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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1041-1050 订阅

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

作者： Krishnan, Ravikiran Sarkar, Sudeep Univ S Florida Dept Comp Sci & Engn Tampa FL 33620 USA

ISBN: (纸本)9780769549903

One of the dominant approaches to gesture recognition, especially when we have one or few samples per class, is to compute the time-warped distance between the two sequences and perform nearest-neighbor classification. In this work, we show that we get much better results if instead we consider the similarity of the pattern of frame-wise distances of these two sequences with a third (anchor) sequence from the modelbase. We refer to these distance pattern vectors as the warp vectors. If these warp vectors are similar, then so are the sequences;if not, they are dissimilar. At the algorithmic core we have two dynamic time warping processes, one to compute the warp vectors with the anchor sequences and the other to compare these warp vectors. We select the anchor sequence to be the one that minimizes the overall distance, i.e. the sequence with respect to which these two sequences are the most similar. We present results on a large dataset of 1500 RGBD sequences spanning 150 gesture classes, such as traffic signals, sign language, and every day actions, extracted from the ChaLearn Gesture Challenge dataset. We experimented with three different feature types: difference of frames, HOG and relational distributions. We found that there were improvements of 5%, 15%, and 7%, respectively, at 20% false alarm rate, over traditional two-sequence based time-warped distance.

关键词： gesture recognition image classification vectors

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Mobile Exergames - Burn Calories While Playing Games on a Smartphone

Mobile Exergames - Burn Calories While Playing Games on a Sm...

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

作者： Buddharaju, Pradeep Pamidi, Naga Siva Chandra Prasad Univ Houston Clear Lake Dept Comp Sci Houston TX 77058 USA

ISBN: (纸本)9780769549903

Exergames combine exercising with game play by requiring the users to perform some kind of physical activity (and exercise) in order to score points in the game. In this paper, we present a novel mobile exergaming framework, which requires the users to physically move and jump in order to score points in a game that is played on a smartphone. Our system uses a custom designed Exercising Pad (called ExerPad) in order to track the user's physical movement, and then automatically updates the corresponding game character's position on the screen. the ExerPad contains different shaped images, which are captured from the smartphone's inbuilt camera, and are automatically detected by our shape detection algorithm. We also use the smartphone's inbuilt accelerometer and gyroscope to detect other physical movements from the user such as jumping, turning etc. the experimental results show that the proposed mobile exergames helps its users to burn calories and have fun at the same time.

关键词： accelerometers cameras computer games gyroscopes health care mobile computing object detection object tracking smart phones

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Using 3D Models to Recognize 2D Faces in the Wild

Using 3D Models to Recognize 2D Faces in the Wild

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

作者： Masi, Iacopo Lisanti, Giuseppe Bagdanov, Andrew D. Pala, Pietro Del Bimbo, Alberto Univ Florence MICC I-50134 Florence Italy

ISBN: (纸本)9780769549903

In this paper we consider the problem of face recognition in imagery captured in uncooperative environments using PTZ cameras. For each subject enrolled in the gallery, we acquire a high-resolution 3D model from which we generate a series of rendered face images of varying viewpoint. the result of regularly sampling face pose for all subjects is a redundant basis that over represents each target. To recognize an unknown probe image, we perform a sparse reconstruction of SIFT features extracted from the probe using a basis of SIFT features from the gallery. While directly collecting images over varying pose for all enrolled subjects is prohibitive at enrollment, the use of high speed, 3D acquisition systems allows our face recognition system to quickly acquire a single model, and generate synthetic views offline. Finally we show, using two publicly available datasets, how our approach performs when using rendered gallery images to recognize 2D rendered probe images and 2D probe images acquired using PTZ cameras.

关键词： data acquisition face recognition feature extraction image reconstruction image resolution image sampling pose estimation rendering (computer graphics) solid modelling

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Efficient GPU-based Graph Cuts for Stereo Matching

Efficient GPU-based Graph Cuts for Stereo Matching

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

作者： Choi, Young-kyu Park, In Kyu Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA Inha Univ Sch Informat & Commun Engn Incheon South Korea

ISBN: (纸本)9780769549903

Although graph cuts (GC) is popularly used in many computer vision problems, slow execution time due to its high complexity hinders wide usage. Manycore solution using Graphics Processing Unit (GPU) may solve this problem. However, conventional GC implementation does not fully exploit GPU's computing power. To address this issue, a new GC algorithm which is suitable for GPU environment is presented in this paper. First, we present a novel graph construction method that accelerates the convergence speed of GC. Next, a repetitive block-based push and relabel method is used to increase the data transfer efficiency. Finally, we propose a low-overhead global relabeling algorithm to increase the GPU occupancy ratio. the experiments on Middlebury stereo dataset shows that 5.2X speedup can be achieved over the baseline implementation, with identical GPU platform and parameters.

关键词： computer vision graph theory graphics processing units image matching stereo image processing

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A multi-sensor traffic scene dataset with omnidirectional video

A multi-sensor traffic scene dataset with omnidirectional vi...

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

作者： Koschorrek, Philipp Piccini, Tommaso Oberg, Per Felsberg, Michael Nielsen, Lars Mester, Rudolf Linkoping Univ Dept Elect Engn Comp Vis Lab S-58183 Linkoping Sweden Linkoping Univ Dept Elect Engn Vehicular Syst Linkoping 58183 Sweden

ISBN: (纸本)9780769549903

the development of vehicles that perceive their environment, in particular those using computer vision, indispensably requires large databases of sensor recordings obtained from real cars driven in realistic traffic situations. these datasets should be time shaped for enabling synchronization of sensor data from different sources. Furthermore, full surround environment perception requires high frame rates of synchronized omnidirectional video data to prevent information loss at any speeds. this paper describes an experimental setup and software environment for recording such synchronized multi-sensor data streams and storing them in a new open source format. the dataset consists of sequences recorded in various environments from a car equipped with an omnidirectional multi-camera, height sensors, an IMU, a velocity sensor, and a GPS. the software environment for reading these data sets will be provided to the public, together with a collection of long multi-sensor and multi-camera data streams stored in the developed format.

关键词： computer vision public domain software software engineering traffic information systems very large databases video signal processing

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the CASIA NIR-VIS 2.0 Face Database

The CASIA NIR-VIS 2.0 Face Database

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

作者： Li, Stan Z. Yi, Dong Lei, Zhen Liao, Shengcai Chinese Acad Sci CASIA Ctr Biometr & Secur Res Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China

ISBN: (纸本)9780769549903

In recent years, heterogeneous face biometrics has attracted more attentions in the face recognition community. After published in 2009, the HFB database [7] has been applied by tens of research groups and widely used for Near infrared vs. Visible light (NIR-VIS) face recognition. Despite its success the HFB database has two disadvantages: a limited number of subjects, lacking specific evaluation protocols. To address these issues we collected the NIR-VIS 2.0 database. It contains 725 subjects, imaged by VIS and NIR cameras in four recording sessions. Because the 3D modality in the HFB database was less used in the literature, we don't consider it in the current version. In this paper, we describe the composition of the database, evaluation protocols and present the baseline performance of PCA on the database. Moreover, two interesting tricks, the facial symmetry and heterogeneous component analysis (HCA) are also introduced to improve the performance.

关键词： cameras face recognition infrared imaging principal component analysis visual databases

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Bio-inspired Dynamic 3D Discriminative Skeletal Features for Human Action recognition

Bio-inspired Dynamic 3D Discriminative Skeletal Features for...

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

作者： Chaudhry, Rizwan Ofli, Ferda Kurillo, Gregorij Bajcsy, Ruzena Vidal, Rene Johns Hopkins Univ Ctr Imaging Sci Baltimore MD 21218 USA Univ Calif Berkeley Tele Immers Lah Berkeley CA 94720 USA

ISBN: (纸本)9780769549903

Over the last few years, with the immense popularity of the Kinect, there has been renewed interest in developing methods for human gesture and action recognition from 3D data. A number of approaches have been proposed that extract representative features from 3D depth data, a reconstructed 3D surface mesh or more commonly from the recovered estimate of the human skeleton. Recent advances in neuroscience have discovered a neural encoding of static 3D shapes in primate infero-temporal cortex that can be represented as a hierarchy of medial axis and surface features. We hypothesize a similar neural encoding might also exist for 3D shapes in motion and propose a hierarchy of dynamic medial axis structures at several spatio-temporal scales that can be modeled using a set of Linear Dynamical Systems (LDSs). We then propose novel discriminative metrics for comparing these sets of LDSs for the task of human activity recognition. Combined with simple classification frameworks, our proposed features and corresponding hierarchical dynamical models provide the highest human activity recognition rates as compared to state-of-the-art methods on several skeletal datasets.

关键词： gesture recognition mesh generation neural nets pattern classification shape recognition solid modelling surface fitting

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A Compensation Method of Motion Features with Regression for Deficient Depth Image

A Compensation Method of Motion Features with Regression for...

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

作者： Yumiba, Ryo Agata, Yoshiki Fujiyoshi, Hironobu Hitachi Ltd Hitachi Res Lab Tokyo Japan Chubu Univ Kasugai Aichi 487 Japan

ISBN: (纸本)9780769549903

In this paper, we propose a method for compensating for motion features that are outside a given viewing angle by using a regression estimate that is based on a correlation between the motion features from human bodies deficient visually, when recognizing the actions of people whose bodies are only partially within the given view. this compensation is good for use in situations where parts of a person's body are partially protruding outside the edges of the viewing angle, and contributes to enlarging the region coverage for action recognition. the motion features and position of the acting person in a depth image are calculated first in the proposed method. Second, the deficit length protruding outside the view angle is calculated, according to the position of the person. Finally, the motion features from the entire body are estimated using a regression estimate from the motion features by selecting the regression coefficients according to the deficit length. the method for improving the effectiveness of the F-measure is confirmed using three kinds of motion features in a fundamental laboratory experiment. We found from the experimental results that the F-measure was improved by more 12.5% when using motion feature compensation compared to without compensation when the person within the viewing angle cannot actually be seen from the floor to 630 mm above it.

关键词： correlation methods feature extraction gesture recognition image motion analysis regression analysis

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Video Demo: An Egocentric vision based Assistive Co-robot

Video Demo: An Egocentric Vision based Assistive Co-robot

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

作者： Zhang, Jingzhe Zhuang, Lishuo Wang, Yang Zhou, Yameng Meng, Yan Hua, Gang Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA Stevens Inst Technol Dept Comp Sci Hoboken NJ 07030 USA

ISBN: (纸本)9780769549903

We present the video demo of the prototype of an egocentric vision based assistive co-robot system. In this co-robot system, the user is wearing a pair of glasses with a forward looking camera, and is actively engaged in the control loop of the robot in navigational tasks. the egocentric vision glasses serve for two purposes. First, it serves as a source of visual input to request the robot to find a certain object in the environment. Second, the motion patterns computed from the egocentric video associated with a specific set of head movements are exploited to guide the robot to find the object. these are especially helpful for quadriplegic individuals who do not have the needed hand functionality for control with other modalities (e.g., joystick). In our co-robot system, when the robot does not fulfill the object finding task in a pre-specified time window, it would actively solicit user controls for guidance. then the users can use the egocentric vision based gesture interface to orient the robot towards the direction of the object. After that the robot will automatically navigate towards the object until it finds it. Our experiments validated the efficacy of the closed-loop design to engage the human in the loop.

关键词： closed loop systems gesture recognition handicapped aids human-robot interaction image motion analysis medical robotics mobile robots path planning robot vision video signal processing

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Nonparametric Facial Feature Localization

Nonparametric Facial Feature Localization

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

作者： Tamersoy, Birgi Hu, Changbo Aggarwal, J. K. Univ Texas Austin Comp & Vis Res Ctr Austin TX 78712 USA

ISBN: (纸本)9780769549903

Any facial feature localization algorithm needs to incorporate two sources of information: 1) prior shape knowledge, and 2) image observations. Existing methods have primarily focused on different ways of representing and incorporating the image observations into the problem solution. Prior shape knowledge, on the other hand, has been mostly modeled using parametrized shape models. Parametrized shape models have relatively few parameters to control the shape variations, and hence their representation power is limited with the examples provided in the training data. In this paper, we propose a novel method for modeling the prior shape knowledge. Rather than using a holistic approach, as in the case for parametrized shape models, we model the prior shape knowledge as a set of local compatibility potentials. this "distributed" approach provides a greater representation power as it allows for individual landmarks to move more freely. the prior shape knowledge is incorporated with local image observations in a probabilistic graphical model framework, where the inference is achieved through nonparametric belief propagation. through qualitative and quantitative experiments, the proposed approach is shown to outperform the state-of-the-art methods in terms of localization accuracy.

关键词： belief networks face recognition feature extraction graph theory inference mechanisms message passing probability shape recognition

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