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Joint boosting feature selection for robust face recognition

Joint boosting feature selection for robust face recognition

引用

2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Xiao, Rong Li, Wujun Tian, Yuandong Tang, Xiaoou Microsoft Research Asia Beijing 100080 China National Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai 200030 China

ISBN: (纸本)0769525970

A fundamental challenge in face recognition lies in determining what facial features are important for the identification of faces. In this paper, a novel face recognition framework is proposed to address this problem. In our framework, 3D face models are used to synthesize a huge database of realistic face images which covers wide appearance variations of faces due to various pose, illumination, and expression changes. A novel feature selection algorithm which we call Joint Boosting is developed to extract discriminative face features using this massive database. The major contributions of this paper are: (1) With the help of 3D face models, a massive database of realistic virtual face images is generated to achieve robust feature selection;(2)Because the huge database covers a wide range of face variations, our feature selection procedure only needs to be trained once, and the selected feature set can be generalized to other face database without re-training;(3) We propose a new learning algorithm, Joint Boosting Algorithm, which is effective and efficient in learning directly from a massive database without having to convert face images to intra-personal and extra-personal difference images. This property is important for applying our algorithm to other general pattern recognition problems. Experimental results show that our method significantly improves recognition performance. © 2006 ieee.

关键词： Face recognition

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Estimating intrinsic component images using non-linear regression

Estimating intrinsic component images using non-linear regre...

引用

2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Tappen, Marshall F. Adelson, Edward H. Freeman, William T. MIT Computer Science and Artificial Intelligence Laboratory Cambridge MA 02139

ISBN: (纸本)0769525970

Images can be represented as the composition of multiple intrinsic component images, such as shading, albedo, and noise images. In this paper, we present a method for estimating intrinsic component images from a single image, which we apply to the problems of estimating shading and albedo images and image denoising. Our method is based on learning estimators that predict filtered versions of the desired image. Unlike previous approaches, our method does not require unnatural discretizations of the problem. We also demonstrate how to learn a weighting function that properly weights the local estimates when constructing the estimated image. For shading estimation, we introduce a new training set of real-world images. The accuracy of our method is measured both qualitatively and quantitatively, showing better performance on the shading/albedo separation problem than previous approaches. The performance on denoising is competitive with the current state of the art. © 2006 ieee.

关键词： Image processing

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Modeling correspondences for multi-camera tracking using nonlinear manifold learning and target dynamics

Modeling correspondences for multi-camera tracking using non...

引用

2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Morariu, Vlad I. Camps, Octavia I. Computer Vision Laboratory University of Maryland College Park MD 20742 Department of Electrical Engineering Pennsylvania State University University Park PA 16802

ISBN: (纸本)0769525970

Multi-camera tracking systems often must maintain consistent identity labels of the targets across views to recover 3D trajectories and fully take advantage of the additional information available from the multiple sensors. Previous approaches to the "correspondence across views" problem include matching features, using camera calibration information, and computing homographies between views under the assumption that the world is planar. However, it can be difficult to match features across significantly different views. Furthermore, calibration information is not always available and planar world hypothesis can be too restrictive. In this paper, a new approach is presented for matching correspondences based on the use of nonlinear manifold learning and system dynamics identification. The proposed approach does not require similar views, calibration nor geometric assumptions of the 3D environment, and is robust to noise and occlusion. Experimental results demonstrate the use of this approach to generate and predict views in cases where identity labels become ambiguous. © 2006 ieee.

关键词： Video cameras

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Shape guided object segmentation

Shape guided object segmentation

引用

2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Borenstein, Eran Malik, Jitendra Division of Applied Mathematics Brown University Department of Electrical Engineering and Computer Science U.C. Berkeley

ISBN: (纸本)0769525970

We construct a Bayesian model that integrates top-down with bottom-up criteria, capitalizing on their relative merits to obtain figure-ground segmentation that is shape-specific and texture invariant. A hierarchy of bottom-up segments in multiple scales is used to construct a prior on all possible figure-ground segmentations of the image. This prior is used by our top-down part to query and detect object parts in the image using stored shape templates. The detected parts are integrated to produce a global approximation for the object's shape, which is then used by an inference algorithm to produce the final segmentation. Experiments with a large sample of horse and runner images demonstrate strong figure-ground segmentation despite high object and background variability. The segmentations are robust to changes in appearance since the matching component depends on shape criteria alone. The model may be useful for additional visual tasks requiring labeling, such as the segmentation of multiple scene objects. © 2006 ieee.

关键词： Image segmentation

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Discriminative learning of mixture of bayesian network classifiers for sequence classification

Discriminative learning of mixture of bayesian network class...

引用

2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Kim, Minyoung Pavlovic, Vladimir Department of Computer Science Rutgers University Piscataway NJ 08854

ISBN: (纸本)0769525970

A mixture of Bayesian Network Classifiers(BNC) has a potential to yield superior classification and generative performance to a single BNC model. We introduce novel discriminative learning methods for mixtures of BNCs. Unlike a single BNC model where the discriminative learning resorts to a gradient search, we can exploit the properties of a mixture to alleviate the complex learning task. The proposed method adds mixture components recursively via functional gradient boosting while maximizing the conditional likelihood. This method is highly efficient as it reduces to generative learning of a base BNC model on weighed data. The proposed approach is particularly suited to sequence classification problems where the kernels in the base model are usually too complex for effective gradient search. We demonstrate the improved classification performance of the proposed methods in an extensive set of evaluations on time-series sequence data, including human motion classification problems. © 2006 ieee.

关键词： Neural networks

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Acceleration strategies for Gaussian mean-shift image segmentation

Acceleration strategies for Gaussian mean-shift image segmen...

引用

2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Carreira-Perpiñán, Miguel Á. Dept. of Computer Science and Electrical Eng. OGI Oregon Health and Science University

ISBN: (纸本)0769525970

Gaussian mean-shift (GMS) is a clustering algorithm that has been shown to produce good image segmentations (where each pixel is represented as a feature vector with spatial and range components). GMS operates by defining a Gaussian kernel density estimate for the data and clustering together points that converge to the same mode under a fixed-point iterative scheme. However, the algorithm is slow, since its complexity is O(kN2), where N is the number of pixels and k the average number of iterations per pixel. We study four acceleration strategies for GMS based on the spatial structure of images and on the fact that GMS is an expectation-maximisation (EM) algorithm: spatial discretisation, spatial neighbourhood, sparse EM and EM-Newton algorithm. We show that the spatial discretisation strategy can accelerate GMS by one to two orders of magnitude while achieving essentially the same segmentation;and that the other strategies attain speedups of less than an order of magnitude. © 2006 ieee.

关键词： Image segmentation

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Tracking with Sobolev active contours

Tracking with Sobolev active contours

引用

2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Sundaramoorthi, Ganesh Jackson, Jeremy D. Yezzi, Anthony Mennucci, Andrea C. School of Electrical and Computer Engineering Georgia Institute of Technology Scuola Normale Superiore

ISBN: (纸本)0769525970

Recently proposed Sobolev active contours introduced a new paradigm for minimizing energies defined on curves by changing the traditional cost of perturbing a curve and thereby redefining their gradients. Sobolev active contours evolve more globally and are less attracted to certain intermediate local minima than traditional active contours. In this paper we analyze Sobolev active contours in the Fourier domain in order to understand their evolution across different scales. This analysis shows an important and useful behavior of Sobolev contours, namely, that they move successively from coarse to increasingly finer scale motions in a continuous manner. Along -with other properties, the previous observation reveals that Sobolev active contours are ideally suited for tracking problems that use active contours. Our purpose in this work is to show how a variety of active contour based tracking methods can be significantly improved merely by evolving the active contours according to the Sobolev method. © 2006 ieee.

关键词： Tracking (position)

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Shape from shading: Recognizing the mountains through a global view

Shape from shading: Recognizing the mountains through a glob...

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2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Qihui, Zhu Jianbo, Shi Dept. of Computer and Information Science University of Pennsylvania Philadelphia PA 19104

ISBN: (纸本)0769525970

Resolving local ambiguities in an important issue for shape from shading (SFS). Pixel ambiguities of SFS can be eliminated by propagation approaches. However, patch ambiguities still exist. Therefore, we formulate the global disambiguation problem to resolve these ambiguities. Intuitively, it can he interpreted as flipping patches and adjusting heights such that the result surface has no kinks. The problem is intractable because exponentially many possible configurations need to be checked. Alternatively, we solve the integrability testing problem closely related to the original one. It can be viewed as finding a surface which satisfies the global integrability constraint. To encode the constraints, we introduce a graph formulation called configuration graph. Searching the solution on this graph can be reduced to a Max-cut problem and its solution is computable using semidefinite programming (SDP) relaxation. Tests carried out on synthetic and real images show that the global disambiguation works well for complex shapes. © 2006 ieee.

关键词： Computational geometry

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Motion estimation from spheres

Motion estimation from spheres

引用

2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Zhang, Guoqiang Wong, Kwan-Yee K. Department of Computer Science University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)0769525970

This paper addresses the problem of recovering epipolar geometry from spheres. Previous works have exploited epipolar tangencies induced by frontier points on the spheres for motion recovery. It will be shown in this paper that besides epipolar tangencies, N2 point features can be extracted from the apparent contours of the N spheres when N > 2. An algorithm for recovering the fundamental matrices from such point features and the epipolar tangencies from 3 or more spheres is developed, with the point features providing a homography over the view pairs and the epipolar tangencies determining the epipoles. In general, there will be two solutions to the locations of the epipoles. One of the solutions corresponds to the true camera configuration, while the other corresponds to a mirrored configuration. Several methods are proposed to select the right solution. Experiments on using 3 and 4 spheres demonstrate that our algorithm can be carried out easily and can achieve a high precision. © 2006 ieee.

关键词： Motion estimation

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Multi-target tracking - Linking identities using Bayesian network inference

Multi-target tracking - Linking identities using Bayesian ne...

引用

2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Nillius, Peter Sullivan, Josephine Carlsson, Stefan Institute of Computer Science FORTH Vassilika Vouton GR-711 10 Heraklion Greece SE-100 44 Stockholm Sweden

ISBN: (纸本)0769525970

Multi-target tracking requires locating the targets and labeling their identities. The latter is a challenge when many targets, with indistinct appearances, frequently occlude one another, as in football and surveillance tracking. We present an approach to solving this labeling problem. When isolated, a target can be tracked and its identity maintained. While, if targets interact this is not always the case. This paper assumes a track graph exists, denoting when targets are isolated and describing how they interact. Measures of similarity between isolated tracks are defined. The goal is to associate the identities of the isolated tracks, by exploiting the graph constraints and similarity measures. We formulate this as a Bayesian network inference problem, allowing us to use standard message propagation to find the most probable set of paths in an efficient way. The high complexity inevitable in large problems is gracefully reduced by removing dependency links between tracks. We apply the method to a 10 min sequence of an international football game and compare results to ground truth. © 2006 ieee.

关键词： Radar target recognition

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