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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411-420 订阅

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Improving Local Learning for Object Categorization by Exploring the Effects of Ranking

Improving Local Learning for Object Categorization by Explor...

引用

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

作者： Tien-Lung Chang Tyng-Luh Liu Jen-Hui Chuang Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Chiao Tung University Hsinchu Taiwan

Local learning for classification is useful in dealing with various vision problems. One key factor for such approaches to be effective is to find good neighbors for the learning procedure. In this work, we describe a novel method to rank neighbors by learning a local distance function, and meanwhile to derive the local distance function by focusing on the high-ranked neighbors. the two aspects of considerations can be elegantly coupled through a well-defined objective function, motivated by a supervised ranking method called P-Norm Push. While the local distance functions are learned independently, they can be reshaped altogether so that their values can be directly compared. We apply the proposed method to the Caltech-101 dataset, and demonstrate the use of proper neighbors can improve the performance of classification techniques based on nearest-neighbor selection.

关键词： Nearest neighbor searches Information science computer science Supervised learning Application software computer vision Neural networks Organizing Training data Euclidean distance

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Learning on Lie Groups for Invariant Detection and Tracking

Learning on Lie Groups for Invariant Detection and Tracking

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

作者： Oncel Tuzel Fatih Porikli Peter Meer Mitsubishi Electric Research Laboratories Inc. Cambridge MA USA Rutgers University Piscataway NJ USA CS ECE Rutgers University Piscataway NJ USA

this paper presents a novel learning based tracking model combined with object detection. the existing techniques proceed by linearizing the motion, which makes an implicit Euclidean space assumption. Most of the trans-formations used in computer vision have matrix Lie group structure. We learn the motion model on the Lie algebra and show that the formulation minimizes a first order approximation to the geodesic error. the learning model is extended to train a class specific tracking function, which is then integrated to an existing pose dependent object detector to build a pose invariant object detection algorithm. the proposed model can accurately detect objects in various poses, where the size of the search space is only a fraction compared to the existing object detection methods. the detection rate of the original detector is improved by more than 90% for large transformations.

关键词： Object detection Motion estimation Target tracking Algebra Detectors computer vision Least squares approximation computer errors Nonlinear optics Image motion analysis

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Global Image Registration Based on Learning the Prior Appearance Model

Global Image Registration Based on Learning the Prior Appear...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Ayman El-Baz Georgy Gimel'farb Bioimaging Laboratory Department of Bioengineering University of Louisville Louisville KY USA Department of Computer Science Tamaki Campus University of Auckland Auckland New Zealand

A new approach to align an image of a textured object with a given prototype (learned reference object) is proposed. Visual appearance of the images, after equalizing their signals, is modeled with a Markov-Gibbs random field with pairwise interaction. Similarity to the prototype (learned reference object) is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs derived automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by gradient search. To get accurate appearance model, we developed a new approach to automatically select the most important cliques (neighborhood system) that describe the visual appearance of a texture object. Experiments confirm that our approach aligns complex objects better than popular conventional algorithms.

关键词： Image registration Prototypes Statistics Biomedical imaging Energy measurement Remote monitoring Water resources Roads Lighting Biomedical engineering

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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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Combining Brain computer Interfaces with vision for Object Categorization

Combining Brain Computer Interfaces with Vision for Object C...

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

作者： Ashish Kapoor Pradeep Shenoy Desney Tan Microsoft Research Limited Redmond USA University of Washington Seattle USA

Human-aided computing proposes using information measured directly from the human brain in order to perform useful tasks. In this paper, we extend this idea by fusing computer vision-based processing and processing done by the human brain in order to build more effective object categorization systems. Specifically, we use an electroen-cephalograph (EEG) device to measure the subconscious cognitive processing that occurs in the brain as users see images, even when they are not trying to explicitly classify them. We present a novel framework that combines a discriminative visual category recognition system based on the Pyramid Match Kernel (PMK) with information derived from EEG measurements as users view images. We propose a fast convex kernel alignment algorithm to effectively combine the two sources of information. Our approach is validated with experiments using real-world data, where we show significant gains in classification accuracy. We analyze the properties of this information fusion method by examining the relative contributions of the two modalities, the errors arising from each source, and the stability of the combination in repeated experiments.

关键词： Brain computer interfaces Humans Electroencephalography Kernel Performance evaluation computer vision Image recognition Information resources Information analysis Stability analysis

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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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3D Pose Refinement from Reflections

3D Pose Refinement from Reflections

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

作者： Pascal Lagger Mathieu Salzmann Vincent Lepetit Pascal Fua Computer Vision Laboratory EPF Lausanne Switzerland

We demonstrate how to exploit reflections for accurate registration of shiny objects: the lighting environment can be retrieved from the reflections under a distant illumination assumption. Since it remains unchanged when the camera or the object of interest moves, this provides powerful additional constraints that can be incorporated into standard pose estimation algorithms. the key idea and main contribution of the paper is therefore to show that the registration should also be performed in the lighting environment space, instead of in the image space only. this lets us recover very accurate pose estimates because the specularities are very sensitive to pose changes. An interesting side result is an accurate estimate of the lighting environment. Furthermore, since the mapping from lighting environment to specularities has no analytical expression for objects represented as 3D meshes, and is not 1-to-1, registering lighting environments is far from trivial. However we propose a general and effective solution. Our approach is demonstrated on both synthetic and real images.

关键词： Optical reflection Cameras Shape computer vision Lighting Information retrieval Robustness Statistics Light sources Lamps

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Segmentation by transduction

Segmentation by transduction

引用

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

作者： Olivier Duchenne Jean-Yves Audibert Renaud Keriven Jean Ponce Florent Segonne Willow-ENS/INRIA Paris France CERTIS-Ecole des Ponts Willow-ENS/INRIA France CERTIS-Ecole des Ponts France Willow-ENS/INRIA France

this paper addresses the problem of segmenting an image into regions consistent with user-supplied seeds (e.g., a sparse set of broad brush strokes). We view this task as a statistical transductive inference, in which some pixels are already associated with given zones and the remaining ones need to be classified. Our method relies on the Laplacian graph regularizer, a powerful manifold learning tool that is based on the estimation of variants of the Laplace-Beltrami operator and is tightly related to diffusion processes. Segmentation is modeled as the task of finding matting coefficients for unclassified pixels given known matting coefficients for seed pixels. the proposed algorithm essentially relies on a high margin assumption in the space of pixel characteristics. It is simple, fast, and accurate, as demonstrated by qualitative results on natural images and a quantitative comparison with state-of-the-art methods on the Microsoft GrabCut segmentation database.

关键词： Image segmentation Application software Object recognition Laplace equations Biomedical imaging Diffusion processes Image databases Partitioning algorithms computer vision Anatomical structure

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View and Scale Invariant Action recognition Using Multiview Shape-Flow Models

View and Scale Invariant Action Recognition Using Multiview ...

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

作者： Pradeep Natarajan Ramakant Nevatia Institute for Robotics and Intelligent Systems University of Southern California Los Angeles CA USA

Actions in real world applications typically take place in cluttered environments with large variations in the orientation and scale of the actor. We present an approach to simultaneously track and recognize known actions that is robust to such variations, starting from a person detection in the standing pose. In our approach we first render synthetic poses from multiple viewpoints using Mocap data for known actions and represent them in a Conditional Random Field (CRF) whose observation potentials are computed using shape similarity and the transition potentials are computed using optical flow. We enhance these basic potentials with terms to represent spatial and temporal constraints and call our enhanced model the Shape, Flow, Duration-Conditional Random Field (SFD-CRF). We find the best sequence of actions using Viterbi search in the SFD-CRF. We demonstrate our approach on videos from multiple viewpoints and in the presence of background clutter.

关键词： Optical computing Image motion analysis Humans pattern recognition Image recognition Hidden Markov models Shape Videos Intelligent robots Robustness

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