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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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A minimal solution for relative pose with unknown focal length

A minimal solution for relative pose with unknown focal leng...

引用

conference on computer vision and pattern recognition (cvpr)

作者： H. Stewenius D. Nister F. Kahl F. Schaffalitzky Lund University Sweden University of Kentucky Lexington USA University of California San Diego USA University of Oxford UK

Assume that we have two perspective images with known intrinsic parameters except for an unknown common focal length. It is a minimally constrained problem to find the relative orientation between the two images given six corresponding points. We present an efficient solution to the problem and show that there are 15 solutions in general (including complex solutions). To the best of our knowledge this was a previously unsolved problem. The solutions are found through eigen-decomposition of a 15/spl times/15 matrix. The matrix itself is generated in closed form. We demonstrate through practical experiments that the algorithm is correct and numerically stable.

关键词： Cameras computer vision Equations Image reconstruction Video sequences Engines Stability analysis Noise measurement Computational geometry

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A New Method for Automatic 3D Face Registration

A New Method for Automatic 3D Face Registration

引用

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

作者： V.R. Ayyagari F. Boughorbel A. Koschan M.A. Abidi University of Tennessee Knoxville TN USA

In view of today’s security concerns, 3D face reconstruction and recognition has gained a significant position in computer vision research. Depth information of a 3D face can be used to solve the problems of illumination and pose variation associated with face recognition. Registration is an integral part of any reconstruction process and hence we focus on the problem of automatic registration of 3D face point sets through a criterion based on Gaussian fields. The method defines a simple energy function, which is always differentiable and convex in a large neighborhood of the alignment parameters; allowing for the use of powerful standard optimization techniques. The new method overcomes the necessity of close initialization, which is required by Iterative Closest Point algorithm. Moreover, the use of the Fast Gauss Transform reduces the computational complexity of the registration algorithm.

关键词： Face detection Face recognition computer security Information security computer vision Lighting Optimization methods Iterative closest point algorithm Gaussian processes Computational complexity

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Unstructured point cloud matching within graph-theoretic and thermodynamic frameworks

Unstructured point cloud matching within graph-theoretic and...

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Jagannathan E.L. Miller Center for Sub-surface Sensing and Imaging Systems Northeastern University Boston MA USA

In the context of object recognition from point cloud data, we present a thermodynamically-inspired graph theoretic algorithm to address the problem of matching the scene and the model point clouds, when the cardinalities of the two sets are orders of magnitude different. Such an approach determines a subset of points from the model that is structurally and spatially as similar as possible to the set of points in the scene. A new formulation for graph enthalpy characterizes the structural differences between point sets, which together with the existing notions of graph entropy quantifies the Gibbs' free energy. A two-scale approach is proposed, wherein, at the coarse scale, a set of points that comprise the model neighborhood around the scene is identified by minimization of entropy. At the fine scale, the desired correspondence is achieved by a refinement process, aimed at maximizing the Gibbs' free energy. The results demonstrate the robustness and efficiency of the approach.

关键词： Clouds Thermodynamics Layout Entropy Robustness Graph theory computer vision Object recognition Context modeling Databases

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Affine object tracking with kernel-based spatial-color representation

Affine object tracking with kernel-based spatial-color repre...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Haihong Zhang Weimin Huang Zhiyong Huang Liyuan Li Institute for Infocomm Research Singapore School of Computing National University of Singapore Singapore

ISBN: (纸本)0769523722

This paper presents a new visual tracking method that can achieve accurate estimation of affine transformation and precise spatial-color representation. The estimation of transformation provides more information than translation for better motion understanding and also helps maintain the precise representation; the precise representation enables tracking objects in highly-cluttered environment. The basis of the method is a kernel-based similarity measure called affine matching that describes the relationship between image regions with respect to affine transformation parameters. Based on the similarity measure, a mathematical solution is derived for estimating the transformation parameters for moving objects in videos. Various experiments have yielded positive results.

关键词： Kernel computer vision Target tracking Histograms Deformable models Motion estimation Parameter estimation Videos Gaussian distribution Expectation-maximization algorithms

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Personal identification utilizing finger surface features

Personal identification utilizing finger surface features

引用

conference on computer vision and pattern recognition (cvpr)

作者： D.L. Woodard P.J. Flynn Department of Computer Science and Engineering University of Notre Dame Notre Dame IN USA

In this paper we present a novel approach for personal identification, which utilizes finger surface features as a biometric identifier. Using dense range data images of the hand, we calculate the curvature-based surface representation, shape index, for the index, middle, and ring fingers. This representation is used for comparisons to determine subject similarity. Our experiments involve the use of a large data set of range images collected over time. We examine the performance of individual finger surfaces as a biometric identifier as well as the performance when using the three finger surfaces in conjunction. The results of our experiments are presented, which indicate that this approach performs well for a first-of-its-kind biometric technique.

关键词： Fingers Biometrics Shape computer science Image sensors Application software Access control computer security Law enforcement Gratings

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Random subwindows for robust image classification

Random subwindows for robust image classification

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conference on computer vision and pattern recognition (cvpr)

作者： R. Maree P. Geurts J. Piater L. Wehenkel Department of Electrical Engineering and Computer Science Institut Montefiore University of Liège Liege Belgium

We present a novel, generic image classification method based on a recent machine learning algorithm (ensembles of extremely randomized decision trees). Images are classified using randomly extracted subwindows that are suitably normalized to yield robustness to certain image transformations. Our method is evaluated on four very different, publicly available datasets (COIL-100, ZuBuD, ETH-80, WANG). Our results show that our automatic approach is generic and robust to illumination, scale, and viewpoint changes. An extension of the method is proposed to improve its robustness with respect to rotation changes.

关键词： Robustness Image classification Testing Machine learning algorithms Image databases Lighting Spatial databases Machine learning Decision trees Geology

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Object class recognition by boosting a part-based model

Object class recognition by boosting a part-based model

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conference on computer vision and pattern recognition (cvpr)

作者： A. Bar-Hillel T. Hertz D. Weinshall School of Computer Science and Engineering and the Center for Neural Computation Hebrew University of Jerusalem Jerusalem Israel

We propose a new technique for object class recognition, which learns a generative appearance model in a discriminative manner. The technique is based on the intermediate representation of an image as a set of patches, which are extracted using an interest point detector. The learning problem becomes an instance of supervised learning from sets of unordered features. In order to solve this problem, we designed a classifier based on a simple, part based, generative object model. Only the appearance of each part is modeled. When learning the model parameters, we use a discriminative boosting algorithm which minimizes the loss of the training error directly. The models thus learnt have clear probabilistic semantics, and also maintain good classification performance. The performance of the algorithm has been tested using publicly available benchmark data, and shown to be comparable to other state of the art algorithms for this task; our main advantage in these comparisons is speed (order of magnitudes faster) and scalability.

关键词： Boosting Detectors computer science Supervised learning Benchmark testing Scalability computer vision Image representation Object recognition Image recognition

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Online selecting discriminative tracking features using particle filter

Online selecting discriminative tracking features using part...

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conference on computer vision and pattern recognition (cvpr)

作者： Jianyu Wang Xilin Chen Wen Gao School of Computer Science and Technology Harbin Institute of Technology Harbin China JDL Institute of Computing Chinese Academy and Sciences Beijing China

ISBN: (纸本)0769523722

The paper proposes a method to keep the tracker robust to background clutters by online selecting discriminative features from a large feature space. Furthermore, the feature selection procedure is embedded into the particle filtering process with the aid of existed "background" particles. Feature values from background patches and object observations are sampled during tracking and Fisher discriminant is employed to rank the classification capacity of each feature based on sampled values. Top-ranked discriminative features are selected into the appearance model and simultaneously invalid features are removed out to adjust the object representation adaptively. The implemented tracker with online discriminative feature selection module embedded shows promising results on experimental video sequences.

关键词： Particle tracking Particle filters Histograms Computational efficiency Space technology Robustness Gabor filters computer science Filtering Video sequences

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Integral Normalized Gradient Image A Novel Illumination Insensitive Representation

Integral Normalized Gradient Image A Novel Illumination Inse...

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

作者： S. Samsung Samsung Advanced Institute of Technology

In this paper, we present a novel illumination insensitive image, called integral normalized gradient image (INIGI), for face recognition. Unlike previous model-based methods, which require training images or have many str constrains for implementation, the proposed representation is simple and generic. Based on the intrinsic and extrinsic factor definition, we firstly normalized the gradient with a smoothed version of input image and then integrate the result into new grayscale image. To avoid unwanted smoothing effects on step edge region, anisotropic diffusion method is introduced. Experiment results on FRGC DB prove that our new approach is very effective in improving verification rate for both holistic and local features.

关键词： Lighting Face recognition Shape Feature extraction Gray-scale Image analysis Equations Smoothing methods Anisotropic magnetoresistance Principal component analysis

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Combining Local and Global Image Features for Object Class recognition

Combining Local and Global Image Features for Object Class R...

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

作者： D.A. Lisin M.A. Mattar M.B. Blaschko E.G. Learned-Miller M.C. Benfield Computer Vision Laboratory Department of Computer Science University of Massachusetts Amherst MA USA Department of Oceanography & Coastal Sciences/Fisheries Institute Louisiana State University Baton Rouge LA USA

Object recognition is a central problem in computer vision research. Most object recognition systems have taken one of two approaches, using either global or local features exclusively. This may be in part due to the difficulty of combining a single global feature vector with a set of local features in a suitable manner. In this paper, we show that combining local and global features is beneficial in an application where rough segmentations of objects are available. We present a method for classification with local features using non-parametric density estimation. Subsequently, we present two methods for combining local and global features. The first uses a "stacking" ensemble technique, and the second uses a hierarchical classification system. Results show the superior performance of these combined methods over the component classifiers, with a reduction of over 20% in the error rate on a challenging marine science application.

关键词： Image recognition Object recognition computer vision Robustness Face detection Laboratories computer science Sea measurements Aquaculture Error analysis

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