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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71-80 订阅

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Sampling Strategies for Real-time Action recognition

Sampling Strategies for Real-time Action Recognition

引用

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

作者： Shi, Feng Petriu, Emil Laganiere, Robert Univ Ottawa Sch Elect Engn & Comp Scinece Ottawa ON Canada

ISBN: (纸本)9780769549897

Local spatio-temporal features and bag-of-features representations have become popular for action recognition. A recent trend is to use dense sampling for better performance. While many methods claimed to use dense feature sets, most of them are just denser than approaches based on sparse interest point detectors. In this paper, we explore sampling with high density on action recognition. We also investigate the impact of random sampling over dense grid for computational efficiency. We present a real-time action recognition system which integrates fast random sampling method with local spatio-temporal features extracted from a Local Part Model. A new method based on histogram intersection kernel is proposed to combine multiple channels of different descriptors. Our technique shows high accuracy on the simple Kth dataset, and achieves state-of-the-art on two very challenging real-world datasets, namely, 93% on Kth, 83.3% on UCF50 and 47.6% on HMDB51.

关键词： feature extraction image recognition image representation image sampling

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Evaluation of Color STIPs for Human Action recognition

Evaluation of Color STIPs for Human Action Recognition

引用

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

作者： Everts, Ivo van Gemert, Jan C. Gevers, theo Univ Amsterdam Intelligent Syst Lab Amsterdam NL-1012 WX Amsterdam Netherlands

ISBN: (纸本)9780769549897

this paper is concerned with recognizing realistic human actions in videos based on spatio-temporal interest points (STIPs). Existing STIP-based action recognition approaches operate on intensity representations of the image data. Because of this, these approaches are sensitive to disturbing photometric phenomena such as highlights and shadows. Moreover, valuable information is neglected by discarding chromaticity from the photometric representation. these issues are addressed by Color STIPs. Color STIPs are multi-channel reformulations of existing intensity-based STIP detectors and descriptors, for which we consider a number of chromatic representations derived from the opponent color space. this enhanced modeling of appearance improves the quality of subsequent STIP detection and description. Color STIPs are shown to substantially outperform their intensity-based counterparts on the challenging UCF sports, UCF11 and UCF50 action recognition benchmarks. Moreover, the results show that color STIPs are currently the single best low-level feature choice for STIP-based approaches to human action recognition.

关键词： image colour analysis image motion analysis image representation video signal processing

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Illumination Estimation based on Bilayer Sparse Coding

Illumination Estimation based on Bilayer Sparse Coding

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

作者： Li, Bing Xiong, Weihua Hu, Weiming Peng, Houwen Chinese Acad Sci Inst Automat NLPR Beijing 100190 Peoples R China

ISBN: (纸本)9780769549897

Computational color constancy is a very important topic in computer vision and has attracted many researchers' attention. Recently, lots of research has shown the effects of using high level visual content cues for improving illumination estimation. However, nearly all the existing methods are essentially combinational strategies in which image's content analysis is only used to guide the combination or selection from a variety of individual illumination estimation methods. In this paper, we propose a novel bilayer sparse coding model for illumination estimation that considers image similarity in terms of both low level color distribution and high level image scene content simultaneously. For the purpose, the image's scene content information is integrated with its color distribution to obtain optimal illumination estimation model. the experimental results on real-world image sets show that our algorithm is superior to some prevailing illumination estimation methods, even better than some combinational methods.

关键词： computer vision image coding image colour analysis

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Whitened Expectation Propagation: Non-Lambertian Shape from Shading and Shadow

Whitened Expectation Propagation: Non-Lambertian Shape from ...

引用

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

作者： Potetz, Brian Hajiarbabi, Mohammadreza Google Inc Mountain View CA 94043 USA Univ Kansas Lawrence KS 66045 USA

ISBN: (纸本)9780769549897

For problems over continuous random variables, MRFs with large cliques pose a challenge in probabilistic inference. Difficulties in performing optimization efficiently have limited the probabilistic models explored in computer vision and other fields. One inference technique that handles large cliques well is Expectation Propagation. EP offers run times independent of clique size, which instead depend only on the rank, or intrinsic dimensionality, of potentials. this property would be highly advantageous in computer vision. Unfortunately, for grid-shaped models common in vision, traditional Gaussian EP requires quadratic space and cubic time in the number of pixels. Here, we propose a variation of EP that exploits regularities in natural scene statistics to achieve run times that are linear in both number of pixels and clique size. We test these methods on shape from shading, and we demonstrate strong performance not only for Lambertian surfaces, but also on arbitrary surface reflectance and lighting arrangements, which requires highly non-Gaussian potentials. Finally, we use large, non-local cliques to exploit cast shadow, which is traditionally ignored in shape from shading.

关键词： Gaussian processes computer vision image resolution inference mechanisms learning (artificial intelligence)

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Recognizing Activities via Bag of Words for Attribute Dynamics

Recognizing Activities via Bag of Words for Attribute Dynami...

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

作者： Li, Weixin Yu, Qian Sawhney, Harpreet Vasconcelos, Nuno Univ Calif San Diego La Jolla CA 92093 USA SRI Int Sarnoff Princeton NJ 08540 USA

ISBN: (纸本)9780769549897

In this work, we propose a novel video representation for activity recognition that models video dynamics with attributes of activities. A video sequence is decomposed into short-term segments, which are characterized by the dynamics of their attributes. these segments are modeled by a dictionary of attribute dynamics templates, which are implemented by a recently introduced generative model, the binary dynamic system (BDS). We propose methods for learning a dictionary of BDSs from a training corpus, and for quantizing attribute sequences extracted from videos into these BDS codewords. this procedure produces a representation of the video as a histogram of BDS codewords, which is denoted the bag-of-words for attribute dynamics (BoWAD). An extensive experimental evaluation reveals that this representation outperforms other state-of-the-art approaches in temporal structure modeling for complex activity recognition.

关键词： image motion analysis image representation object recognition video signal processing

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Sparse Quantization for Patch Description

Sparse Quantization for Patch Description

引用

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

作者： Boix, Xavier Gygli, Michael Roig, Gemma Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9780769549897

the representation of local image patches is crucial for the good performance and efficiency of many vision tasks. Patch descriptors have been designed to generalize towards diverse variations, depending on the application, as well as the desired compromise between accuracy and efficiency. We present a novel formulation of patch description, that serves such issues well. Sparse quantization lies at its heart. this allows for efficient encodings, leading to powerful, novel binary descriptors, yet also to the generalization of existing descriptors like SIFT or BRIEF. We demonstrate the capabilities of our formulation for both keypoint matching and image classification. Our binary descriptors achieve state-of-the-art results for two keypoint matching benchmarks, namely those by Brown [6] and Mikolajczyk [18]. For image classification, we propose new descriptors that perform similar to SIFT on Caltech101 [10] and PASCAL VOC07 [9].

关键词： compressed sensing computer vision image classification image coding image representation

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Supervised Descent Method and its Applications to Face Alignment

Supervised Descent Method and its Applications to Face Align...

引用

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

作者： Xiong, Xuehan De la Torre, Fernando Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA

ISBN: (纸本)9780769549897

Many computer vision problems (e. g., camera calibration, image alignment, structure from motion) are solved through a nonlinear optimization method. It is generally accepted that 2nd order descent methods are the most robust, fast and reliable approaches for nonlinear optimization of a general smooth function. However, in the context of computer vision, 2nd order descent methods have two main drawbacks: (1) the function might not be analytically differentiable and numerical approximations are impractical. (2) the Hessian might be large and not positive definite. To address these issues, this paper proposes a Supervised Descent Method (SDM) for minimizing a Non-linear Least Squares (NLS) function. During training, the SDM learns a sequence of descent directions that minimizes the mean of NLS functions sampled at different points. In testing, SDM minimizes the NLS objective using the learned descent directions without computing the Jacobian nor the Hessian. We illustrate the benefits of our approach in synthetic and real examples, and show how SDM achieves state-of-the-art performance in the problem of facial feature detection. the code is available at ***/intraface.

关键词： computer vision face recognition feature extraction gradient methods learning (artificial intelligence) least squares approximations nonlinear programming

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Automatic Signer Diarization - the Mover is the Signer Approach

Automatic Signer Diarization - the Mover is the Signer Appro...

引用

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

作者： Gebre, Binyam Gebrekidan Wittenburg, Peter Heskes, Tom Max Planck Inst Psycholinguist Nijmegen Netherlands Radboud Univ Nijmegen Nijmegen Netherlands

ISBN: (纸本)9780769549903

We present a vision-based method for signer diarization - the task of automatically determining "who signed when?" in a video. this task has similar motivations and applications as speaker diarization but has received little attention in the literature. In this paper, we motivate the problem and propose a method for solving it. the method is based on the hypothesis that signers make more movements than their interlocutors. Experiments on four videos (a total of 1.4 hours and each consisting of two signers) show the applicability of the method. the best diarization error rate (DER) obtained is 0.16.

关键词： computer vision gesture recognition image motion analysis object recognition video signal processing

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A Mobile vision System for Fast and Accurate Ellipse Detection

A Mobile Vision System for Fast and Accurate Ellipse Detecti...

引用

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

作者： Fornaciari, Michele Cucchiara, Rita Prati, Andrea Univ Modena & Reggio Emilia Via Vignolese 905 Modena Italy Univ IUAV Venice I-1957 Venice Italy

ISBN: (纸本)9780769549903

Several papers addressed ellipse detection as a first step for several computer vision applications, but most of the proposed solutions are too slow to be applied in real time on large images or with limited hardware resources, as in the case of mobile devices. this demo is based on a novel algorithm for fast and accurate ellipse detection. the proposed algorithm relies on a careful selection of arcs which are candidate to form ellipses and on the use of Hough transform to estimate parameters in a decomposed space. the demo will show it working on a commercial smart-phone.

关键词： Hough transforms computer vision smart phones

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Optimized Product Quantization for Approximate Nearest Neighbor Search

Optimized Product Quantization for Approximate Nearest Neigh...

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

作者： Ge, Tiezheng He, Kaiming Ke, Qifa Sun, Jian Univ Sci & Technol China Hefei Peoples R China Microsoft Res Asia Beijing Peoples R China Microsoft Research Silicon Valley Redmond WA USA

ISBN: (纸本)9780769549897

Product quantization is an effective vector quantization approach to compactly encode high-dimensional vectors for fast approximate nearest neighbor (ANN) search. the essence of product quantization is to decompose the original high-dimensional space into the Cartesian product of a finite number of low-dimensional subspaces that are then quantized separately. Optimal space decomposition is important for the performance of ANN search, but still remains unaddressed. In this paper, we optimize product quantization by minimizing quantization distortions w.r.t. the space decomposition and the quantization codebooks. We present two novel methods for optimization: a non-parametric method that alternatively solves two smaller sub-problems, and a parametric method that is guaranteed to achieve the optimal solution if the input data follows some Gaussian distribution. We show by experiments that our optimized approach substantially improves the accuracy of product quantization for ANN search.

关键词： Gaussian distribution computer vision optimisation search problems vector quantisation

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