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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1011-1020 订阅

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"You're it!": Role Identification using Pairwise Interactions in Tag Games

"You're it!": Role Identification using Pairwise Interaction...

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

作者： Moreno, Alejandro Poppe, Ronald Univ Twente NL-7500 AE Enschede Netherlands

ISBN: (纸本)9780769549903

We aim at designing interactive playgrounds that automatically analyze the behavior of children while playing games, in order to adapt the gameplay and make the games more engaging. In this paper, we focus on recognizing roles in tag games, where children are taggers or runners. We start by tracking the location and motion of individual players, and subsequently recognize pairwise interactions: approach, chase and avoid. At each moment in time, we inspect the full set of pairwise interactions to determine the role of each player. Our approach is fully probabilistic, deals with any number of players and can easily be extended to include other interactions and roles. We evaluate our algorithm using simulations, which show promising results. We intend to extend our framework to recognize variants of the tag game, and to address actual play interactions.

关键词： behavioural sciences computer games interactive systems object recognition object tracking probability

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Robust Estimation of Nonrigid Transformation for Point Set Registration

Robust Estimation of Nonrigid Transformation for Point Set R...

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

作者： Ma, Jiayi Zhao, Ji Tian, Jinwen Tu, Zhuowen Yuille, Alan L. Huazhong Univ Sci & Technol Wuhan Peoples R China Univ Calif Los Angeles Dept Stat Los Angeles CA USA CMU Inst Robot Pittsburgh PA USA Univ Calif Los Angeles Lab Neuro Imaging Los Angeles CA 90024 USA

ISBN: (纸本)9780769549897

We present a new point matching algorithm for robust nonrigid registration. the method iteratively recovers the point correspondence and estimates the transformation between two point sets. In the first step of the iteration, feature descriptors such as shape context are used to establish rough correspondence. In the second step, we estimate the transformation using a robust estimator called L2E. this is the main novelty of our approach and it enables us to deal with the noise and outliers which arise in the correspondence step. the transformation is specified in a functional space, more specifically a reproducing kernel Hilbert space. We apply our method to nonrigid sparse image feature correspondence on 2D images and 3D surfaces. Our results quantitatively show that our approach outperforms state-of-the-art methods, particularly when there are a large number of outliers. Moreover, our method of robustly estimating transformations from correspondences is general and has many other applications.

关键词： Hilbert spaces image matching image registration shape recognition

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Multi-resolution Shape Analysis via Non-Euclidean Wavelets: Applications to Mesh Segmentation and Surface Alignment Problems

Multi-resolution Shape Analysis via Non-Euclidean Wavelets: ...

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

作者： Kim, Won Hwa Chung, Moo K. Singh, Vikas Univ Wisconsin Dept Comp Sci 1210 W Dayton St Madison WI 53706 USA Univ Wisconsin Dept Biostat & Med Informat Madison WI USA

ISBN: (纸本)9780769549897

the analysis of 3-D shape meshes is a fundamental problem in computer vision, graphics, and medical imaging. Frequently, the needs of the application require that our analysis take a multi-resolution view of the shape's local and global topology, and that the solution is consistent across multiple scales. Unfortunately, the preferred mathematical construct which offers this behavior in classical image/signal processing, Wavelets, is no longer applicable in this general setting (data with non-uniform topology). In particular, the traditional definition does not allow writing out an expansion for graphs that do not correspond to the uniformly sampled lattice (e.g., images). In this paper, we adapt recent results in harmonic analysis, to derive Non-Euclidean Wavelets based algorithms for a range of shape analysis problems in vision and medical imaging. We show how descriptors derived from the dual domain representation offer native multi-resolution behavior for characterizing local/global topology around vertices. With only minor modifications, the framework yields a method for extracting interest/key points from shapes, a surprisingly simple algorithm for 3-D shape segmentation (competitive with state of the art), and a method for surface alignment (without landmarks). We give an extensive set of comparison results on a large shape segmentation benchmark and derive a uniqueness theorem for the surface alignment problem.

关键词： computer vision feature extraction image representation image resolution image segmentation mesh generation wavelet transforms

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

作者： Aoyama, Shoichiro Ito, Koichi Aoki, Takafumi Tohoku Univ Grad Sch Informat Sci Aoba Ku Sendai Miyagi 9808579 Japan

ISBN: (纸本)9780769549903

In the field of biometric recognition, similarity measure using local features such as Gabor-based coding, Local Binary patterns (LBP) and Scale Invariant Feature Transform (SIFT) has been applied to various biometric recognition problems. these features, however, may not always exhibit higher recognition performance than the recognition algorithms of the specific biometric trait. In this paper, we propose a novel similarity measurement technique using local phase features for biometric recognition. the phase information obtained from 2D Discrete Fourier Transform (DFT) of images exhibits good performance for evaluating the similarity between images. the local phase features extracted from multi-scale image pyramids can handle nonlinear deformation of images. through a set of experiments in some biometric recognition such as face, palmprint and finger knuckle recognition, we demonstrate the efficient performance and versatility of the proposed features compared with the state-of-the-art conventional algorithms.

关键词： Gabor filters discrete Fourier transforms face recognition feature extraction palmprint recognition

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Exploiting Unlabeled Ages for Aging pattern Analysis on A Large Database

Exploiting Unlabeled Ages for Aging Pattern Analysis on A La...

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

作者： Zhang, Chao Guo, Guodong W Virginia Univ Lane Dept CSEE Morgantown WV 26506 USA

ISBN: (纸本)9780769549903

"Big Data" analysis is an emerging topic in computer vision and pattern recognition. As one example problem of big data, we study semantic age labels and facial aging pattern analysis on a large database. In aging analysis, one of the great challenges is the lack of a large number of face images with ground truth age labels. Unlike many other example-based recognition problems where human annotations can be used as the ground truth labels for both training and testing, it is quite difficult to label the exact ages in face images by human annotators. An alternative is to exploit the unlabeled ages to enhance the age estimation performance. However, it is unclear whether the face images with unlabeled ages can be used or not for age estimation, and how to use the unlabeled data. In this paper, we study the two problems comprehensively under two paradigms: the semi-supervised learning and unsupervised learning for aging pattern analysis. We emphasize the importance of using ground truth age labels and a large database in order to derive a meaningful measure in the context of big data. Our study can make an impact on collecting aging patterns that is very expensive and time consuming in practice.

关键词： computer vision data analysis face recognition unsupervised learning visual databases

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the Power is in Your Hands: 3D Analysis of Hand Gestures in Naturalistic Video

The Power is in Your Hands: 3D Analysis of Hand Gestures in ...

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

作者： Ohn-Bar, Eshed Trivedi, Mohan M. Univ Calif San Diego Comp Vis & Robot Res Lab La Jolla CA 92093 USA

ISBN: (纸本)9780769549903

We study natural human activity under difficult settings of cluttered background, volatile illumination, and frequent occlusion. To that end, a two-stage method for hand and hand-object interaction detection is developed. First, activity proposals are generated from multiple sub-regions in the scene. then, these are integrated using a second-stage classifier. We study a set of descriptors for detection and activity recognition in terms of performance and speed. With the overarching goal of reducing 'lab setting bias', a case study is introduced with a publicly available annotated RGB and depth dataset. the dataset was captured using a Kinect under real-world driving settings. the approach is motivated by studying actions-as well as semantic elements in the scene and the driver's interaction with them-which may be used to infer driver inattentiveness. the proposed framework significantly outperforms a state-of-the-art baseline on our dataset for hand detection.

关键词： gesture recognition image classification interactive devices object detection video signal processing

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An Augmented Linear Discriminant Analysis Approach for Identifying Identical Twins with the Aid of Facial Asymmetry Features

An Augmented Linear Discriminant Analysis Approach for Ident...

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

作者： Juefei-Xu, Felix Savvides, Marios Carnegie Mellon Univ Dept Elect & Comp Engn CyLab Biometr Ctr Pittsburgh PA 15213 USA

ISBN: (纸本)9780769549903

In this work, we have proposed an Augmented Linear Discriminant Analysis (ALDA) approach to identify identical twins. It learns a common subspace that not only can identify from which family the individual comes, but also can distinguish between individuals within the same family. We evaluate the ALDA against the traditional LDA approach for subspace learning on the Notre Dame twin database. We have shown that the proposed ALDA method with the aid of facial asymmetry features significantly outperforms other well-established facial descriptors (LBP, LTP, LTrP), and the ALDA subspace method does a much better job in distinguishing identical twins than LDA. We are able to achieve 48.50% VR at 0.1% FAR for identifying family membership of identical twin individuals in the crowd and an averaged 82.58% VR at 0.1% FAR for verifying identical twin individuals within the same family, a significant improvement over traditional descriptors and traditional LDA method.

关键词： face recognition feature extraction learning (artificial intelligence)

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Quality Assessment for Fingerprints Collected by Smartphone Cameras

Quality Assessment for Fingerprints Collected by Smartphone ...

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

作者： Li, Guoqiang Yang, Bian Olsen, Martin Aastrup Busch, Christoph Gjovik Univ Coll Norwegian Informat Secur Lab Gjovik Norway

ISBN: (纸本)9780769549903

We propose an approach to assess the quality of fingerprint samples captured by smartphone cameras under real-life scenarios. Our approach extracts a set of quality features for image blocks. Without needing segmentation, the approach determines a sample's quality by checking all image blocks divided from the sample and for each block a trained support vector machine gives a binary indication - "high-quality" or "non-high-quality" (including the low quality case and the background block case). A quality score is then generated for the whole sample. Experiments show this approach performs well in identifying the high quality blocks - the Spearman correlation coefficient between the proposed quality scores and samples' normalized comparison scores (ground truth) reaches 0.53 while the rate of false detection (background blocks judged as high-quality ones) is still low as 4.63 percent over a challenging dataset collected under various real-life scenarios.

关键词： correlation methods feature extraction fingerprint identification image sensors object recognition smart phones support vector machines

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Computationally Efficient Face Spoofing Detection with Motion Magnification

Computationally Efficient Face Spoofing Detection with Motio...

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

作者： Bharadwaj, Samarth Dhamecha, Tejas I. Vatsa, Mayank Singh, Richa IIIT Delhi Delhi India

ISBN: (纸本)9780769549903

For a robust face biometric system, a reliable anti-spoofing approach must be deployed to circumvent the print and replay attacks. Several techniques have been proposed to counter face spoofing, however a robust solution that is computationally efficient is still unavailable. this paper presents a new approach for spoofing detection in face videos using motion magnification. Eulerian motion magnification approach is used to enhance the facial expressions commonly exhibited by subjects in a captured video. Next, two types of feature extraction algorithms are proposed: (i) a configuration of LBP that provides improved performance compared to other computationally expensive texture based approaches and (ii) motion estimation approach using HOOF descriptor. On the Print Attack and Replay Attack spoofing datasets, the proposed framework improves the state-of-art performance;especially HOOF descriptor yielding a near perfect half total error rate of 0% and 1.25% respectively.

关键词： biometrics (access control) computer crime face recognition feature extraction image enhancement image texture motion estimation video databases video signal processing

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Visible-Spectrum Gaze Tracking for Sports

Visible-Spectrum Gaze Tracking for Sports

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

作者： Pires, Bernardo R. Hwangbo, Myung Devyver, Michael Kanade, Takeo Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9780769549903

In sports, wearable gaze tracking devices can enrich the viewer experience and be a powerful training tool. Because devices can be used for long periods of time, often outside, it is desirable that they do not use active illumination (infra-red light sources) for safety reasons and to minimize the interference of the sun. Unlike traditional wearable devices, in sports, the gaze tracking method must be robust to (often dramatic) movements of the user in relation to the device (i.e. the common assumption that, because the device is wearable, the eye does not move with regards to the camera no longer holds.) this paper extends a visible-spectrum gaze tracker in the literature to handle the requirements of a motor-sports application. Specifically, the method presented removes the original assumption (in the original method) that the eye position is fixed, and proposes the use of template matching to allow for changes in the eye location from frame to frame. Experimental results demonstrate that the proposed method can handle severe changes in the eye location and is very fast to compute (up to 60 frames per second in modern hardware.)

关键词： eye image matching interference iris recognition object tracking safety sport

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