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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013"

共 656 条记录，以下是401-410 订阅

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Closing the Loop in Scene Interpretation

Closing the Loop in Scene Interpretation

引用

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

作者： Derek Hoiem Alexei A. Efros Martial Hebert Beckman Institute University of Illinois USA Robotics Institute Carnegie Mellon University USA

Image understanding involves analyzing many different aspects of the scene. In this paper, we are concerned with how these tasks can be combined in a way that improves the performance of each of them. Inspired by Barrow and Tenenbaum, we present a flexible framework for interfacing scene analysis processes using intrinsic images. Each intrinsic image is a registered map describing one characteristic of the scene. We apply this framework to develop an integrated 3D scene understanding system with estimates of surface orientations, occlusion boundaries, objects, camera viewpoint, and relative depth. Our experiments on a set of 300 outdoor images demonstrate that these tasks reinforce each other, and we illustrate a coherent scene understanding with automatically reconstructed 3D models.

关键词： Layout Image analysis Surface reconstruction Feedforward systems Object detection Inference algorithms Robot kinematics Cameras Object recognition Reflectivity

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IM2GPS: estimating geographic information from a single image

IM2GPS: estimating geographic information from a single imag...

引用

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

作者： James Hays Alexei A. Efros Carnegie Mellon University USA

Estimating geographic information from an image is an excellent, difficult high-level computer vision problem whose time has come. the emergence of vast amounts of geographically-calibrated image data is a great reason for computer vision to start looking globally - on the scale of the entire planet! In this paper, we propose a simple algorithm for estimating a distribution over geographic locations from a single image using a purely data-driven scene matching approach. For this task, we will leverage a dataset of over 6 million GPS-tagged images from the Internet. We represent the estimated image location as a probability distribution over the Earth's surface. We quantitatively evaluate our approach in several geolocation tasks and demonstrate encouraging performance (up to 30 times better than chance). We show that geolocation estimates can provide the basis for numerous other image understanding tasks such as population density estimation, land cover estimation or urban/rural classification.

关键词： computer vision Layout Earth Surface topography Humans Probability distribution Sea surface Global Positioning System Planets Internet

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Granularity and Elasticity Adaptation in Visual Tracking

Granularity and Elasticity Adaptation in Visual Tracking

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

作者： Ming Yang Ying Wu Department of EECS Northwestern University Evanston IL USA

the observation models in tracking algorithms are critical to both tracking performance and applicable scenarios but are often simplified to focus on fixed level of certain target properties such as appearances and structures. In this paper, we propose a unified tracking paradigm in which targets are represented by Markov random fields of interest regions and introduce a new way to adapt observation models by automatically tuning the feature granularity and model elasticity, i.e. the abstraction level of features and the model's degree of flexibility to tolerate deformations. Specifically, we employ a multi-scale scheme to extract features from interest regions and adjust the parameters of the potential functions of the MRF model to maximize the likelihoods of tracking results. Experiments demonstrate the method can estimate translation, scaling and rotation and deal with deformation, partial occlusions, and camouflage objects within this unified framework.

关键词： Elasticity Target tracking Deformable models Solid modeling Feature extraction Markov random fields Statistics Humans Motion analysis pattern matching

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Improved Building Detection by Gaussian Processes Classification via Feature Space Rescale and Spectral Kernel Selection

Improved Building Detection by Gaussian Processes Classifica...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.3

作者： Hang Zhou David Suter Department Elec & Computer System Engineering Monash University Clayton VIC Australia

We use spectral analysis to facilitate Gaussian processes (GP) classification. Our solution provides two improvements: scaling of the data to achieve a more isotropic nature, as well as a method to choose the kernel to match certain data characteristics. Given the dataset, from the Fourier transform of the training data we compare the frequency domain features of each dimension to estimate a rescaling (towards making the data isotropic). Also, the spectrum of the training data is compared with several candidate kernel spectrums. From this comparison the best matching kernel is chosen. In these ways, the training data matches better the GP classification kernel function (and hence the underlying assumed correlation characteristics), resulting in a better GP classification result. Test results on both non image and image data show the efficiency and effectiveness of our approach.

关键词： Gaussian processes Kernel Training data Anisotropic magnetoresistance Frequency domain analysis Spectral analysis Australia Fourier transforms Frequency estimation Testing

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A Framework for Reducing Ink-Bleed in Old Documents

A Framework for Reducing Ink-Bleed in Old Documents

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

作者： Yi Huang Michael S. Brown Dong Xu Nanyang Technological University Singapore Singapore SG National University of Singapore School of Computing Republic of Singapore Nanyang Technological University School of Computer Engineering Republic of Singapore

We describe a novel application framework to reduce the effects of ink-bleed in old documents. this task is treated as a classification problem where training-data is used to compute per-pixel likelihoods for use in a dual-layer Markov Random Field (MRF) that simultaneously labels image pixels of the front and back of a document as either foreground, background, or ink-bleed, while maintaining the integrity of foreground strokes. Our approach obtains better results than previous work without the need for assumptions about ink-bleed intensities or extensive parameter tuning. Our overall framework is detailed, including front and back image alignment, training-data collection, and the MRF formulation with associated likelihoods and intra- and interlayer cost computations.

关键词： Ink Principal component analysis Pixel Computational efficiency Chemicals Application software Markov random fields Aging Humidity Interference

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Efficient Photometric Stereo on Glossy Surfaces with Wide Specular Lobes

Efficient Photometric Stereo on Glossy Surfaces with Wide Sp...

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

作者： Hin-Shun Chung Jiaya Jia Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong China

this paper presents a new photometric stereo method aiming to efficiently estimate BRDF and reconstruct glossy surfaces. Rough specular surfaces exhibit wide specular lobes under different lightings. they are ubiquitous and usually bring difficulties to both specular pixel removal and surface normal recovery. In our approach, we do not apply unreliable highlight separation and specularity estimation. Instead, an important visual cue, i.e. the cast shadow silhouette of the object, is employed to optimally recover global BRDF parameters. these parameter estimates are then taken into a reflectance model for robustly computing the surface normals and other local parameters using an iterative optimization. Within the unified framework, our method can also be used to reconstruct object surfaces assembled with multiple materials.

关键词： Photometry Surface reconstruction Rough surfaces Surface roughness Reflectivity Image reconstruction Brain modeling Robustness Optical reflection Stereo image processing

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Languages and Compilers for Parallel Computing 1

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Călin Cașcaval Pablo Montesinos

ISBN: (数字)9783319099675

ISBN: (纸本)9783319099668

this book constitutes the thoroughly refereed post-conference proceedings of the 26th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2013, held in Tokyo, Japan, in September 2012. the 20 revised full papers and two keynote papers presented were carefully reviewed and selected from 44 submissions. the focus of the papers is on following topics: parallel programming models, compiler analysis techniques, parallel data structures and parallel execution models, to GPGPU and other heterogeneous execution models, code generation for power efficiency on mobile platforms, and debugging and fault tolerance for parallel systems.

关键词： computer Imaging, vision, pattern recognition and Graphics

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A Statistical Deformation Prior for Non-Rigid Image and Shape Registration

A Statistical Deformation Prior for Non-Rigid Image and Shap...

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

作者： thomas Albrecht Marcel Luthi thomas Vetter Computer Science Department University of Basel Basel Switzerland

Non-rigid registration is central to many problems in computer vision and medical image analysis. We propose a registration algorithm which is regularized by prior knowledge in the form of a statistical deformation model. this model is obtained from previous registrations performed on a set of noise-free training examples given by images, or shapes represented by level set functions. Contrary to similar approaches, our method does not strictly constrain the result to lie in the span of the statistical model but rather uses the model for Tikhonov regularization. therefore, our method can be used to reduce the influence of noise and artifacts even when the model contains only a few typical examples. this automatically gives rise to a bootstrapping strategy for building statistical models from noisy data sets requiring only a limited number of high quality examples. We demonstrate the effectiveness of the approach on synthetic and medical images.

关键词： Shape Deformable models Image registration Biomedical imaging computer vision Image analysis Noise shaping Image segmentation computer science Noise level

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Shape Priors in Variational Image Segmentation: Convexity, Lipschitz Continuity and Globally Optimal Solutions

Shape Priors in Variational Image Segmentation: Convexity, L...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.3

作者： Daniel Cremers Frank R. Schmidt Frank Barthel Department of Computer Science University of Bonn Germany

In this work, we introduce a novel implicit representation of shape which is based on assigning to each pixel a probability that this pixel is inside the shape. this probabilistic representation of shape resolves two important drawbacks of alternative implicit shape representations such as the level set method: Firstly, the space of shapes is convex in the sense that arbitrary convex combinations of a set of shapes again correspond to a valid shape. Secondly, we prove that the introduction of shape priors into variational image segmentation leads to functionals which are convex with respect to shape deformations. For a large class of commonly considered (spatially continuous) functionals, we prove that - under mild regularity assumptions - segmentation and tracking with statistical shape priors can be performed in a globally optimal manner. In experiments on tracking a walking person through a cluttered scene we demonstrate the advantage of global versus local optimality.

关键词： Shape Image segmentation Level set Pixel Legged locomotion Probability Cost function computer science Layout Background noise

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Semi-Supervised Boosting using Visual Similarity Learning

Semi-Supervised Boosting using Visual Similarity Learning

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

作者： Christian Leistner Helmut Grabner Horst Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria

the required amount of labeled training data for object detection and classification is a major drawback of current methods. Combining labeled and unlabeled data via semi-supervised learning holds the promise to ease the tedious and time consuming labeling effort. this paper presents a novel semi-supervised learning method which combines the power of learned similarity functions and classifiers. the approach capable of exploiting both labeled and unlabeled data is formulated in a boosting framework. One classifier (the learned similarity) serves as a prior which is steadily improved via training a second classifier on labeled and unlabeled samples. We demonstrate the approach on challenging computer vision applications. First, we show how we can train a classifier using only a few labeled samples and many unlabeled data. Second, we improve (specialize) a state-of-the-art detector by using labeled and unlabeled data.

关键词： Boosting Semisupervised learning computer vision Detectors Training data Application software Machine learning Object detection Support vector machines Face detection

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