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Linear combination representation for outlier detection in motion tracking

Linear combination representation for outlier detection in m...

引用

conference on computer vision and pattern recognition (cvpr)

作者： G. Guo C.R. Dyer Z. Zhang Computer Sciences Department University of Wisconsin Madison Madison WI USA Microsoft Research Limited Redmond WA USA

In this paper we show that Ullman and Basri's linear combination (LC) representation, which was originally proposed for alignment-based object recognition, can be used for outlier detection in motion tracking with an affine camera. For this task LC can be realized either on image frames or feature trajectories, and therefore two methods are developed which we call linear combination of frames and linear combination of trajectories. For robust estimation of the linear combination coefficients, the support vector regression (SVR) algorithm is used and compared with the RANSAC method. SVR based on quadratic programming optimization can efficiently deal with more than 50 percent outliers and delivers more consistent results than RANSAC in our experiments. The linear combination representation can use SVR in a straightforward manner while previous factorization-based or subspace separation methods cannot. Experimental results are presented using real video sequences to demonstrate the effectiveness of our LC+SVR approaches, including a quantitative comparison of SVR and RANSAC.

关键词： Motion detection Tracking Robustness Vectors Video sequences Object recognition Object detection Image sequences computer vision Cameras

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Pose-robust face recognition using geometry assisted probabilistic modeling

Pose-robust face recognition using geometry assisted probabi...

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conference on computer vision and pattern recognition (cvpr)

作者： Xiaoming Liu Tsuhan Chen General Electric Global Research Center Visualization and Computer Vision Laboratory Schenectady NY USA Advanced Multimedia Processing Lab Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA

ISBN: (纸本)0769523722

Researchers have been working on human face recognition for decades. Face recognition is hard due to different types of variations in face images, such as pose, illumination and expression, among which pose variation is the hardest one to deal with. To improve face recognition under pose variation, this paper presents a geometry assisted probabilistic approach. We approximate a human head with a 3D ellipsoid model, so that any face image is a 2D projection of such a 3D ellipsoid at a certain pose. In this approach, both training and test images are back projected to the surface of the 3D ellipsoid, according to their estimated poses, to form the texture maps. Thus the recognition can be conducted by comparing the texture maps instead of the original images, as done in traditional face recognition. In addition, we represent the texture map as an array of local patches, which enables us to train a probabilistic model for comparing corresponding patches. By conducting experiments on the CMU PIE database, we show that the proposed algorithm provides better performance than the existing algorithms.

关键词： Face recognition Geometry Solid modeling Ellipsoids Humans Lighting Head Testing Surface texture Image recognition

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Separating style and content on a nonlinear manifold

Separating style and content on a nonlinear manifold

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conference on computer vision and pattern recognition (cvpr)

作者： A. Elgammal Chan-Su Lee Department of Computer Science Rutgers University New Brunswick NJ USA

Bilinear and multi-linear models have been successful in decomposing static image ensembles into perceptually orthogonal sources of variations, e.g., separation of style and content. If we consider the appearance of human motion such as gait, facial expression and gesturing, most of such activities result in nonlinear manifolds in the image space. The question that we address in this paper is how to separate style and content on manifolds representing dynamic objects. In this paper we learn a decomposable generative model that explicitly decomposes the intrinsic body configuration (content) as a function of time from the appearance (style) of the person performing the action as time-invariant parameter. The framework we present in this paper is based on decomposing the style parameters in the space of nonlinear functions which map between a learned unified nonlinear embedding of multiple content manifolds and the visual input space.

关键词： Legged locomotion Tensile stress Shape Humans Principal component analysis Head Image analysis computer science Biological system modeling Face recognition

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Detecting text in natural scenes with stroke width transform

Detecting text in natural scenes with stroke width transform

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conference on computer vision and pattern recognition (cvpr)

作者： Boris Epshtein Eyal Ofek Yonatan Wexler Microsoft Corporation USA

We present a novel image operator that seeks to find the value of stroke width for each image pixel, and demonstrate its use on the task of text detection in natural images. The suggested operator is local and data dependent, which makes it fast and robust enough to eliminate the need for multi-scale computation or scanning windows. Extensive testing shows that the suggested scheme outperforms the latest published algorithms. Its simplicity allows the algorithm to detect texts in many fonts and languages.

关键词： Layout Optical character recognition software Pixel computer vision Filter bank Engines Image segmentation Colored noise Geometry Robustness

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Exploratory active vision: theory

Exploratory active vision: theory

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conference on computer vision and pattern recognition (cvpr)

作者： J.-Y. Herve Y. Aloimonos Computer Vision Laboratory Center for Automation Research Department of Computer Science Institute for Advanced Computer Studies University of Maryland College Park MD USA

An active approach to the integration of shape from x modules-here shape from shading and shape from texture-is proposed. The question of what constitutes a good motion for the active observer is addressed. Generally, the role of the visual system is to provide depth information to an autonomous robot; a trajectory module will then interpret it to determine a motion for the robot, which in turn will affect the visual information received. It is suggested that the motion can also be chosen so as to improve the performance of the visual system.< >

关键词： Layout Motion analysis Active shape model Cameras computer vision Robots Visual system Laboratories Automation computer science

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Attention control for robot vision

Attention control for robot vision

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conference on computer vision and pattern recognition (cvpr)

作者： C.-F. Westin C.-J. Westelius H. Knutsson G. Granlund Computer Vision Laboratory Department of Electrical Engineering Linkoping University Linkoping Sweden Brigham and Womens Hospital Harvard Medical School Surgical Planning Laboratory Boston MA USA

Focus of attention mechanisms for robot vision are discussed. A new method for neglecting low level filter responses from already modelled structures is presented. The method is based on a filtering technique termed normalized convolution. In one experiment, the robot is continuously moving its arm in the scene while tracking other objects. It is shown how the arm can be made "invisible" so that only the moving object of interest is detected. This makes tracking of objects much simpler. In another experiment, the attention of the system is shifted between objects by simply cancelling the mask of the object to be attended to. With this strategy the low level processes do not need to know the difference between a new object entering the scene and a mask being cancelled, and thus a complex communication structure between high and low levels is avoided.

关键词： Robot control Robot vision systems Layout Laboratories Focusing Robot sensing systems Feature extraction Context modeling Computational modeling Filtering

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Beyond pairwise clustering

Beyond pairwise clustering

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conference on computer vision and pattern recognition (cvpr)

作者： S. Agarwal Jongwoo Lim L. Zelnik-Manor P. Perona D. Kriegman S. Belongie Department of Computer Science & Engineering University of California San Diego USA Department of Electrical Engineering California Institute of Technology USA

We consider the problem of clustering in domains where the affinity relations are not dyadic (pairwise), but rather triadic, tetradic or higher. The problem is an instance of the hypergraph partitioning problem. We propose a two-step algorithm for solving this problem. In the first step we use a novel scheme to approximate the hypergraph using a weighted graph. In the second step a spectral partitioning algorithm is used to partition the vertices of this graph. The algorithm is capable of handling hyperedges of all orders including order two, thus incorporating information of all orders simultaneously. We present a theoretical analysis that relates our algorithm to an existing hypergraph partitioning algorithm and explain the reasons for its superior performance. We report the performance of our algorithm on a variety of computer vision problems and compare it to several existing hypergraph partitioning algorithms.

关键词： Clustering algorithms Partitioning algorithms computer vision Machine learning algorithms Clustering methods Approximation algorithms computer science Performance analysis Algorithm design and analysis Machine learning

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A SIFT descriptor with global context

A SIFT descriptor with global context

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conference on computer vision and pattern recognition (cvpr)

作者： E.N. Mortensen Hongli Deng L. Shapiro Oregon State University USA University of Washington USA

Matching points between multiple images of a scene is a vital component of many computer vision tasks. Point matching involves creating a succinct and discriminative descriptor for each point. While current descriptors such as SIFT can find matches between features with unique local neighborhoods, these descriptors typically fail to consider global context to resolve ambiguities that can occur locally when an image has multiple similar regions. This paper presents a feature descriptor that augments SIFT with a global context vector that adds curvilinear shape information from a much larger neighborhood, thus reducing mismatches when multiple local descriptors are similar. It also provides a more robust method for handling 2D nonrigid transformations since points are more effectively matched individually at a global scale rather than constraining multiple matched points to be mapped via a planar homography. We have tested our technique on various images and compare matching accuracy between the SIFT descriptor with global context to that without.

关键词： Shape computer vision Insects pattern matching Layout Image reconstruction Cameras Humans Image resolution Robustness

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Flexible spatial models for grouping local image features

Flexible spatial models for grouping local image features

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conference on computer vision and pattern recognition (cvpr)

作者： G. Carneiro A.D. Jepson Department of Computer Science University of Toronto Toronto ONT Canada

A key step for the effective use of local image features (i.e., highly distinctive and robust features) for recognition or image matching is the appropriate grouping of feature matches. Spatial constraints are important in this grouping because, during a recognition process, they allow for the reduction of the number of hypotheses that must be verified and also reduce the number of false positives present in each of these hypotheses. A common choice for this grouping task is to use the Hough transform on the global spatial transformation parameters of the hypothesized matches. Here, instead, we use semi-local spatial constraints which allow for a greater range of shape deformations. A comparison with Hough transform shows that our method is more robust to both rigid and non-rigid deformations. Its functionality is demonstrated in an exemplar-based object recognition system that deals well with severe non-rigid deformations. We also show the efficacy of our flexible spatial grouping far long range motion problems.

关键词： Robustness Image databases Spatial databases Image recognition Object recognition Gabor filters Object detection computer science Image matching Shape

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Efficient search of faces from complex line drawings

Efficient search of faces from complex line drawings

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conference on computer vision and pattern recognition (cvpr)

作者： Jianzhuang Liu Xiaoou Tang Department of Information Engineering Chinese University of Hong Kong Hong Kong China

Single 2D line drawing is a straightforward method to illustrate 3D objects. The faces of an object depicted by a line drawing give very useful information for the reconstruction of its 3D geometry. Each of the two recently proposed methods for face identification from line drawings involves two combinatorial problems. When dealing with complex objects having more faces, the combinatorial explosion prevents these methods from practical uses. This paper proposes a new approach to tackling the face identification problem by a variable-length genetic algorithm (GA) with geometric constraints and a novel heuristic incorporated for local search. The hybrid GA solves the two combinatorial problems simultaneously. Experimental results show that our algorithm can find the faces of a line drawing having more than 30 faces much more efficiently.

关键词： Circuits Face detection Engineering drawings Explosions Genetic algorithms computer vision Information geometry Computational geometry Application software Design automation

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