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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301-310 订阅

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An Implicit Spatiotemporal Shape Model for Human Activity Localization and recognition

An Implicit Spatiotemporal Shape Model for Human Activity Lo...

引用

ieee-computer-Society conference on computer vision and pattern recognition Workshops

作者： Oikonomopoulos, A. Patras, I. Pantic, M. Univ London Imperial Coll Sci Technol & Med Dept Comp London England Queen Mary Univ London Dept Elect Engn London England

ISBN: (纸本)9781424439942

In this paper we address the problem of localisation and recognition of human activities in unsegmented image sequences. the main contribution of the proposed method is the use of an implicit representation of the spatiotemporal shape of the activity which relies on the spatiotemporal localization of characteristic, sparse, 'visual words' and 'visual verbs'. Evidence for the spatiotemporal localization of the activity are accumulated in a probabilistic spatiotemporal voting scheme. the local nature of our voting framework allows us to recover multiple activities that take place in the same scene, as well as activities in the presence of clutter and occlusions. We construct class-specific codebooks using the descriptors in the training set, where we take the spatial co-occurrences of pairs of codewords into account. the positions of the codeword pairs with respect to the object centre, as well as the frame in the training set in which they occur are subsequently stored in order to create a spatiotemporal model of codeword co-occurrences. During the testing phase, we use Mean Shift Mode estimation in order to spatially segment the subject that performs the activities in every frame, and the Radon transform in order to extract the most probable hypotheses concerning the temporal segmentation of the activities within the continuous stream.

关键词： computer vision

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A robust identification approach to gait recognition

A robust identification approach to gait recognition

引用

26th ieee conference on computer vision and pattern recognition, cvpr

作者： Tao, Ding Pennsylvania State University University Park PA 16802 United States

ISBN: (纸本)9781424422432

In this paper we address the problem of human gait recognition from a robust identification and model (in)validation prospective. the main idea is to apply dimensionality reduction technique to extract the spatio-temporal information by mapping the gait silhouette sequence to a low dimensional time sequence, which is considered as the output of a linear time invariant (LTI) system. A class of gaits is associated to a nominal discrete LTI system which has a periodic impulse response and is identified by robust identification approach. Correspondingly, gait recognition can be formulated as measuring the difference between the models representing different gait sequences. Our approach provides an efficient way to extract, to model shape-motion information of gait sequence, and to measure the difference between gait sequence models which is robust to gait cycle localization, gross appearance variation, and time scaling. these results are illustrated with practical examples on popular gait databases. ©2008 ieee.

关键词： Impulse response

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A NURBS-based spectral reflectance descriptor with applications in computer vision and pattern recognition

A NURBS-based spectral reflectance descriptor with applicati...

引用

26th ieee conference on computer vision and pattern recognition, cvpr

作者： Cong, Phuoc Huynh Robles-Kelly, Antonio RSISE Bldg. 115 Australian National University Canberra ACT 0200 Australia Locked Bag 8001 Canberra ACT 2601 Australia

ISBN: (纸本)9781424422432

In this paper, we present a surface reflectance descriptor based on the control points resulting from the interpolation of Non-Uniform Rational B-Spline (NURBS) curves to multispectral reflectance data. the interpolation is based upon a knot removal scheme in the parameter domain. thus, we exploit the local support of NURBS so as to recover a compact descriptor robust to noise and local perturbation of the spectra. We demonstrate the utility of our NURBS-based descriptor for material identification. To this end, we perform skin spectra recognition making use of a Support Vector Machine classifier. We also provide results on hyperspectral imagery and elaborate on the preprocessing step for skin segmentation. We compare our results with those obtained using an alternative descriptor. ©2008 ieee.

关键词： Interpolation

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Hybrid body representation for integrated pose recognition, localization and segmentation

Hybrid body representation for integrated pose recognition, ...

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26th ieee conference on computer vision and pattern recognition, cvpr

作者： Chen, G. Fan, Guoliang School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74078 United States

ISBN: (纸本)9781424422432

We propose a hybrid body representation that represents each typical pose by both template-like view information and part-based structural information. Specifically, each body part as well as the whole body are represented by an off-line learned shape model where both region-based and edge-based priors are combined in a coupled shape representation. Part-based spatial priors are represented by a "star" graphical model. this hybrid body representation can synergistically integrate pose recognition, localization and segmentation into one computational flow. Moreover, as an important step for feature extraction and model inference, segmentation is involved in the low-level, mid-level and high-level vision stages, where top-down prior knowledge and bottom-up data processing is well integrated via the proposed hybrid body representation. ©2008 ieee.

关键词： Gesture recognition

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Discriminative human action segmentation and recognition using semi-markov model

Discriminative human action segmentation and recognition usi...

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26th ieee conference on computer vision and pattern recognition, cvpr

作者： Shi, Qinfeng Wang, Li Cheng, Li Smola, Alex NICTA Australian National University Canberra ACT Australia Southeast University Nanjing China

ISBN: (纸本)9781424422432

Given an input video sequence of one person conducting a sequence of continuous actions, we consider the problem of jointly segmenting and recognizing actions. We propose a discriminative approach to this problem under a semi-Markov model framework, where we are able to define a set of features over input-output space that captures the characteristics on boundary frames, action segments and neighboring action segments, respectively. In addition, we show that this method can also be used to recognize the person who performs in this video sequence. A Viterbi-like algorithm is devised to help efficiently solve the induced optimization problem. Experiments on a variety of datasets demonstrate the effectiveness of the proposed method. ©2008 ieee.

关键词： Video recording

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Radiometric calibration with illumination change for outdoor scene analysis

Radiometric calibration with illumination change for outdoor...

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26th ieee conference on computer vision and pattern recognition, cvpr

作者： Seon, Joo Kim Frahm, Jan-Michael Pollefeys, Marc Department of Computer Science University of North Carolina Chapel Hill NC United States Department of Computer Science ETH Zurich

ISBN: (纸本)9781424422432

the images of an outdoor scene collected over time are valuable in studying the scene appearance variation which can lead to novel applications and help enhance existing methods that were constrained to controlled environments. However, the images do not reflect the true appearance of the scene in many cases due to the radiometric properties of the camera : the radiometric response function and the changing exposure. We introduce a new algorithm to compute the radiometric response function and the exposure of images given a sequence of images of a static outdoor scene where the illumination is changing. We use groups of pixels with constant behaviors towards the illumination change for the response estimation and introduce a sinusoidal lighting variation model representing the daily motion of the sun to compute the exposures. ©2008 ieee.

关键词： Image enhancement

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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

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

ISBN: (纸本)9781424422432

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 nonlinear 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. ©2008 ieee.

关键词： Biochemistry

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Video segmentation: Propagation, validation and aggregation of a preceding graph

Video segmentation: Propagation, validation and aggregation ...

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26th ieee conference on computer vision and pattern recognition, cvpr

作者： Liu, Siying Dong, Guo Chye, Hwang Yan Sim, Heng Ong National University of Singapore Electrical and Computer Engineering 4 Engineering Drive 3 Singapore 117576 Singapore DSO National Laboratories 20 Science Park Drive Singapore 118230 Singapore

ISBN: (纸本)9781424422432

In this work, video segmentation is viewed as an efficient intra-frame grouping temporally reinforced by a strong inter-frame coherence. Traditional approaches simply regard pixel motions as another prior in the MRF-MAP framework. Since pixel pre-grouping is inefficiently performed on every frame, the strong correlation between inter-frame groupings is largely underutilized. We exploit the inter-frame correlation to propagate trustworthy groupings from the previous frame. A preceding graph is constructed and labeled for the previous frame. It is temporally propagated to the current frame and validated by similarity measures. All unlabeled subgraphs are spatially aggregated for the final grouping. Experimental results show that the proposed approach is highly efficient for spatio-temporal segmentation. It makes good use of temporal correlation and produces satisfactory grouping results. ©2008 ieee.

关键词： Pixels

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Image segmentation via convolution of a level-set function with a Rigaut Kernel

Image segmentation via convolution of a level-set function w...

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26th ieee conference on computer vision and pattern recognition, cvpr

作者： Subakan, Özlem N. Vemuri, Baba C. Department of Computer and Information Science and Engineering University of Florida P.O. Box 116120 Gainesville FL 32611 United States

ISBN: (纸本)9781424422432

Image segmentation is a fundamental task in computer vision and there are numerous algorithms that have been successfully applied in various domains. there are still plenty of challenges to be met with. In this paper, we consider one such challenge, that of achieving segmentation while preserving complicated and detailed features present in the image, be it a gray level or a textured image. We present a novel approach that does not make use of any prior information about the objects in the image being segmented. Segmentation is achieved using local orientation information, which is obtained via the application of a steerable Gabor filter bank, in a statistical framework. this information is used to construct a spatially varying kernel called the Rigaut Kernel, which is then convolved with the signed distance function of an evolving contour (placed in the image) to achieve segmentation. We present numerous experimental results on real images, including a quantitative evaluation. Superior performance of our technique is depicted via comparison to the state-of-the-art algorithms in literature. ©2008 ieee.

关键词： Image segmentation

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On the use of independent tasks for face recognition

On the use of independent tasks for face recognition

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26th ieee conference on computer vision and pattern recognition, cvpr

作者： Lapedriza, Àgata Masip, David Vitrià, Jordi Campus UAB Barcelona Spain Universitat Oberta de Catalunya Rambla del Poblenou 156 Barcelona Spain Universiat de Barcelona Gran Via de les Corts Catalanes 585 Barcelona Spain

ISBN: (纸本)9781424422432

We present a method for learning discriminative linear feature extraction using independent tasks. More concretely, given a target classification task, we consider a complementary classification task that is independent of the target one. For example, in face classification field, subject recognition can be a target task while facial expression classification can be a complementary task. then, we use labels of the complementary task in order to obtain a more robust feature extraction, being the new feature space less sensitive to the complementary classification. To learn the proposed feature extraction we use the mutual information measure between the projected data and both labels from the target and the complementary tasks. In our experiments, this framework has been applied to a face recognition problem, in order to inhibit this classification task from environmental artifacts, and to mitigate the effects of the small sample size problem. Our classification experiments show an improved feature extraction process using the proposed method. ©2008 ieee.

关键词： Face recognition

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