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

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Global Image Registration Based on Learning the Prior Appearance Model

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

引用

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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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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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...

引用

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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Volumetric Reconstruction from Multi-Energy Single-View Radiography

Volumetric Reconstruction from Multi-Energy Single-View Radi...

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

作者： Sang N. Le Mei Kay Lee Shamima Banu Anthony C. Fang Department of Computer Science School of Computing National University of Singapore Singapore Physical Education and Sports Sciences National Institute of Education Nanyang Technological University Singapore

We address the volumetric reconstruction problem that takes as input a series of orthographic multi-energy X-ray images, producing as output a reconstructed model space consisting of uniform-size mass density voxels. Our approach solves the non-linear constrained optimization formulation problem by constructing a compliant estimate of volumetric distribution, subject to projective and domain constraints, and minimizes variational irregularities. To resolve the inherent ambiguities of single-view formulation, an optional shape model may be introduced to aid the reconstruction process. We demonstrate our method's practical usage as a new in-vivo method for estimating three-dimensional body segmental compositions, and compare its results with those of existing methods.

关键词： Radiography Image reconstruction Image segmentation Magnetic resonance imaging Bones X-ray imaging Costs Minerals Layout Biomedical measurements

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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...

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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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Real-Time Face Pose Estimation from Single Range Images

Real-Time Face Pose Estimation from Single Range Images

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

作者： Michael D. Breitenstein Daniel Kuettel thibaut Weise Luc van Gool Hanspeter Pfister Computer Vision Laboratory ETH Zurich Switzerland School of Engineering and Applied Sciences Harvard University Cambridge MA USA

We present a real-time algorithm to estimate the 3D pose of a previously unseen face from a single range image. Based on a novel shape signature to identify noses in range images, we generate candidates for their positions, and then generate and evaluate many pose hypotheses in parallel using modern graphics processing units (GPUs). We developed a novel error function that compares the input range image to precomputed pose images of an average face model. the algorithm is robust to large pose variations of ±90° yaw, ±45° pitch and ±30° roll rotation, facial expression, partial occlusion, and works for multiple faces in the field of view. It correctly estimates 97.8% of the poses within yaw and pitch error of 15° at 55.8 fps. To evaluate the algorithm, we built a database of range images with large pose variations and developed a method for automatic ground truth annotation.

关键词： Face detection Robustness Nose Graphics Runtime computer vision Laboratories Shape Image generation Error correction

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On Controlling Light Transport in Poor Visibility Environments

On Controlling Light Transport in Poor Visibility Environmen...

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

作者： Mohit Gupta Srinivasa G. Narasimhan Yoav Y. Schechner School of Computer Sc. Carnegie Mellon University Pittsburgh USA Department of Electrical Engineering Technion-Israel Institute of Technology Haifa Israel

Poor visibility conditions due to murky water, bad weather, dust and smoke severely impede the performance of vision systems. Passive methods have been used to restore scene contrast under moderate visibility by digital post-processing. However, these methods are ineffective when the quality of acquired images is poor to begin with. In this work, we design active lighting and sensing systems for controlling light transport before image formation, and hence obtain higher quality data. First, we present a technique of polarized light striping based on combining polarization imaging and structured light striping. We show that this technique out-performs different existing illumination and sensing methodologies. Second, we present a numerical approach for computing the optimal relative sensor-source position, which results in the best quality image. Our analysis accounts for the limits imposed by sensor noise.

关键词： Lighting control Layout Backscatter Optical polarization Control systems Cameras computer vision Image restoration Image quality Light scattering

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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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