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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201-210 订阅

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Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers

Adapting a Pedestrian Detector by Boosting LDA Exemplar Clas...

引用

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

作者： Xu, Jiaolong Vazquez, David Ramos, Sebastian Lopez, Antonio M. Ponsa, Daniel Autonomous Univ Barcelona Comp Vis Ctr E-08193 Barcelona Spain

ISBN: (纸本)9780769549903

Training vision-based pedestrian detectors using synthetic datasets (virtual world) is a useful technique to collect automatically the training examples with their pixel-wise ground truth. However, as it is often the case, these detectors must operate in real-world images, experiencing a significant drop of their performance. In fact, this effect also occurs among different real-world datasets, i.e. detectors' accuracy drops when the training data (source domain) and the application scenario (target domain) have inherent differences. therefore, in order to avoid this problem, it is required to adapt the detector trained with synthetic data to operate in the real-world scenario. In this paper, we propose a domain adaptation approach based on boosting LDA exemplar classifiers from both virtual and real worlds. We evaluate our proposal on multiple real-world pedestrian detection datasets. the results show that our method can efficiently adapt the exemplar classifiers from virtual to real world, avoiding drops in average precision over the 15%.

关键词： computer vision object detection pedestrians statistical analysis

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General Regression and Representation Model for Face recognition

General Regression and Representation Model for Face Recogni...

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

作者： Qian, Jianjun Yang, Jian Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9780769549903

Recently, the regularized coding-based classification method (e. g. SRC and CRC) shows a great potential for face recognition. However, most existing coding methods ignore the statistical information from the training data, which actually plays an important role in classification. To address this problem, we develop a general regression and representation model (GRR) for classification. GRR not only has advantages of CRC, but also introduces the prior information and the specific information to enhance the classification performance. In GRR, we combine the leave-one-out strategy with K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using the iterative algorithm to update the feature weights of the test sample. Finally, we classify the test sample based on the reconstruction error of each class. the proposed model is evaluated on public face image databases. And the experimental results demonstrate the advantages of GRR over state-of-the-art methods.

关键词： face recognition image classification image coding image reconstruction image representation iterative methods regression analysis visual databases

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Single-Sample Face recognition with Image Corruption and Misalignment via Sparse Illumination Transfer

Single-Sample Face Recognition with Image Corruption and Mis...

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

作者： Zhuang, Liansheng Yang, Allen Y. Zhou, Zihan Sastry, S. Shankar Ma, Yi Univ Sci & Technol China Hefei 230026 Peoples R China Univ Calif Berkeley Dept EECS Berkeley CA 94720 USA Univ Illinois Dept ECE Chicago IL 60680 USA

ISBN: (纸本)9780769549897

Single-sample face recognition is one of the most challenging problems in face recognition. We propose a novel face recognition algorithm to address this problem based on a sparse representation based classification (SRC) framework. the new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required training images to one sample per class. To compensate the missing illumination information typically provided by multiple training images, a sparse illumination transfer (SIT) technique is introduced. the SIT algorithms seek additional illumination examples of face images from one or more additional subject classes, and form an illumination dictionary. By enforcing a sparse representation of the query image, the method can recover and transfer the pose and illumination information from the alignment stage to the recognition stage. Our extensive experiments have demonstrated that the new algorithms significantly outperform the existing algorithms in the single-sample regime and with less restrictions. In particular, the face alignment accuracy is comparable to that of the well-known Deformable SRC algorithm using multiple training images;and the face recognition accuracy exceeds those of the SRC and Extended SRC algorithms using hand labeled alignment initialization.

关键词： computer vision face recognition image classification image representation image resolution image retrieval lighting pose estimation

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Cardiff Conversation Database (CCDb): A Database of Natural Dyadic Conversations

Cardiff Conversation Database (CCDb): A Database of Natural ...

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

作者： Aubrey, Andrew J. Marshall, David Rosin, Paul L. Vandeventer, Jason Cunningham, Douglas W. Wallraven, Christian Cardiff Univ Sch Comp Sci Cardiff CF10 3AX S Glam Wales Brandenburg Tech Univ Cottbus Cottbus Germany Korea Univ Seoul South Korea

ISBN: (纸本)9780769549903

We present the Cardiff Conversation Database (CCDb), a unique 2D audiovisual database containing natural conversations between pairs of people. the database currently contains 30 conversations. To date, eight conversations are fully annotated for speaker activity, facial expressions, head motion, and non-verbal utterances. In this paper we describe the data collection and annotation process. We also provide results of baseline experiments in which an SVM classifier was used to identify which parts of the recordings are from the frontchannel speaker and which are backchannel signals. We believe this database will make a useful contribution to computer vision, affective computing, and cognitive science communities by providing raw data, features, annotations and baseline comparisons.

关键词： audio databases pattern classification support vector machines visual databases

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Automated X-ray object recognition using an efficient search algorithm in multiple views

Automated X-ray object recognition using an efficient search...

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

作者： Mery, Domingo Riffo, Vladimir Zuccar, Irene Pieringer, Christian Pontificia Univ Catolica Chile Dept Comp Sci Santiago Chile

ISBN: (纸本)9780769549903

In order to reduce the security risk of a commercial aircraft, passengers are not allowed to take certain items in their carry-on baggage. For this reason, human operators are trained to detect prohibited items using a manually controlled baggage screening process. In this paper, we propose the use of an automated method based on multiple Xray views to recognize certain regular objects with highly defined shapes and sizes. the method consists of two steps: 'monocular analysis', to obtain possible detections in each view of a sequence, and 'multiple view analysis', to recognize the objects of interest using matchings in all views. the search for matching candidates is efficiently performed using a lookup table that is computed off-line. In order to illustrate the effectiveness of the proposed method, experimental results on recognizing regular objects -clips, springs and razor blades-in pen cases are shown achieving around 93% accuracy for 120 objects. We believe that it would be possible to design an automated aid in a target detection task using the proposed algorithm.

关键词： X-ray imaging object recognition security

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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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Constrained Clustering and Its Application to Face Clustering in Videos

Constrained Clustering and Its Application to Face Clusterin...

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

作者： Wu, Baoyuan Zhang, Yifan Hu, Bao-Gang Ji, Qiang CASIA NLPR Beijing 100190 Peoples R China Rensselaer Polytech Inst Troy NY 12180 USA

ISBN: (纸本)9780769549897

In this paper, we focus on face clustering in videos. Given the detected faces from real-world videos, we partition all faces into K disjoint clusters. Different from clustering on a collection of facial images, the faces from videos are organized as face tracks and the frame index of each face is also provided. As a result, many pairwise constraints between faces can be easily obtained from the temporal and spatial knowledge of the face tracks. these constraints can be effectively incorporated into a generative clustering model based on the Hidden Markov Random Fields (HMRFs). Within the HMRF model, the pairwise constraints are augmented by label-level and constraint-level local smoothness to guide the clustering process. the parameters for both the unary and the pairwise potential functions are learned by the simulated field algorithm, and the weights of constraints can be easily adjusted. We further introduce an efficient clustering framework specially for face clustering in videos, considering that faces in adjacent frames of the same face track are very similar. the framework is applicable to other clustering algorithms to significantly reduce the computational cost. Experiments on two face data sets from real-world videos demonstrate the significantly improved performance of our algorithm over state-of-the-art algorithms.

关键词： face recognition hidden Markov models object tracking pattern clustering video signal processing

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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 ...

引用

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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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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Towards Pose Robust Face recognition

Towards Pose Robust Face Recognition

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

作者： Yi, Dong Lei, Zhen Li, Stan Z. Chinese Acad Sci Inst Automat Ctr Biometr & Secur Res Beijing 100864 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China

ISBN: (纸本)9780769549897

Most existing pose robust methods are too computational complex to meet practical applications and their performance under unconstrained environments are rarely evaluated. In this paper, we propose a novel method for pose robust face recognition towards practical applications, which is fast, pose robust and can work well under unconstrained environments. Firstly, a 3D deformable model is built and a fast 3D model fitting algorithm is proposed to estimate the pose of face image. Secondly, a group of Gabor filters are transformed according to the pose and shape of face image for feature extraction. Finally, PCA is applied on the pose adaptive Gabor features to remove the redundances and Cosine metric is used to evaluate the similarity. the proposed method has three advantages: (1) the pose correction is applied in the filter space rather than image space, which makes our method less affected by the precision of the 3D model;(2) By combining the holistic pose transformation and local Gabor filtering, the final feature is robust to pose and other negative factors in face recognition;(3) the 3D structure and facial symmetry are successfully used to deal with self-occlusion. Extensive experiments on FERET and PIE show the proposed method outperforms state-of-the-art methods significantly, meanwhile, the method works well on LFW.

关键词： Gabor filters face recognition feature extraction hidden feature removal pose estimation principal component analysis solid modelling

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