gallup Jan-Michael Frahm Philippos Mordohai Marc Pollefeys North Carolina State University Chapel Hill NC USA University of Pennsylvania Philadelphia PA USA ETH Zurich Zurich Switzerland

We present a novel multi-baseline, multi-resolution stereo method, which varies the baseline and resolution proportionally to depth to obtain a reconstruction in which the depth error is constant. this is in contrast to traditional stereo, in which the error grows quadratically with depth, which means that the accuracy in the near range far exceeds that of the far range. this accuracy in the near range is unnecessarily high and comes at significant computational cost. It is, however, non-trivial to reduce this without also reducing the accuracy in the far range. Many datasets, such as video captured from a moving camera, allow the baseline to be selected with significant flexibility. By selecting an appropriate baseline and resolution (realized using an image pyramid), our algorithm computes a depthmap which has these properties: 1) the depth accuracy is constant over the reconstructed volume, 2) the computational effort is spread evenly over the volume, 3) the angle of triangulation is held constant w.r.t. depth. Our approach achieves a given target accuracy with minimal computational effort, and is orders of magnitude faster than traditional stereo.

关键词： Stereo vision Image reconstruction Stereo image processing Cameras Cities and towns Computational efficiency Pixel Earth Layout Image resolution

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Large Margin Pursuit for a Conic Section Classifier

Large Margin Pursuit for a Conic Section Classifier

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

作者： Santhosh Kodipaka Arunava Banerjee Baba C. Vemuri Department of Computer & Information Science && Engineering University of Florida Gainesville FL USA

Learning a discriminant becomes substantially more difficult when the datasets are high-dimensional and the available samples are few. this is often the case in computer vision and medical diagnosis applications. A novel Conic Section classifier (CSC) was recently introduced in the literature to handle such datasets, wherein each class was represented by a conic section parameterized by its focus, directrix and eccentricity. the discriminant boundary was the locus of all points that are equi-eccentric relative to each class-representative conic section. Simpler boundaries were preferred for the sake of generalizability. In this paper, we improve the performance of the two-class classifier via a large margin pursuit. When formulated as a non-linear optimization problem, the margin computation is demonstrated to be hard, especially due to the high dimensionality of the data. Instead, we present a geometric algorithm to compute the distance of a point to the nonlinear discriminant boundary generated by the CSC in the input space. We then introduce a large margin pursuit in the learning phase so as to enhance the generalization capacity of the classifier. We validate the algorithm on real datasets and show favorable classification rates in comparison to many existing state-of-the-art binary classifiers as well as the CSC without margin pursuit.

关键词： Virtual colonoscopy Cancer computer vision Application software Kernel Supervised learning Information science Biomedical engineering Data engineering Medical diagnosis

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What Are the High-Level Concepts with Small Semantic Gaps?

What Are the High-Level Concepts with Small Semantic Gaps?

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

作者： Yijuan Lu Lei Zhang Qi Tian Wei-Ying Ma One UTSA Circle University of Texas San Antonio San Antonio TX USA Microsoft Research Asia Beijing China

Concept-based multimedia search has become more and more popular in Multimedia Information Retrieval (MIR). However, which semantic concepts should be used for data collection and model construction is still an open question. Currently, there is very little research found on automatically choosing multimedia concepts with small semantic gaps. In this paper, we propose a novel framework to develop a lexicon of high-level concepts with small semantic gaps (LCSS) from a large-scale web image dataset. By defining a confidence map and content-context similarity matrix, images with small semantic gaps are selected and clustered. the final concept lexicon is mined from the surrounding descriptions (titles, categories and comments) of these images. this lexicon offers a set of high-level concepts with small semantic gaps, which is very helpful for people to focus for data collection, annotation and modeling. It also shows a promising application potential for image annotation refinement and rejection. the experimental results demonstrate the validity of the developed concepts lexicon.

关键词： Information retrieval Europe Asia Large-scale systems Focusing Multimedia systems Power system modeling Object recognition Training data Vocabulary

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Visibility in Bad Weather from a Single Image

Visibility in Bad Weather from a Single Image

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

作者： Robby T. Tan Imperial College London Communications and Signal Processing Group NICTA-Australian National University Australia

Bad weather, such as fog and haze, can significantly degrade the visibility of a scene. Optically, this is due to the substantial presence of particles in the atmosphere that absorb and scatter light. In computer vision, the absorption and scattering processes are commonly modeled by a linear combination of the direct attenuation and the airlight. Based on this model, a few methods have been proposed, and most of them require multiple input images of a scene, which have either different degrees of polarization or different atmospheric conditions. this requirement is the main drawback of these methods, since in many situations, it is difficult to be fulfilled. To resolve the problem, we introduce an automated method that only requires a single input image. this method is based on two basic observations: first, images with enhanced visibility (or clear-day images) have more contrast than images plagued by bad weather;second, airlight whose variation mainly depends on the distance of objects to the viewer, tends to be smooth. Relying on these two observations, we develop a cost function in the framework of Markov random fields, which can be efficiently optimized by various techniques, such as graph-cuts or belief propagation. the method does not require the geometrical information of the input image, and is applicable for both color and gray images.

关键词： Optical scattering Layout Optical attenuators Light scattering Particle scattering Atmospheric modeling Degradation Atmosphere computer vision Absorption

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A Region Based Stereo Matching Algorithm Using Cooperative Optimization

A Region Based Stereo Matching Algorithm Using Cooperative O...

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

作者： Zeng-Fu Wang Zhi-Gang Zheng University of Science and Technology Hefei Anhui China

this paper presents a new stereo matching algorithm based on inter-regional cooperative optimization. the proposed algorithm uses regions as matching primitives and defines the corresponding region energy functional for matching by utilizing the color Statistics of regions and the constraints on smoothness and occlusion between adjacent regions. In order to obtain a more reasonable disparity map, a cooperative optimization procedure has been employed to minimize the matching costs of all regions by introducing the cooperative and competitive mechanism between regions. Firstly, a color based segmentation method is used to segment the reference image into regions with homogeneous color. Secondly, a local window-based matching method is used to determine the initial disparity estimate of each image pixel. And then, a voting based plane fitting technique is applied to obtain the parameters of disparity plane corresponding to each image region. Finally, the disparity plane parameters of all regions are iteratively optimized by an inter-regional cooperative optimization procedure until a reasonable disparity map is obtained the experimental Results on Middlebury test set and real stereo images indicate that the performance of our method is competitive with the best stereo matching algorithms and the disparity maps recovered are close to the ground truth data.

关键词： Stereo vision Image segmentation Clustering algorithms Statistics Cost function Layout Labeling Pixel Voting Testing

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Sensor Planning for Automated and Persistent Object Tracking with Multiple Cameras

Sensor Planning for Automated and Persistent Object Tracking...

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

作者： Yi Yao Chung-Hao Chen Besma Abidi David Page Andreas Koschan Mongi Abidi Imaging Robotics and Intelligent Systems (IRIS) Laboratory University of Tennessee Knoxville USA

Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). However, visibility, a fundamental requirement of object tracking, is insufficient for persistent and automated tracking. In such applications, a continuous and consistently labeled trajectory of the same object should be maintained across different cameras' views. therefore, a sufficient overlap between the cameras' FOVs should be secured so that camera handoff can be executed successfully and automatically before the object of interest becomes untraceable or unidentifiable. the proposed sensor planning method improves existing algorithms by adding handoff rate analysis, which preserves necessary overlapped FOVs for an optimal handoff success rate. In addition, special considerations such as resolution and frontal view requirements are addressed using two approaches: direct constraint and adaptive weight. the resulting camera placement is compared with a reference algorithm by Erdem and Sclaroff. Significantly improved handoff success rate and frontal view percentage are illustrated via experiments using typical office floor plans.

关键词： Surveillance Sensor systems Algorithm design and analysis Safety Intelligent sensors Robot vision systems Smart cameras Intelligent robots Image sensors Optical imaging

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Learning Subcategory Relevances for Category recognition

Learning Subcategory Relevances for Category Recognition

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

作者： Sinisa Todorovic Narendra Ahuja Beckman Institute University of Illinois Urbana-Champaign USA

A real-world object category can be viewed as a characteristic configuration of its parts, that are themselves simpler, smaller (sub)categories. recognition of a category can therefore be made easier by detecting its constituent subcategories and combing these detection results. Given a set of training images, each labeled by an object category contained in it, we present an approach to learning: (1) Taxonomy defined by recursive sharing of subcategories by multiple image categories;(2) Subcategory relevance as the degree of evidence a subcategory offers for the presence of its parent;(3) Likelihood that the image contains a subcategory;and (4) Prior that a subcategory occurs. the images are represented as points in a feature space spanned by confidences in the occurrences of the subcategories. the subcategory relevances are estimated as weights, necessary to rescale the corresponding axes of the feature space so that the images with the same label are closer to each other than to those with different labels. When a new image is encountered, the learned taxonomy, relevances, likelihoods, and priors are used by a linear classifier to categorize the image. On the challenging Caltech-256 dataset, the proposed approach significantly outperforms the best categorizations reported. this result is significant in that it not only demonstrates the advantages of exploiting subcategory taxonomy for recognition, but also suggests that a feature space spanned by part properties, instead of direct object properties, allows for linear separation of image classes.

关键词： Taxonomy Image recognition Maximum likelihood detection Character recognition Diversity reception Lighting Cameras Object detection Object recognition Wheels

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Approximate earth mover's distance in linear time

Approximate earth mover's distance in linear time

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

作者： Sameer Shirdhonkar David W. Jacobs Center for Automation Research University of Maryland College Park USA

the earth mover's distance (EMD) [16] is an important perceptually meaningful metric for comparing histograms, but it suffers from high (O(N{sup}3 log N)) computational complexity. We present a novel linear time algorithm for approximating the EMD for low dimensional histograms using the sum of absolute values of the weighted wavelet coefficients of the difference histogram. EMD computation is a special case of the Kantorovich-Rubinstein transshipment problem, and we exploit the Holder continuity constraint in its dual form to convert it into a simple optimization problem with an explicit solution in the wavelet domain. We prove that the resulting wavelet EMD metric is equivalent to EMD, i.e. the ratio of the two is bounded. We also provide estimates for the bounds. the weighted wavelet transform can be computed in time linear in the number of histogram bins, while the comparison is about as fast as for normal Euclidean distance or χ{sup}2 statistic. We experimentally show that wavelet EMD is a good approximation to EMD, has similar performance, but requires much less computation.

关键词： Earth Histograms Computational complexity Wavelet coefficients Wavelet domain Statistics Extraterrestrial measurements Image retrieval pattern matching Jacobian matrices

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Graph-Shifts: Natural Image Labeling by Dynamic Hierarchical Computing

Graph-Shifts: Natural Image Labeling by Dynamic Hierarchical...

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

作者： Jason J. Corso Alan Yuille Zhuowen Tu Computer Science and Engineering SUNY Buffalo USA Statistics UCLA USA Neurology UCLA USA

In this paper, we present a new approach for image labeling based on the recently introduced graph-shifts algorithm. Graph-shifts is an energy minimization algorithm that does labeling by dynamically manipulating, or shifting, the parent-child relationships in a hierarchical decomposition of the image. Each shift optimally reduces the energy by indirectly causing a change to the labeling;graph-shifts is able to rapidly compute and select this optimal shift at every iteration. there are no constraints on the terms of the (pairwise) energy function. the algorithm was originally presented in the context of medical image labeling using conditional random field models. In this paper, we consider the algorithm in the context of both low- and high-level natural image labeling. We show that for examples in both classes of problems, graph-shifts does labeling both accurately and rapidly. For low-level vision, we explore image restoration, and for high-level vision, we make use of a hybrid discriminative-generative model to segment and label images into semantically meaningful regions (e.g., trees, buildings, etc.). For both problems, we obtain comparable or superior results to the state-of-the-art computed in just a few seconds per image.

关键词： Labeling Minimization methods Biomedical imaging Image segmentation Tree graphs Belief propagation computer science Power engineering and energy Statistics Nervous system

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