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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是1331-1340 订阅

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Real-Time Pose Estimation of Articulated Objects using Low-Level Motion

Real-Time Pose Estimation of Articulated Objects using Low-L...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.5

作者： Ben Daubney David Gibson Neill Campbell Department of Computer Science University of Bristol UK

We present a method that is capable of tracking and estimating pose of articulated objects in real-time. this is achieved by using a bottom-up approach to detect instances of the object in each frame, these detections are then linked together using a high-level a priori motion model. Unlike other approaches that rely on appearance, our method is entirely dependent on motion;initial low-level part detection is based on how a region moves as opposed to its appearance. this work is best described as Pictorial Structures using motion. A sparse cloud of points extracted using a standard feature tracker are used as observational data, this data contains noise that is not Gaussian in nature but systematic due to tracking errors. Using a probabilistic framework we are able to overcome both corrupt and missing data whilst still inferring new poses from a generative model. Our approach requires no manual initialisation and we show results for a number of complex scenes and different classes of articulated object, this demonstrates both the robustness and versatility of the presented technique.

关键词： Motion estimation Motion detection Object detection Data mining Tracking Dynamic programming Belief propagation computer science Clouds Gaussian noise

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Who Killed the Directed Model?

Who Killed the Directed Model?

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Justin Domke Alap Karapurkar Yiannis Aloimonos Department of Computer Science University of Maryland USA

Prior distributions are useful for robust low-level vision, and undirected models (e.g. Markov Random Fields) have become a central tool for this purpose. though sometimes these priors can be specified by hand, this becomes difficult in large models, which has motivated learning these models from data. However, maximum likelihood learning of undirected models is extremely difficult- essentially all known methods require approximations and/or high computational cost. Conversely, directed models are essentially trivial to learn from data, but have not received much attention for low-level vision. We compare the two formalisms of directed and undirected models, and conclude that there is no a priori reason to believe one better represents low-level vision quantities. We formulate two simple directed priors, for natural images and stereo disparity, to empirically test if the undirected formalism is superior. We find in both cases that a simple directed model can achieve results similar to the best learnt undirected models with significant speedups in training time, suggesting that directed models are an attractive choice for tractable learning.

关键词： Maximum likelihood estimation Pixel Markov random fields computer science Robustness Computational efficiency Testing Uncertainty Object segmentation Image motion analysis

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A Fast Local Descriptor for Dense Matching

A Fast Local Descriptor for Dense Matching

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.8

作者： Engin Tola Vincent Lepetit Pascal Fua École Polytechnique Fédérale de Lausanne (EPFL) Computer Vision Laboratory Lausanne Switzerland École PolytechniqueFédérale de Lausanne (EPFL) Computer Vision Laboratory Lausanne Switzerland

We introduce a novel local image descriptor designed for dense wide-baseline matching purposes. We feed our descriptors to a graph-cuts based dense depth map estimation algorithm and this yields better wide-baseline performance than the commonly used correlation windows for which the size is hard to tune. As a result, unlike competing techniques that require many high-resolution images to produce good reconstructions, our descriptor can compute them from pairs of low-quality images such as the ones captured by video streams. Our descriptor is inspired from earlier ones such as SIFT and GLOH but can be computed much faster for our purposes. Unlike SURF which can also be computed efficiently at every pixel, it does not introduce artifacts that degrade the matching performance. Our approach was tested with ground truth laser scanned depth maps as well as on a wide variety of image pairs of different resolutions and we show that good reconstructions are achieved even with only two low quality images.

关键词： Image reconstruction Robustness Yield estimation Layout Pixel Histograms Computational efficiency computer vision Laboratories Feeds

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Filtering Internet Image Search Results Towards Keyword Based Category recognition

Filtering Internet Image Search Results Towards Keyword Base...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.7

作者： Kamil Wnuk Stefano Soatto Computer Science Department University of California Los Angeles CA USA

In this work we aim to capitalize on the availability of Internet image search engines to automatically create image training sets from user provided queries. this problem is particularly difficult due to the low precision of image search results. Unlike many existing dataset gathering approaches, we do not assume a category model based on a small subset of the noisy data or an ad-hoc validation set. Instead we use a nonparametric measure of strangeness [8] in the space of holistic image representations, and perform an iterative feature elimination algorithm to remove the most strange examples from the category. this is the equivalent of keeping only features that are found to be consistent with others in the class. We show that applying our method to image search data before training improves average recognition performance, and demonstrate that we obtain comparative precision and recall results to the current state of the art, all the while maintaining a significantly simpler approach. In the process we also extend the strangeness-based feature elimination algorithm to automatically select good threshold values and perform filtering of a single class when the background is given.

关键词： Information filtering Information filters Internet Image recognition Training data Search engines Filtering algorithms Testing Noise reduction computer science

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Robust Dual Motion Deblurring

Robust Dual Motion Deblurring

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Jia Chen Lu Yuan Chi-Keung Tang Long Quan Vision and Graphics Group Hong Kong University of Science and Technology Hong Kong China

this paper presents a robust algorithm to deblur two consecutively captured blurred photos from camera shaking. Previous dual motion deblurring algorithms succeeded in small and simple motion blur and are very sensitive to noise. We develop a robust feedback algorithm to perform iteratively kernel estimation and image deblurring. In kernel estimation, the stability and capability of the algorithm is greatly improved by incorporating a robust cost function and a set of kernel priors. the robust cost function serves to reject outliers and noise, while kernel priors, including sparseness and continuity, remove ambiguity and maintain kernel shape. In deblurring, we propose a novel and robust approach which takes two blurred images as input to infer the clear image. the deblurred image is then used as feedback to refine kernel estimation. Our method can successfully estimate large and complex motion blurs which cannot be handled by previous dual or single image motion deblurring algorithms. the results are shown to be significantly better than those of previous approaches.

关键词： Noise robustness Kernel Iterative algorithms Feedback Robust stability Cost function Cameras Image restoration Noise shaping Shape

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引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.2

作者： Sanja Fidler Marko Boben Ales Leonardis Faculty of Computer and Information Science University of Ljubljana Slovenia

this paper proposes a new concept in hierarchical representations that exploits features of different granularity and specificity coming from all layers of the hierarchy. the concept is realized within a cross-layered compositional representation learned from the visual data. We show how similarity connections among discrete labels within and across hierarchical layers can be established in order to produce a set of layer-independent shape-terminals, i.e. shapinals. We thus break the traditional notion of hierarchies and show how the category-specific layers can make use of all the necessary features stemming from all hierarchical layers. this, on the one hand, brings higher generalization into the representation, yet on the other hand, it also encodes the notion of scales directly into the hierarchy, thus enabling a multi-scale representation of object categories. By focusing on shape information only, the approach is tested on the Caltech 101 dataset demonstrating good performance in comparison with other state-of-the-art methods.

关键词： Shape Testing Feature extraction Hierarchical systems Data mining Information science Robustness Unsupervised learning Object detection Detectors

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Recovering Consistent Video Depth Maps via Bundle Optimization

Recovering Consistent Video Depth Maps via Bundle Optimizati...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.4

作者： Guofeng Zhang Jiaya Jia Tien-Tsin Wong Hujun Bao State Key Lab of CAD&CG University of Zhejiang China Chinese University of Hong Kong Hong Kong China

this paper presents a novel method for reconstructing high-quality video depth maps. A bundle optimization model is proposed to address the key issues, including image noise and occlusions, in stereo reconstruction. Our method not only uses the color constancy constraint, but also explicitly incorporates the geometric coherence constraint associating multiple frames in a video, thus can naturally maintain the temporal coherence of the recovered video depths without introducing over-smoothing artifact. To make the inference problem tractable, we introduce an iterative optimization scheme by first initializing disparity maps using segmentation prior and then refining the disparities by means of bundle optimization. Unlike previous work estimating complex visibility parameters, our approach implicitly models the probabilistic visibility in a statistical way. the effectiveness of our automatic method is demonstrated using challenging video examples.

关键词： Image reconstruction Video sequences Stereo vision Colored noise Coherence Image segmentation Cameras Lamps Roads Stereo image processing

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Modeling complex luminance variations for target tracking

Modeling complex luminance variations for target tracking

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.6

作者： Christophe Collewet Eric Marchand Rennes France

Lambert's model is widely used in low level computer vision algorithms such as matching, tracking or optical flow computation for example. However, it is well known that these algorithms often fail when they face complex luminance variations. therefore, we revise in this paper the underlying hypothesis of its temporal constancy and propose a new optical flow constraint. To do that, we use the Blinn-Phong reflection model to take into account that the scene may move with respect to the lighting and/or to the observer, and that specular highlights may occur. To validate in practice these analytical results, we consider the case where a camera is mounted on a robot end-effector with a lighting mounted on this camera and show experimental results of target tracking by visual servoing. Such an approach requires to analytically compute the luminance variations due to the observer motion which can be easily derived from our revised optical flow constraint. In addition, while the visual servoing classical approaches rely on geometric features, we present here a new method that directly relies on the luminance of all pixels in the image which does not require any tracking or matching process.

关键词： Target tracking Image motion analysis Optical computing computer vision Robot vision systems Cameras Visual servoing Face detection Optical reflection Layout

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Stereoscopic Inpainting: Joint Color and Depth Completion from Stereo Images

Stereoscopic Inpainting: Joint Color and Depth Completion fr...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.9

作者： Liang Wang Hailin Jin Ruigang Yang Minglun Gong Center for Visualization and Virtual Environments University of Kentucky USA Advanced Technology Laboratories Adobe Systems Inc. USA Computer Science Department Memorial University of Newfoundland Canada

We present a novel algorithm for simultaneous color and depth inpainting. the algorithm takes stereo images and estimated disparity maps as input and fills in missing color and depth information introduced by occlusions or object removal. We first complete the disparities for the occlusion regions using a segmentation-based approach. the completed disparities can be used to facilitate the user in labeling objects to be removed. Since part of the removed regions in one image is visible in the other, we mutually complete the two images through 3D warping. Finally, we complete the remaining unknown regions using a depth-assisted texture synthesis technique, which simultaneously fills in both color and depth. We demonstrate the effectiveness of the proposed algorithm on several challenging data sets.

关键词： Layout Filling Image segmentation Algorithm design and analysis Image sampling Visualization Virtual environment computer science Labeling Digital images

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Bayesian Color Constancy Revisited

Bayesian Color Constancy Revisited

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.11

作者： Peter Vincent Gehler Carsten Rother Andrew Blake Tom Minka Toby Sharp Max-Planck Institute Tubingen Germany Microsoft Research Cambridge Cambridge UK

Computational color constancy is the task of estimating the true reflectances of visible surfaces in an image. In this paper we follow a line of research that assumes uniform illumination of a scene, and that the principal step in estimating reflectances is the estimation of the scene illuminant. We review recent approaches to illuminant estimation, firstly those based on formulae for normalisation of the reflectance distribution in an image - so-called grey-world algorithms, and those based on a Bayesian formulation of image formation. In evaluating these previous approaches we introduce a new tool in the form of a database of 568 high-quality, indoor and outdoor images, accurately labelled with illuminant, and preserved in their raw form, free of correction or normalisation. this has enabled us to establish several properties experimentally. Firstly automatic selection of grey-world algorithms according to image properties is not nearly so effective as has been thought. Secondly, it is shown that Bayesian illuminant estimation is significantly improved by the improved accuracy of priors for illuminant and reflectance that are obtained from the new dataset.

关键词： Bayesian methods Layout Reflectivity Lighting Testing Cameras Image databases Object recognition Image resolution Yield estimation

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