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检索条件"任意字段=MIPPR 2007: Pattern Recognition and Computer Vision"

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Robust 3D Face recognition Using Learned Visual Codebook

Robust 3D Face Recognition Using Learned Visual Codebook

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Cheng Zhong Zhenan Sun Tieniu Tan National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

In this paper, we propose a novel learned visual code-book (LVC) for 3D face recognition. In our method, we first extract intrinsic discriminative information embedded in 3D faces using Gabor filters, then K-means clustering is adopted to learn the centers from the filter response vectors. We construct LVC by these learned centers. Finally we represent 3D faces based on LVC and achieve recognition using a nearest neighbor (NN) classifier. The novelty of this paper comes from 1) We first apply textons based methods into 3D face recognition; 2) We encompass the efficiency of Gabor features for face recognition and the robustness of texton strategy for texture classification simultaneously. Our experiments are based on two challenging databases, CASIA 3D face database and FRGC2.0 3D face database. Experimental results show LVC performs better than many commonly used methods.

关键词： Robustness Face recognition Spatial databases Data mining Gabor filters Principal component analysis Neural networks Flowcharts Tensile stress Histograms

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Scaled Motion Dynamics for Markerless Motion Capture

Scaled Motion Dynamics for Markerless Motion Capture

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Bodo Rosenhahn Thomas Brox Hans-Peter Seidel Max-Planck-Institute for Informatics Saarbrücken Germany CVPR Goup University of Bonn Bonn Germany

This work proposes a way to use a-priori knowledge on motion dynamics for markerless human motion capture (MoCap). Specifically, we match tracked motion patterns to training patterns in order to predict states in successive frames. Thereby, modeling the motion by means of twists allows for a proper scaling of the prior. Consequently, there is no need for training data of different frame rates or velocities. Moreover, the method allows to combine very different motion patterns. Experiments in indoor and outdoor scenarios demonstrate the continuous tracking of familiar motion patterns in case of artificial frame drops or in situations insufficiently constrained by the image data.

关键词： Tracking Training data Humans History Cameras Surface fitting Legged locomotion Informatics pattern matching Data mining

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Design and Deployment of Visible-Thermal Biometric Surveillance Systems

Design and Deployment of Visible-Thermal Biometric Surveilla...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Diego A. Socolinsky Equinox Corporation Baltimore MD USA

Automatic video surveillance in uncontrolled outdoor settings is a very challenging computer vision task. Nearly infinite variability of the environmental factors and the open-ended goals of many surveillance problems conspire to create situations where even the most advanced detection, tracking and recognition algorithms falter. While the common academic response to such challenges is to develop new, more powerful algorithms capable of handling a broader range of conditions with acceptable performance, this course of action is sometimes not appropriate from the industrial-commercial point of view. Sometimes systems must be deployed sooner than would allow for the development cycle of complex new algorithms, and must be more robust than most such algorithms can be expected to be on short notice. Under those circumstances, one may look toward better data quality as one means of improving performance while remaining close to the existing state-of-the-art in algorithmic technology. This is often the motivation for deployment of multimodal surveillance systems in the real world. This presentation constitutes a brief foray into design issues for visible-thermal systems, with emphasis on biometric surveillance.

关键词： Biometrics Sensor systems Object detection Cameras Costs Video surveillance Robustness Optical imaging computer vision Environmental factors

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Shape from Shading Under Various Imaging Conditions

Shape from Shading Under Various Imaging Conditions

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Abdelrehim H. Ahmed Aly A. Farag Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY USA

Most of the shape from shading (SFS) algorithms have been developed under the simplifying assumptions of a Lambertian surface, an orthographic projection, and a distant light source. Due to the difficulty of the SFS problem, only a small number of algorithms have been proposed for surfaces with non-Lambertian reflectance, and among those, only very few algorithms are applicable for surfaces with specular and diffuse reflectance. In this paper we propose a unified framework that is capable of solving the SFS problem under various settings of imaging conditions i.e., Lambertian or non-Lambertian, orthographic or perspective projection, and distant or nearby light source. The proposed algorithm represents the image irradiance equation of each setting as an explicit Partial Differential Equation (PDF). In our implementation we use the Lax-Friedrichs sweeping method to solve this PDF. To demonstrate the efficiency of the proposed algorithm, several comparisons with the state of the art of the SFS literature are given.

关键词： Shape Reflectivity Light sources Partial differential equations Nonlinear equations Cameras Brightness computer vision Differential equations Strips

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Adaptive Patch Features for Object Class recognition with Learned Hierarchical Models

Adaptive Patch Features for Object Class Recognition with Le...

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Conference on computer vision and pattern recognition (CVPR)

作者： Fabien Scalzo Justus H. Piater Computer Vision Laboratory University of Nevada Reno USA Montefiore Institute University of Liège Belgium

We present a hierarchical generative model for object recognition that is constructed by weakly-supervised learning. A key component is a novel, adaptive patch feature whose width and height are automatically determined. The optimality criterion is based on minimum-variance analysis, which first computes the variance of the appearance model for various patch deformations, and then selects the patch dimensions that yield the minimum variance over the training data. They are integrated into each level of our hierarchical representation that is learned in an iterative, bottom-up fashion. At each level of the hierarchy, pairs of features are identified that tend to occur at stable positions relative to each other, by clustering the configurational distributions of observed feature co-occurrences using expectation-maximization. For recognition, evidence is propagated using nonparametric belief propagation. Discriminative models are learned on the basis of our feature hierarchy by combining a SVM classifier with feature selection based on the Fisher score. Experiments on two very different, challenging image databases demonstrate the effectiveness of this framework for object class recognition, as well as the contribution of the adaptive patch features towards attaining highly competitive results.

关键词： Object recognition Analysis of variance Deformable models Training data Belief propagation Diversity reception Support vector machines Support vector machine classification Image databases Image recognition

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Quasi-Dense Wide Baseline Matching Using Match Propagation

Quasi-Dense Wide Baseline Matching Using Match Propagation

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Conference on computer vision and pattern recognition (CVPR)

作者： Juho Kannala Sami S. Brandt Machine Vision Group University of Oulu Finland

In this paper we propose extensions to the match propagation algorithm which is a technique for computing quasi-dense point correspondences between two views. The extensions make the match propagation applicable for wide baseline matching, i.e., for cases where the camera pose can vary a lot between the views. Our first extension is to use a local affine model for the geometric transformation between the images. The estimate of the local transformation is obtained from affine covariant interest regions which are used as seed matches. The second extension is to use the second order intensity moments to adapt the current estimate of the local affine transformation during the propagation. This allows a single seed match to propagate into regions where the local transformation between the views differs from the initial one. The experiments with real data show that the proposed techniques improve both the quality and coverage of the quasi-dense disparity map.

关键词： Image reconstruction Cameras Surface reconstruction Image sequences Geometry computer vision Layout Machine vision Solid modeling Algorithm design and analysis

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Shape Representation and Registration using Vector Distance Functions

Shape Representation and Registration using Vector Distance ...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Hossam Abd El Munim Aly A. Farag Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY USA

This paper introduces a new method for shape registration by matching vector distance functions. The vector distance function representation is more flexible than the conventional signed distance map since it enables us to better control the shapes registration process by using more general transformations. Based on this model, a variational frame work is proposed for the global and local registration of shapes which does not need any point correspondences. The optimization criterion can handle efficiently the estimation of the global registration parameters. A closed form solution is provided to handle an incremental free form deformation model for covering the local deformations. This is an advantage over the gradient descent optimization which is biased towards the initialization and is more time consuming. Results of real shapes registration will be demonstrated to show the efficiency of the proposed approach with small and large global/local deformations.

关键词： Shape measurement computer vision Shape control Image segmentation Distortion measurement Image processing Laboratories Closed-form solution Deformable models Biomedical imaging

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P3 & Beyond: Solving Energies with Higher Order Cliques

P3 & Beyond: Solving Energies with Higher Order Cliques

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Conference on computer vision and pattern recognition (CVPR)

作者： Pushmeet Kohli M. Pawan Kumar Philip H. S. Torr Oxford Brookes University UK

In this paper we extend the class of energy functions for which the optimal alpha-expansion and alphabeta-swap moves can be computed in polynomial time. Specifically, we introduce a class of higher order clique potentials and show that the expansion and swap moves for any energy function composed of these potentials can be found by minimizing a submodular function. We also show that for a subset of these potentials, the optimal move can be found by solving an st-mincut problem. We refer to this subset as the P 3 Potts model. Our results enable the use of powerful move making algorithms i.e. alpha-expansion and alphabeta-swap for minimization of energy functions involving higher order cliques. Such functions have the capability of modelling the rich statistics of natural scenes and can be used for many applications in computer vision. We demonstrate their use on one such application i.e. the texture based video segmentation problem.

关键词： Inference algorithms computer vision Polynomials Minimization methods Higher order statistics Layout Application software Random variables Labeling Approximation methods

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Multi-label image segmentation via max-sum solver

Multi-label image segmentation via max-sum solver

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Conference on computer vision and pattern recognition (CVPR)

作者： Banislav Micusik Tomas Pajdla Pattern Recognition and Image Processing Group Inst. of Computer Aided Automation University of Technology Vienna Austria Center for Machine Perception Dpt. of Cybernetics Czech Technical University Czech Republic

We formulate single-image multi-label segmentation into regions coherent in texture and color as a MAX-SUM problem for which efficient linear programming based solvers have recently appeared. By handling more than two labels, we go beyond widespread binary segmentation methods, e.g., MIN-CUT or normalized cut based approaches. We show that the MAX-SUM solver is a very powerful tool for obtaining the MAP estimate of a Markov random field (MRF). We build the MRF on superpixels to speed up the segmentation while preserving color and texture. We propose new quality functions for setting the MRF, exploiting priors from small representative image seeds, provided either manually or automatically. We show that the proposed automatic segmentation method outperforms previous techniques in terms of the global consistency error evaluated on the Berkeley segmentation database.

关键词： Image segmentation Image databases Belief propagation pattern recognition Image processing Automation Cybernetics Color Linear programming Markov random fields

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Algorithms for Batch Matrix Factorization with Application to Structure-from-Motion

Algorithms for Batch Matrix Factorization with Application t...

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Conference on computer vision and pattern recognition (CVPR)

作者： Jean-Philippe Tardif Adrien Bartoli Martin Trudeau Nicolas Guilbert Sebastien Roy Université de Montreal Canada CNRS-LASMEA France Lund University Sweden

Matrix factorization is a key component for solving several computer vision problems. It is particularly challenging in the presence of missing or erroneous data, which often arise in structure-from-motion. We propose batch algorithms for matrix factorization. They are based on closure and basis constraints, that are used either on the cameras or the structure, leading to four possible algorithms. The constraints are robustly computed from complete measurement sub-matrices with e.g. random data sampling. The cameras and 3D structure are then recovered through linear least squares. Prior information about the scene such as identical camera positions or orientations, smooth camera trajectory, known 3D points and coplanarity of some 3D points can be directly incorporated. We demonstrate our algorithms on challenging image sequences with tracking error and more than 95% missing data.

关键词： Cameras Transmission line matrix methods computer vision Robustness Iterative algorithms Kernel Motion estimation Image reconstruction Application software Image sampling

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