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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

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Dynamosaics: video mosaics with non-chronological time

Dynamosaics: video mosaics with non-chronological time

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Rav-Acha Y. Pritch D. Lischinski S. Peleg School of Computer Science and Engineering Hebrew University of Jerusalem Jerusalem Israel

With the limited field of view of human vision, our perception of most scenes is built over time while our eyes are scanning the scene. In the case of static scenes, this process can be modeled by panoramic mosaicing: stitching together images into a panoramic view. Can a dynamic scene, scanned by a video camera, be represented with a dynamic panoramic video even though different regions were visible at different times? In this paper, we explore time flow manipulation in video, such as the creation of new videos in which events that occurred at different times are displayed simultaneously. More general changes in the time flow are also possible, which enable re-scheduling the order of dynamic events in the video, for example. We generate dynamic mosaics by sweeping the aligned space-time volume of the input video by a time front surface and generating a sequence of time slices in the process. Various sweeping strategies and different time front evolutions manipulate the time flow in the video, enabling many unexplored and powerful effects, such as panoramic movies.

关键词： Layout Motion pictures Cameras Videoconference Humans Eyes computer science Contracts Stacking Strips

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conference on computer vision and pattern recognition (cvpr)

作者： Feng Kang Rong Jin R. Sukthankar Michigan State University USA Robotics Institute Carnegie Mellon University USA Intel Research Pittsburgh USA

Many computer vision applications, such as scene analysis and medical image interpretation, are ill-suited for traditional classification where each image can only be associated with a single class. This has stimulated recent work in multi-label learning where a given image can be tagged with multiple class labels. A serious problem with existing approaches is that they are unable to exploit correlations between class labels. This paper presents a novel framework for multi-label learning termed Correlated Label Propagation (CLP) that explicitly models interactions between labels in an efficient manner. As in standard label propagation, labels attached to training data points are propagated to test data points; however, unlike standard algorithms that treat each label independently, CLP simultaneously co-propagates multiple labels. Existing work eschews such an approach since naive algorithms for label co-propagation are intractable. We present an algorithm based on properties of submodular functions that efficiently finds an optimal solution. Our experiments demonstrate that CLP leads to significant gains in precision/recall against standard techniques on two real-world computer vision tasks involving several hundred labels.

关键词： Testing computer vision Application software Medical robotics Robot vision systems Image analysis Biomedical imaging Training data Frequency computer society

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Robust L/sub 1/ norm factorization in the presence of outliers and missing data by alternative convex programming

Robust L/sub 1/ norm factorization in the presence of outlie...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Qifa Ke T. Kanade Computer Science Department Carnegie Mellon University Pittsburgh PA USA

Matrix factorization has many applications in computer vision. Singular value decomposition (SVD) is the standard algorithm for factorization. When there are outliers and missing data, which often happen in real measurements, SVD is no longer applicable. For robustness iteratively re-weighted least squares (IRLS) is often used for factorization by assigning a weight to each element in the measurements. Because it uses L/sub 2/ norm, good initialization in IRLS is critical for success, but is nontrivial. In this paper, we formulate matrix factorization as a L/sub 1/ norm minimization problem that is solved efficiently by alternative convex programming. Our formulation 1) is robust without requiring initial weighting, 2) handles missing data straightforwardly, and 3) provides a framework in which constraints and prior knowledge (if available) can be conveniently incorporated. In the experiments we apply our approach to factorization-based structure from motion. It is shown that our approach achieves better results than other approaches (including IRLS) on both synthetic and real data.

关键词： Robustness Cost function Matrix decomposition Application software computer vision Singular value decomposition Noise measurement Maximum likelihood estimation Least squares approximation computer science

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A graphical model framework for coupling MRFs and deformable models

A graphical model framework for coupling MRFs and deformable...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Rui Huang V. Pavlovic D.N. Metaxas Division of Computer and Information Sciences Rutgers University USA

This paper proposes a new framework for image segmentation based on the integration of MRFs and deformable models using graphical models. We first construct a graphical model to represent the relationship of the observed image pixels, the true region labels and the underlying object contour. We then formulate the problem of image segmentation as the one of joint regioncontour inference and learning in the graphical model. The graphical model representation allows us to use an approximate structured variational inference technique to solve this otherwise intractable joint inference problem. Using this technique, the MAP solution to the original model is obtained by finding the MAP solutions of two simpler models, an extended MRF model and a probabilistic deformable model, iteratively and incrementally. In the extended MRF model, the true region labels are estimated using the BP algorithm in a band area around the estimated contour from the probabilistic deformable model, and the result in turn guides the probabilistic deformable model to an improved estimation of the contour. Experimental results show that our new hybrid method outperforms both the MRF-based and the deformable model-based methods.

关键词： Graphical models Deformable models Image segmentation Image edge detection Pixel Iterative algorithms computer vision pattern recognition Noise shaping Shape

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Recovering shape and reflectance model of non-lambertian objects from multiple views

Recovering shape and reflectance model of non-lambertian obj...

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conference on computer vision and pattern recognition (cvpr)

作者： Tianli Yu Ning Xu N. Ahuja Electrical and Computer Engineering Department University of Illinois Urbana-Champaign USA

This paper proposes an algorithm to simultaneously estimate both the 3D shape and parameters of a surface reflectance model from multiple views of an object made of a single material. The algorithm is based on a multiple view shape from shading method. A triangular mesh represents the shape of the object. The Phong reflectance model is used to model the surface reflectance. We iteratively find the shape and reflectance parameters that best fit all input images. Subdividing triangles in the mesh into smaller ones gradually refines the estimates of shape and reflectance model. The estimation takes into account both self-occlusion and self-shadowing. Analysis shows that the accuracy of reflectance estimation is limited by the triangle size in the shape model. We also propose to use Richardson extrapolation to overcome this and further refine the reflectance model estimate. The estimated 3D shape and reflectance model can be used to render the same object from different viewing directions and under different lighting conditions. Experimental results on both synthetic and real objects are given.

关键词： Shape Reflectivity Image reconstruction Photometry Stereo vision Rendering (computer graphics) Layout Surface reconstruction computer graphics computer vision

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Linear combination representation for outlier detection in motion tracking

Linear combination representation for outlier detection in m...

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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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A Shape Representation for Planar Curves by Shape Signature Harmonic Embedding

A Shape Representation for Planar Curves by Shape Signature ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Sang-Mook Lee A.L. Abbott N.A. Clark P.A. Araman Electrical and Computer Engineering Virginia Polytechnic Institute and State University Blacksburg VA USA SRS-4702 USDA Forest Service Blacksburg VA USA

This paper introduces a new representation for planar curves. From the well-known Dirichlet problem for a disk, the harmonic function embedded in a circular disk is solely dependent on specified boundary values and can be obtained from Poisson’s integral formula. We derive a discrete version of Poisson’s formula and assess its harmonic properties. Various shape signatures can be used as boundary values, whereas only the corresponding Fourier descriptors are needed for the framework. The proposed approach is similar to a scale space representation but exhibits greater generality by accommodating using any type of shape signature. In addition, it is robust to noise and computationally efficient, and it is guaranteed to have a unique solution. In this paper, we demonstrate that the approach has strong potential for shape representation and matching applications.

关键词： Shape Kernel computer vision USA Councils Poisson equations Harmonic analysis Power harmonic filters Low pass filters Embedded computing US Department of Agriculture

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Surface segmentation from stereo

Surface segmentation from stereo

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conference on computer vision and pattern recognition (cvpr)

作者： L.-H. Chen W.-C. Lin Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA

An algorithm for extracting and segmenting surface descriptions from stereo images is presented. Compared with the advance of range image segmentation, the progress of surface segmentation from stereo images is slow. The algorithm integrates the boundary-based and region-based approaches to achieve more satisfactory segmentation results. The algorithm yields such a rich description of a scene in terms of global surface patches and closed surface boundaries. These shape primitives are useful for subsequent high-level processing.< >

关键词： Image segmentation Stereo vision Surface fitting Layout Shape Iterative methods Merging Image analysis Low pass filters computer vision

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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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