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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41-50 订阅

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In Defense of Sparsity Based Face recognition

In Defense of Sparsity Based Face Recognition

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Deng, Weihong Hu, Jiani Guo, Jun Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China

ISBN: (纸本)9780769549897

the success of sparse representation based classification (SRC) has largely boosted the research of sparsity based face recognition in recent years. A prevailing view is that the sparsity based face recognition performs well only when the training images have been carefully controlled and the number of samples per class is sufficiently large. this paper challenges the prevailing view by proposing a "prototype plus variation" representation model for sparsity based face recognition. Based on the new model, a Superposed SRC (SSRC), in which the dictionary is assembled by the class centroids and the sample-to-centroid differences, leads to a substantial improvement on SRC. the experiments results on AR, FERET and FRGC databases validate that, if the proposed prototype plus variation representation model is applied, sparse coding plays a crucial role in face recognition, and performs well even when the dictionary bases are collected under uncontrolled conditions and only a single sample per classes is available.

关键词： face recognition image classification image coding image representation matrix algebra

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Optical Flow Estimation using Laplacian Mesh Energy

Optical Flow Estimation using Laplacian Mesh Energy

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Li, Wenbin Cosker, Darren Brown, Matthew Tang, Rui Univ Bath Dept Comp Sci MTRC Bath BA2 7AY Avon England

ISBN: (纸本)9780769549897

In this paper we present a novel non-rigid optical flow algorithm for dense image correspondence and non-rigid registration. the algorithm uses a unique Laplacian Mesh Energy term to encourage local smoothness whilst simultaneously preserving non-rigid deformation. Laplacian deformation approaches have become popular in graphics research as they enable mesh deformations to preserve local surface shape. In this work we propose a novel Laplacian Mesh Energy formula to ensure such sensible local deformations between image pairs. We express this wholly within the optical flow optimization, and show its application in a novel coarse-to-fine pyramidal approach. Our algorithm achieves the state-of-the-art performance in all trials on the Garg et al. dataset, and top tier performance on the Middlebury evaluation.

关键词： computer graphics computer vision image registration image sequences mesh generation shape recognition

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Procrustean Normal Distribution for Non-Rigid Structure from Motion

Procrustean Normal Distribution for Non-Rigid Structure from...

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Lee, Minsik Cho, Jungchan Choi, Chong-Ho Oh, Songhwai Seoul Natl Univ ASRI Dept EECS Seoul 151742 South Korea

ISBN: (纸本)9780769549897

Non-rigid structure from motion is a fundamental problem in computer vision, which is yet to be solved satisfactorily. the main difficulty of the problem lies in choosing the right constraints for the solution. In this paper, we propose new constraints that are more effective for non-rigid shape recovery. Unlike the other proposals which have mainly focused on restricting the deformation space using rank constraints, our proposal constrains the motion parameters so that the 3D shapes are most closely aligned to each other, which makes the rank constraints unnecessary. Based on these constraints, we define a new class of probability distribution called the Procrustean normal distribution and propose a new NRSfM algorithm, EM-PND. the experimental results show that the proposed method outperforms the existing methods, and it works well even if there is no temporal dependence between the observed samples.

关键词： computer vision deformation image motion analysis normal distribution

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Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation

Subspace Interpolation via Dictionary Learning for Unsupervi...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ni, Jie Qiu, Qiang Chellappa, Rama Univ Maryland Ctr Automat Res College Pk MD 20742 USA

ISBN: (纸本)9780769549897

Domain adaptation addresses the problem where data instances of a source domain have different distributions from that of a target domain, which occurs frequently in many real life scenarios. this work focuses on unsupervised domain adaptation, where labeled data are only available in the source domain. We propose to interpolate subspaces through dictionary learning to link the source and target domains. these subspaces are able to capture the intrinsic domain shift and form a shared feature representation for cross domain recognition. Further we introduce a quantitative measure to characterize the shift between two domains, which enables us to select the optimal domain to adapt to the given multiple source domains. We present experiments on face recognition across pose, illumination and blur variations, cross dataset object recognition, and report improved performance over the state of the art.

关键词： face recognition interpolation object recognition unsupervised learning

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Sensing and Recognizing Surface Textures Using a GelSight Sensor

Sensing and Recognizing Surface Textures Using a GelSight Se...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Li, Rui Adelson, Edward H. MIT Cambridge MA 02139 USA

ISBN: (纸本)9780769549897

Sensing surface textures by touch is a valuable capability for robots. Until recently it was difficult to build a compliant sensor with high sensitivity and high resolution. the GelSight sensor is compliant and offers sensitivity and resolution exceeding that of the human fingertips. this opens the possibility of measuring and recognizing highly detailed surface textures. the GelSight sensor, when pressed against a surface, delivers a height map. this can be treated as an image, and processed using the tools of visual texture analysis. We have devised a simple yet effective texture recognition system based on local binary patterns, and enhanced it by the use of a multi-scale pyramid and a Hellinger distance metric. We built a database with 40 classes of tactile textures using materials such as fabric, wood, and sandpaper. Our system can correctly categorize materials from this database with high accuracy. this suggests that the GelSight sensor can be useful for material recognition by robots.

关键词： image recognition image sensors image texture robot vision

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SCaLE: Supervised and Cascaded Laplacian Eigenmaps for Visual Object recognition Based on Nearest Neighbors

SCaLE: Supervised and Cascaded Laplacian Eigenmaps for Visua...

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Wu, Ruobing Yu, Yizhou Wang, Wenping Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9780769549897

Recognizing the category of a visual object remains a challenging computer vision problem. In this paper we develop a novel deep learning method that facilitates example-based visual object category recognition. Our deep learning architecture consists of multiple stacked layers and computes an intermediate representation that can be fed to a nearest-neighbor classifier. this intermediate representation is discriminative and structure-preserving. It is also capable of extracting essential characteristics shared by objects in the same category while filtering out nonessential differences among them. Each layer in our model is a nonlinear mapping, whose parameters are learned through two sequential steps that are designed to achieve the aforementioned properties. the first step computes a discrete mapping called supervised Laplacian Eigenmap. the second step computes a continuous mapping from the discrete version through nonlinear regression. We have extensively tested our method and it achieves state-of-the-art recognition rates on a number of benchmark datasets.

关键词： computer vision eigenvalues and eigenfunctions image recognition learning (artificial intelligence) regression analysis

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Boundary Cues for 3D Object Shape Recovery

Boundary Cues for 3D Object Shape Recovery

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Karsch, Kevin Liao, Zicheng Rock, Jason Barron, Jonathan T. Hoiem, Derek Univ Illinois Urbana IL 61801 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9780769549897

Early work in computer vision considered a host of geometric cues for both shape reconstruction [11] and recognition [14]. However, since then, the vision community has focused heavily on shading cues for reconstruction [1], and moved towards data-driven approaches for recognition [6]. In this paper, we reconsider these perhaps overlooked "boundary" cues (such as self occlusions and folds in a surface), as well as many other established constraints for shape reconstruction. In a variety of user studies and quantitative tasks, we evaluate how well these cues inform shape reconstruction (relative to each other) in terms of both shape quality and shape recognition. Our findings suggest many new directions for future research in shape reconstruction, such as automatic boundary cue detection and relaxing assumptions in shape from shading (e.g. orthographic projection, Lambertian surfaces).

关键词： edge detection image reconstruction shape recognition solid modelling

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Photometric Ambient Occlusion

Photometric Ambient Occlusion

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Hauagge, Daniel Wehrwein, Scott Bala, Kavita Snavely, Noah Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9780769549897

We present a method for computing ambient occlusion (AO) for a stack of images of a scene from a fixed viewpoint. Ambient occlusion, a concept common in computer graphics, characterizes the local visibility at a point: it approximates how much light can reach that point from different directions without getting blocked by other geometry. While AO has received surprisingly little attention in vision, we show that it can be approximated using simple, per-pixel statistics over image stacks, based on a simplified image formation model. We use our derived AO measure to compute reflectance and illumination for objects without relying on additional smoothness priors, and demonstrate state-of-the art performance on the MIT Intrinsic Images benchmark. We also demonstrate our method on several synthetic and real scenes, including 3D printed objects with known ground truth geometry.

关键词： computational geometry computer graphics lighting natural scenes

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Accurate Localization of 3D Objects from RGB-D Data using Segmentation Hypotheses

Accurate Localization of 3D Objects from RGB-D Data using Se...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Kim, Byung-soo Xu, Shili Savarese, Silvio Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9780769549897

In this paper we focus on the problem of detecting objects in 3D from RGB-D images. We propose a novel framework that explores the compatibility between segmentation hypotheses of the object in the image and the corresponding 3D map. Our framework allows to discover the optimal location of the object using a generalization of the structural latent SVM formulation in 3D as well as the definition of a new loss function defined over the 3D space in training. We evaluate our method using two existing RGB-D datasets. Extensive quantitative and qualitative experimental results show that our proposed approach outperforms state-of-the-art as methods well as a number of baseline approaches for both 3D and 2D object recognition tasks.

关键词： image colour analysis image segmentation object detection object recognition stereo image processing support vector machines

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Light Field Distortion Feature for Transparent Object recognition

Light Field Distortion Feature for Transparent Object Recogn...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Maeno, Kazuki Nagahara, Hajime Shimada, Atsushi Taniguchi, Rin-ichiro Kyushu Univ Grad Sch Informat Sci & Elect Engn Nishi Ku Fukuoka 8190395 Japan

ISBN: (纸本)9780769549897

Current object-recognition algorithms use local features, such as scale-invariant feature transform (SIFT) and speeded-up robust features (SURF), for visually learning to recognize objects. these approaches though cannot apply to transparent objects made of glass or plastic, as such objects take on the visual features of background objects, and the appearance of such objects dramatically varies with changes in scene background. Indeed, in transmitting light, transparent objects have the unique characteristic of distorting the background by refraction. In this paper, we use a single-shot light field image as an input and model the distortion of the light field caused by the refractive property of a transparent object. We propose a new feature, called the light field distortion (LFD) feature, for identifying a transparent object. the proposal incorporates this LFD feature into the bag-of-features approach for recognizing transparent objects. We evaluated its performance in laboratory and real settings.

关键词： image classification object recognition refraction

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