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检索条件"任意字段=2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005"

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Kernel-based Bayesian filtering for object tracking

Kernel-based Bayesian filtering for object tracking

引用

conference on computer vision and pattern recognition (cvpr)

作者： Bohyung Han Ying Zhu D. Comaniciu L. Davis Department of Computer Science University of Maryland College Park MD USA Real-Time Vision and Modeling Siemens Corporate Research Inc. Princeton NJ USA Integrated Data and Systems Siemens Corporate Research Inc. Princeton NJ USA

Particle filtering provides a general framework for propagating probability density functions in nonlinear and non-Gaussian systems. However, the algorithm is based on a Monte Carlo approach and sampling is a problematic issue, especially for high dimensional problems. This paper presents a new kernel-based Bayesian filtering framework, which adopts an analytic approach to better approximate and propagate density functions. In this framework, the techniques of density interpolation and density approximation are introduced to represent the likelihood and the posterior densities by Gaussian mixtures, where all parameters such as the number of mixands, their weight, mean, and covariance are automatically determined. The proposed analytic approach is shown to perform sampling more efficiently in high dimensional space. We apply our algorithm to real-time tracking problems, and demonstrate its performance on real video sequences as well as synthetic examples.

关键词： Bayesian methods Density functional theory Sampling methods Interpolation Monte Carlo methods Filtering algorithms Kernel computer vision Density measurement computer science

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A Bayesian approach for shadow extraction from a single image

A Bayesian approach for shadow extraction from a single imag...

引用

Proceedings - 10th ieee International conference on computer vision, ICCV 2005

作者： Wu, Tai-Pang Tang, Chi-Keung Vision and Graphics Group Hong Kong University of Science and Technology Clear Water Bay Hong Kong Hong Kong

ISBN: (纸本)076952334X

This paper addresses the problem of shadow extraction from a single image of a complex natural scene. No simplifying assumption on the camera and the light source other than the Lambertian assumption is used. Our method is unique because it is capable of translating very rough user-supplied hints into the effective likelihood and prior functions for our Bayesian optimization. The likelihood function requires a decent estimation of the shadowless image, which is obtained by solving the associated Poisson equation. Our Bayesian framework allows for the optimal extraction of smooth shadows while preserving texture appearance under the extracted shadow. Thus our technique can be applied to shadow removal, producing some best results to date compared with the current state-of-the-art techniques using a single input image. We propose related applications in shadow compositing and image repair using our Bayesian technique. © 2005 ieee.

关键词： pattern recognition

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Creating invariance to "nuisance parameters" in face recognition

Creating invariance to "nuisance parameters" in face recogni...

引用

conference on computer vision and pattern recognition (cvpr)

作者： S.J.D. Prince J.H. Elder Centre for Vision Research York University Toronto ONT Canada

A major goal for face recognition is to identify faces where the pose of the probe is different from the stored face. Typical feature vectors vary more with pose than with identity, leading to very poor recognition performance. We propose a non-linear many-to-one mapping from a conventional feature space to a new space constructed so that each individual has a unique feature vector regardless of pose. Training data is used to implicitly parameterize the position of the multi-dimensional face manifold by pose. We introduce a co-ordinate transform, which depends on the position on the manifold. This transform is chosen so that different poses of the same face are mapped to the same feature vector. The same approach is applied to illumination changes. We investigate different methods for creating features, which are invariant to both pose and illumination. We provide a metric to assess the discriminability of the resulting features. Our technique increases the discriminability of faces under unknown pose and lighting compared to contemporary methods.

关键词： Face recognition Probes Lighting Image databases Spatial databases Face detection Feature extraction Principal component analysis Magnetic heads Training data

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Towards complete generic camera calibration

Towards complete generic camera calibration

引用

conference on computer vision and pattern recognition (cvpr)

作者： Srikumar Ramalingam P. Sturm S.K. Lodha Department of Computer Science University of California Santa Cruz CA USA INRIA Rhône-Alpes GRAVIR-CNRS Montbonnot France

We consider the problem of calibrating a highly generic imaging model, that consists of a non-parametric association of a projection ray in 3D to every pixel in an image. Previous calibration approaches for this model do not seem to be directly applicable for cameras with large fields of view and non-central cameras. In this paper, we describe a complete calibration approach that should in principle be able to handle any camera that can be described by the generic imaging model. Initial calibration is performed using multiple-images of overlapping calibration grids simultaneously. This is then improved using pose estimation and bundle adjustment-type algorithms. The approach has been applied on a wide variety of central and non-central cameras including fisheye lens, catadioptric cameras with spherical and hyperbolic mirrors, and multi-camera setups. We also consider the question if non-central models are more appropriate for certain cameras than central models.

关键词： Cameras Calibration Pixel Optical imaging Mirrors computer science Lenses Solid modeling Optical distortion Shape

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Automatic Sign Detection and recognition in Natural Scenes

Automatic Sign Detection and Recognition in Natural Scenes

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： P. Silapachote J. Weinman A. Hanson M.A. Mattar R. Weiss Department of Computer Science University of Massachusetts Amherst Amherst MA USA Hampshire College School of Cognitive Sciences Amherst MA USA

Visually impaired individuals are unable to utilize the significant amount of information in signs. VIDI is a system for detecting and recognizing signs in the environment and voice synthesizing their contents. The wide variety of signs and unconstrained imaging conditions make the problem challenging. We detect signs using local color and texture features to classify image regions with a conditional maximum entropy model. Detected sign regions are then recognized by matching them against a known database of signs. A support vector machine classifier uses color to focus the search, and a match is found based on the correspondences of corners and their associated shape contexts. Our dataset includes images of downtown scenes with several signs exhibiting both illumination differences and projective distortions. A wide range of signs are detected and recognized including both text and symbolic information. The detection and the recognition components each perform well on their respective tasks, and initial evaluations of a complete detection and recognition system are promising.

关键词： Layout Speech recognition Automatic speech recognition Entropy Image databases Spatial databases Support vector machines Support vector machine classification Focusing Shape

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Cross-generalization: learning novel classes from a single example by feature replacement

Cross-generalization: learning novel classes from a single e...

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conference on computer vision and pattern recognition (cvpr)

作者： E. Bart S. Ullman Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel

We develop an object classification method that can learn a novel class from a single training example. In this method, experience with already learned classes is used to facilitate the learning of novel classes. Our classification scheme employs features that discriminate between class and non-class images. For a novel class, new features are derived by selecting features that proved useful for already learned classification tasks, and adapting these features to the new classification task. This adaptation is performed by replacing the features from already learned classes with similar features taken from the novel class. A single example of a novel class is sufficient to perform feature adaptation and achieve useful classification performance. Experiments demonstrate that the proposed algorithm can learn a novel class from a single training example, using 10 additional familiar classes. The performance is significantly improved compared to using no feature adaptation. The robustness of the proposed feature adaptation concept is demonstrated by similar performance gains across 107 widely varying object categories.

关键词： Horses Costs Dogs Cows computer science Mathematics Robustness Performance gain Training data Humans

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Error-metrics for Camera Ego-motion Estimation

Error-metrics for Camera Ego-motion Estimation

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Juhua Zhu Ying Zhu V. Ramesh Electrical Engineering Department Princeton University USA Real-Time Vision & Modeling Siemens Corporate Research Inc. USA

This paper presents a scheme of camera ego-motion estimation through locating the focus of expansion (FOE). We showed that the bilinear constraint [2] leads to a suboptimal solution of motion parameters in the sense that it does not correspond to maximum likelihood estimate. The contribution of the paper is that we study different error metrics, evaluate the metrics, and propose to use two normalized error metrics under dependent and independent noise model, respectively. They are demonstrated to be optimal in the sense of maximum likelihood. In addition, based on the bilinear nature of the objective functions, we propose to use some specific optimization algorithms to achieve efficient and accurate convergence. Robust estimation problem is also addressed to handle outliers caused by independent motions. Promising results have been obtained in experiments. The estimated motion parameters can be used to detect various independently moving objects on the road.

关键词： Cameras Motion estimation Maximum likelihood detection Maximum likelihood estimation Convergence Noise robustness Parameter estimation Object detection Motion detection Roads

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Flattening curved documents in images

Flattening curved documents in images

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conference on computer vision and pattern recognition (cvpr)

作者： Jian Liang D. DeMenthon D. Doermann Language and Media Processing Laboratory University of Maryland College Park MD USA

Compared to scanned images, document pictures captured by camera can suffer from distortions due to perspective and page warping. It is necessary to restore a frontal planar view of the page before other OCR techniques can be applied. In this paper we describe a novel approach for flattening a curved document in a single picture captured by an uncalibrated camera. To our knowledge this is the first reported method able to process general curved documents in images without camera calibration. We propose to model the page surface by a developable surface, and exploit the properties (parallelism and equal line spacing) of the printed textual content on the page to recover the surface shape. Experiments show that the output images are much more OCR friendly than the original ones. While our method is designed to work with any general developable surfaces, it can be adapted for typical special cases including planar pages, scans of thick books, and opened books.

关键词： Shape Books Optical character recognition software Image restoration Digital cameras Nonlinear distortion Laboratories Educational institutions Calibration Design methodology

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Online detection and classification of moving objects using progressively improving detectors

Online detection and classification of moving objects using ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： O. Javed S. Ali M. Shah Computer Vision Laboratory University of Central Florida Orlando FL USA

Boosting based detection methods have successfully been used for robust detection of faces and pedestrians. However, a very large amount of labeled examples are required for training such a classifier. Moreover, once trained, the boosted classifier cannot adjust to the particular scenario in which it is employed. In this paper, we propose a co-training based approach to continuously label incoming data and use it for online update of the boosted classifier that was initially trained from a small labeled example set. The main contribution of our approach is that it is an online procedure in which separate views (features) of the data are used for co-training, while the combined view (all features) is used to make classification decisions in a single boosted framework. The features used for classification are derived from principal component analysis of the appearance templates of the training examples. In order to speed up the classification, background modeling is used to prune away stationary regions in an image. Our experiments indicate that starting from a classifier trained on a small training set, significant performance gains can be made through online updation from the unlabeled data.

关键词： Object detection Face detection computer vision Surveillance Detectors Testing Boosting Robustness Principal component analysis Performance gain

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A Swarm-Based Volition/Attention Framework for Object recognition

A Swarm-Based Volition/Attention Framework for Object Recogn...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Y. Owechko S. Medasani HRL Laboratories LLC Malibu CA USA

Visual attention helps identify the salient parts of a scene and enables efficient object recognition by allocating visual resources to more relevant regions of the scene. In this paper, we present an object recognition framework that combines top-down volitional recognition with attention processes using a swarm of cooperating intelligent agents. Each agent in the swarm is a selfcontained independent classifier that can, given any location in the image, predict the presence of a particular object of interest. Our framework combines bottom-up attention and top-down object classification using Particle Swarm Optimization (PSO) dynamics in a novel architecture that utilizes spatially-modulated evolutionary search to rapidly detect objects of interest in a scene. We use bottom-up maps that are automatically built from saliency, past swarm experience, and constraints on possible object positions to modify the swarm’s behavior and help guide the swarm in locating objects. We present fast object detection/recognition results for a variety of video sequences. Our results show that our framework allows objects to be quickly and accurately located and classified using very sparse sampling of the scene.

关键词： Object recognition Layout Object detection Focusing Particle swarm optimization Laboratories Resource management Intelligent agent Video sequences Image sampling

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