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检索条件"任意字段=2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006"

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Improved Likelihood Function in Particle-based IR Eye Tracking

Improved Likelihood Function in Particle-based IR Eye Tracki...

引用

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

作者： D. Witzner Hansen R. Satria J. Sorensen R. Hammoud Dept. of Innovation IT University Copenhagen Denmark Delphi Electronics and Safety Kokomo Indiana USA Dept. of Innovation IT University Copenhagen

In this paper we propose a log likelihood-ratio function of foreground and background models used in a particle filter to track the eye region in dark-bright pupil image sequences. This model fuses information from both dark and bright pupil images and their difference image into one model. Our enhanced tracker overcomes the issues of prior selection of static thresholds during the detection of feature observations in the bright-dark difference images. The auto-initialization process is performed using cascaded classifier trained using adaboost and adapted to IR eye images. Experiments show good performance in challenging sequences with test subjects showing large head movements and under significant light conditions.

关键词： Particle tracking Lighting Face detection Eyes Layout Light sources computer vision Head Humans Robustness

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A variational approach to problems in calibration of multiple cameras

A variational approach to problems in calibration of multipl...

引用

conference on computer vision and pattern recognition (cvpr)

作者： G. Unal A. Yezzi Siemens AG Corporate Research and Development Princeton NJ USA Georgia Institute of Technology Atlanta GA USA

This paper addresses the problem of calibrating camera parameters using variational methods. One problem addressed in this paper is the severe lens distortion in wide angle/inexpensive camera lenses. The camera distortion effects lead to inaccurate 3D reconstructions and geometrical measurements if not accounted for. A second problem is the color calibration problem caused by variations in camera responses which results in different color measurements and affects the algorithms that depend on these measurements. We present multi-view stereo techniques based on variational ideas to address these calibration problems. To reduce computational complexity of such algorithms, we utilize a prior knowledge on the calibration object which is used in the process, and evolve the pose, orientation, and scale parameters of such a 3D model object. We derive the evolution equations for the distortion coefficients, the color calibration parameters of the cameras, and present experimental results which demonstrate their potential use.

关键词： Calibration Cameras Nonlinear distortion Lenses Distortion measurement Parameter estimation Image segmentation Image reconstruction computer vision Image edge detection

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Recognizing 3D objects from 2D images: an error analysis

Recognizing 3D objects from 2D images: an error analysis

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conference on computer vision and pattern recognition (cvpr)

作者： W.E.L. Grimson D.P. Huttenlocher T.D. Alter AI Laboratory MIT Cambridge MA USA Department of Computer Science Cornell University Ithaca NY USA

Object recognition systems that use a small number of pairings of data and model features to compute the 3D transformation from model to sensor coordinates are considered. The effects of 2D sensor uncertainty on such computations are examined. The uncertainty in transformation parameters is bounded, and the effect of this uncertainty on false positive recognition rates is analyzed.< >

关键词： Image recognition Error analysis Uncertainty Solid modeling Artificial intelligence Laboratories Object recognition Image analysis Integrated circuit modeling computer science

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Bayesian face recognition using support vector machine and face clustering

Bayesian face recognition using support vector machine and f...

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conference on computer vision and pattern recognition (cvpr)

作者： Zhifeng Li Xiaoou Tang Department of Information Engineering Chinese University of Hong Kong Sha Tin Hong Kong China

In this paper, we first develop a direct Bayesian based support vector machine by combining the Bayesian analysis with the SVM. Unlike traditional SVM-based face recognition method that needs to train a large number of SVMs, the direct Bayesian SVM needs only one SVM trained to classify the face difference between intra-personal variation and extra-personal variation. However, the added simplicity means that the method has to separate two complex subspaces by one hyper-plane thus affects the recognition accuracy. In order to improve the recognition performance we develop three more Bayesian based SVMs, including the one-versus-all method, the hierarchical agglomerative clustering based method, and the adaptive clustering method. We show the improvement of the new algorithms over traditional subspace methods through experiments on two face databases, the FERET database and the XM2VTS database.

关键词： Bayesian methods Face recognition Support vector machines Support vector machine classification Linear discriminant analysis Clustering algorithms Databases Karhunen-Loeve transforms Face detection Information analysis

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Extended-Regular Sequence for Automated Analysis of Microarray Images

Extended-Regular Sequence for Automated Analysis of Microarr...

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

作者： Hee-Jeong Jin Bong-Kyung Chun Hwan-Gue Cho Department of Computer Engineering Pusan National University busan South Korea

A variety of high throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy for can be applied in the analysis of data sets of thousands of genes during cellular differentiation and response.

关键词： Image analysis Image sequence analysis Throughput Wavelet analysis pattern analysis Wavelet transforms Delay effects Propagation delay Microorganisms Data analysis

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Uncalibrated and unsynchronized human motion capture: a stereo factorization approach

Uncalibrated and unsynchronized human motion capture: a ster...

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conference on computer vision and pattern recognition (cvpr)

作者： P. Tresadern I. Reid Department of Engineering Science University of Oxford Oxford UK

Human motion capture typically requires several high quality, synchronized and calibrated cameras in a studio environment and can be potentially costly and technically complex. Instead, we propose a system which combines and improves upon two existing techniques, yielding an efficient method that recovers maximum likelihood joint angles and anthropomorphic data of the subject by factorization. The first technique concerns using a rank constraint framework to synchronize sequences of non-rigid motions where we extend affine methods to perspective and homography projection models. The second is a self-calibration method for two affine cameras, using constraints derived from prior knowledge of the underlying structure. We propose a minimal parameterization of the system to obtain an initial solution then apply a full bundle adjustment over the free parameters based on a geometric error We demonstrate the efficacy of our method by comparing the recovered structure and motion with that from a commercial motion capture system.

关键词： Humans Cameras Calibration Motion estimation computer vision Anthropomorphism Motion control Control systems Tracking Image reconstruction

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Layered object detection for multi-class segmentation

Layered object detection for multi-class segmentation

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conference on computer vision and pattern recognition (cvpr)

作者： Yi Yang Sam Hallman Deva Ramanan Charless Fowlkes Department of Computer Science University of California Irvine USA

We formulate a layered model for object detection and multi-class segmentation. Our system uses the output of a bank of object detectors in order to define shape priors for support masks and then estimates appearance, depth ordering and labeling of pixels in the image. We train our system on the PASCAL segmentation challenge dataset and show good test results with state of the art performance in several categories including segmenting humans.

关键词： Object detection Image segmentation Detectors Shape Labeling Pixel computer science System testing Humans Benchmark testing

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Creating invariance to "nuisance parameters" in face recognition

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

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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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Scene-adapted structured light

Scene-adapted structured light

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conference on computer vision and pattern recognition (cvpr)

作者： T.P. Koninckx P. Peers P. Dutre L. Van Gool Department ESAT / VISICS Katholieke Universiteit Leuven Leuven Belgium Department Computer Science Katholieke Universiteit Leuven Leuven Belgium D-ITET / BIWI Swiss Federal Institute of Technology Zurich Switzerland

In order to overcome several limitations of structured light 3D acquisition methods, the colors, intensities, and shapes of the projected patterns are adapted to the scene. Based on a crude estimate of the scene geometry and reflectance characteristics, the local intensity ranges in the projected patterns are adapted, in order to avoid over- and under-exposure in the image. This avoids the infamous specularity problems and generally increases accuracy. The estimated geometry also helps to limit the effect of aliasing caused by the sampling of foreshortened patterns. Furthermore, the approach also accounts for the adverse effects that small motions during scanning would normally have. Moreover, the approach yields a confidence measure at every pixel of the range image. Last but not least, the scanner consists of consumer products only, and therefore is cheap.

关键词： Layout Geometry Cameras Costs Reflectivity Robustness computer science Shape Image sampling Pixel

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How CanWe Evaluate Object recognition Algorithms Using a Public Object Image Database?

How CanWe Evaluate Object Recognition Algorithms Using a Pub...

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

作者： Soo Chahn Lee Duck Hoon Kim Sang Uk Lee Dong Yun Yun School of EECS Seoul National University Seoul South Korea School of EIE Hankuk University of Foreinn Studies Yongin si South Korea

In this paper, to evaluate the performance of object recognition algorithms, we propose a new evaluation framework by synthesizing natural scenes based on the Amsterdam Library of Object Images [1]. Here, the evaluation of an object recognition algorithm has the basis on searching an object in a synthetic scene. More specifically, an object is selected, and then the synthetic scene under a specific condition is generated by using images, affected by that condition, of that object and other objects in the database. When generating the synthetic scene, the other objects are randomly selected and all objects are naturally distributed in the synthetic scene. Let us refer to this synthetic scene as a Virtual Scene. Then the performance of object recognition algorithms for the specific condition can be analyzed by using a group of Virtual Scenes in that condition. As an example of utilizing the proposed framework, an object recognition algorithm using the scale-invariant feature transform [2] has been evaluated and analyzed in the case of changing the viewing direction, illumination color, and illumination direction.

关键词： Object recognition Image databases Layout Lighting Algorithm design and analysis Performance analysis computer vision Face recognition Libraries Image generation

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