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检索条件"任意字段=2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005"

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Seeing beyond occlusions (and other marvels of a finite lens aperture)

Seeing beyond occlusions (and other marvels of a finite lens...

引用

conference on computer vision and pattern recognition (cvpr)

作者： P. Favaro S. Soatto Department of Electrical Engineering Washington University Saint Louis MO USA Department of Computer Science UCLA Los Angeles CA USA

We present a novel algorithm to reconstruct the geometry and photometry of a scene with occlusions from a collection of defocused images. The presence of a finite lens aperture allows us to recover portions of the scene that would be occluded in a pin-hole projection, thus "uncovering" the occlusion. We estimate the shape of each object (a surface, including the occluding boundaries), and its radiance (a positive function defined on the surface, including portions that are occluded by other objects).

关键词： Lenses Apertures Layout computer vision Solid modeling Computational geometry Cameras Image generation Shape measurement Humans

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Comparative object similarity for improved recognition with few or no examples

Comparative object similarity for improved recognition with ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Gang Wang David Forsyth Derek Hoiem Department of Electrical and Computer Engineering University of Illinois Urbana Champaign USA Department of Computer Science University of Illinois Urbana Champaign USA

Learning models for recognizing objects with few or no training examples is important, due to the intrinsic long-tailed distribution of objects in the real world. In this paper, we propose an approach to use comparative object similarity. The key insight is that: given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. We develop a regularized kernel machine algorithm to use this category dependent similarity regularization. Our experiments on hundreds of categories show that our method can make significant improvement, especially for categories with no examples.

关键词： Kernel Humans Birds computer science Management training Object recognition Leg computer vision Extraterrestrial measurements Clouds

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Towards Unlocking Web Video: Automatic People Tracking and Clustering

Towards Unlocking Web Video: Automatic People Tracking and C...

引用

ieee computer society conference on computer vision and pattern recognition Workshops

作者： Alex Holub Pierre Moreels Atiq Islam Andrei Makhanov Rui Yang Ooyala Inc CA

ISBN: (纸本)9781424439942

This paper describes a system for automatically extracting meta-information on people from videos on the web. The system contains multiple modules which automatically track people, including both faces and bodies, and clusters the people into distinct groups. We present new technology and significantly modify existing algorithms for body-detection, shot-detection and grouping, tracking, and track-clustering within our system. The system was designed to work effectivity on web content, and thus exhibits robust tracking and clustering behavior over a broad spectrum of professional and semi-professional video content. In order to quantify and evaluate our system we created a large ground-truth data-set of people within video. Finally, we provide actual video examples of our algorithm and find that the results are quite strong over a broad range of content.

关键词： Face detection computer vision Clustering algorithms Solids Image recognition Robustness Application software Information filtering Information filters Object recognition

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Comparison of EEG pattern classification methods for brain-computer interfaces

Comparison of EEG pattern classification methods for brain-c...

引用

29th Annual International conference of the ieee-Engineering-in-Medicine-and-Biology-society

作者： Dias, N. S. Kamrunnahar, A. Mendes, P. M. Schiff, S. J. Correia, J. H. Univ Minho Dept Ind Elect Campus Azurem P-4800058 Braga Portugal Penn State Univ Dept Engn Sci & Mech University Pk PA 16802 USA Penn State Univ Dept Engn Sci & Mechan Neurosurgery University Pk PA 16802 USA

ISBN: (纸本)9781424407873

The aim of this study is to compare 2 EEG pattern classification methods towards the development of BCI. The methods are: (1) discriminant stepwise, and (2) Principal Component Analysis (PCA)-Linear Discriminant Analysis (LDA) joint method. Both methods use Fisher's LDA approach, but differ in the data dimensionality reduction procedure. Data were recorded from 3 male subjects 20-30 years old. Three runs per subject took place. The classification methods were tested in 240 trials per subject after merging all runs for the same subject. The mental tasks performed were feet, tongue, left hand and right hand movement imagery. In order to avoid previous assumptions on preferable channel locations and frequency ranges, 105 (21 electrodesx5 frequency ranges) electroencephalogram (EEG) features were extracted from the data. The best performance for each classification method was taken into account. The discriminant stepwise method showed better performance than the PCA based method. The classification error by the stepwise method varied between 31.73% and 38.5% for all subjects whereas the error range using the PCA based method was 39.42% to 54%.

关键词： pattern recognition

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Motion estimation with non-local total variation regularization

Motion estimation with non-local total variation regularizat...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Manuel Werlberger Thomas Pock Horst Bischof Institute for Computer Graphics and Vision

State-of-the-art motion estimation algorithms suffer from three major problems: Poorly textured regions, occlusions and small scale image structures. Based on the Gestalt principles of grouping we propose to incorporate a low level image segmentation process in order to tackle these problems. Our new motion estimation algorithm is based on non-local total variation regularization which allows us to integrate the low level image segmentation process in a unified variational framework. Numerical results on the Middlebury optical flow benchmark data set demonstrate that we can cope with the aforementioned problems.

关键词： Motion estimation Image motion analysis Robustness Optical computing Image segmentation computer vision computer graphics Tracking Motion analysis Databases

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Pareto-optimal dictionaries for signatures

Pareto-optimal dictionaries for signatures

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conference on computer vision and pattern recognition (cvpr)

作者： Michael Calonder Vincent Lepetit Pascal Fua EPF Lausanne Lausanne Switzerland

We present an effective method to optimize over the parameters of an image patch descriptor to obtain one that is computationally more efficient while maintaining a high recognition rate. We formulate the optimization problem in a multi-objective manner, which balances two conflicting goals while removing the need for traditional weighting coefficients. To this end we introduce the Pareto efficiency criterion, which helps finding solutions that increase one objective without decreasing the other. Despite the vast size of the search space, we show how a state-of-the-art Genetic Algorithm can be tailored to find good solutions. Not only does the resulting descriptor perform better than state-of-the-art ones, but our approach is of broader significance as optimization problems with balanced goals are often encountered in computer vision.

关键词： Dictionaries Genetic algorithms computer vision Design optimization Image recognition Space exploration Application software Cellular phones Nearest neighbor searches Runtime

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Robust Real-Time 3D Modeling of Static Scenes Using Solely a Time-of-Flight Sensor

Robust Real-Time 3D Modeling of Static Scenes Using Solely a...

引用

ieee computer society conference on computer vision and pattern recognition Workshops

作者： Johannes Feulner Jochen Penne Eva Kollorz Joachim Hornegger Chair of Pattern Recognition Friedrich-Alexander-University

ISBN: (纸本)9781424439942

An algorithm is proposed for the 3D modeling of static scenes solely based on the range and intensity data acquired by a Time-of-Flight camera during an arbitrary movement. No additional scene acquisition devices, like inertia sensor, positioning robots or intensity based cameras are incorporated. The current pose is estimated by maximizing the uncentered correlation coefficient between edges detected in the current and a preceding frame at a minimum frame rate of four fps and an average accuracy of 45mm. The paper also describes several extensions for robust registration like multiresolution hierarchies and projection Iterative Closest Point algorithm. The basic registration algorithm and its extensions were intensively evaluated against ground truth data to validate the accuracy, robustness and real-time-capability.

关键词： 3D modeling 3D reconstruction ToF camera Time-of-Flight

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Supervised Learning of Edges and Object Boundaries

Supervised Learning of Edges and Object Boundaries

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conference on computer vision and pattern recognition (cvpr)

作者： P. Dollar Zhuowen Tu S. Belongie Computer Science & Engineering University of California San Diego USA Lab of Neuro Imaging University of California Los Angeles USA

Edge detection is one of the most studied problems in computer vision, yet it remains a very challenging task. It is difficult since often the decision for an edge cannot be made purely based on low level cues such as gradient, instead we need to engage all levels of information, low, middle, and high, in order to decide where to put edges. In this paper we propose a novel supervised learning algorithm for edge and object boundary detection which we refer to as Boosted Edge Learning or BEL for short. A decision of an edge point is made independently at each location in the image; a very large aperture is used providing significant context for each decision. In the learning stage, the algorithm selects and combines a large number of features across different scales in order to learn a discriminative model using an extended version of the Probabilistic Boosting Tree classification algorithm. The learning based framework is highly adaptive and there are no parameters to tune. We show applications for edge detection in a number of specific image domains as well as on natural images. We test on various datasets including the Berkeley dataset and the results obtained are very good.

关键词： Supervised learning Image edge detection Object detection computer science computer vision Apertures Boosting Application software Detectors Neuroimaging

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Edge reinforcement using parametrized relaxation labeling

Edge reinforcement using parametrized relaxation labeling

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conference on computer vision and pattern recognition (cvpr)

作者： J.S. Duncan T. Birkholzer Departments of Diagnostic Radiology Yale University New Heaven CT USA Departments of Electrical Engineering Yale University New Heaven CT USA

The problem of reinforcing local evidence of edges while suppressing unwanted information in noisy images is considered using a form of relaxation labeling. The methodology is based on parameterizing a continuous set of edge orientation labels using a single vector. A sigmoidal thresholding function similar to that used in artificial neural networks to bias neighborhood-influence and insure convergence to meaningful stable states is also utilized. A global optimization function is defined, and a decentralized parallel algorithm is derived that uses a steepest-gradient-descent approach to arrive at the optimal point on the functional surface, corresponding to desirable edge-reinforced and noise-suppressed labelings. In addition, a modification to the functional is presented which incorporates a thinning operation to insure that each edge is marked by only a single-pixel-wide response. Results from several image data sets indicate that the algorithm performs as well as or better than other relaxation labeling methods, and with improved computational efficiency.< >

关键词： Labeling Prototypes Algorithm design and analysis Radiology Computed tomography Artificial neural networks Convergence Parallel algorithms Computational efficiency computer vision

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A self-correcting projector

A self-correcting projector

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conference on computer vision and pattern recognition (cvpr)

作者： R. Raskar P. Beardsley Mitsubishi Electric Research Laboratories Cambridge MA USA

ISBN: (纸本)0769512720

We describe a calibration and rendering technique for a projector that can render rectangular images under keystoned position. The projector utilizes a rigidly attached camera to form a stereo pair. We describe a very easy to use technique for calibration of the projector-camera pair using only black planar surfaces. We present an efficient rendering method to pre-warp images so that they appear correctly on the screen, and show experimental results.

关键词： Cameras Calibration Rendering (computer graphics) Prototypes Displays Laboratories Stereo vision computer vision Computational geometry Lenses

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