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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015"
19687 条 记 录,以下是161-170 订阅
排序:
Dealing with occlusions in the eigenspace approach
Dealing with occlusions in the eigenspace approach
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1966 ieee computer Society conference on computer vision and pattern recognition
作者: Leonardis, A Bischof, H VIENNA TECH UNIV DEPT PATTERN RECOGNIT & IMAGE PROCA-1040 VIENNAAUSTRIA
The basic limitations of the current appearance-based matching methods using eigenimages are non-robust estimation of coefficients and inability to cope with problems related to occlusions and segmentation. In this pa... 详细信息
来源: 评论
Learning and recognizing human dynamics in video sequences
Learning and recognizing human dynamics in video sequences
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1997 ieee computer Society conference on computer vision and pattern recognition (cvpr 97)
作者: Bregler, C Univ of California Berkeley United States
This paper describes a probabilistic decomposition of human dynamics at multiple abstractions, and shows how to propagate hypotheses across space, time, and abstraction levels. recognition in this framework is the suc... 详细信息
来源: 评论
Learning a Non-linear Knowledge Transfer Model for Cross-View Action recognition
Learning a Non-linear Knowledge Transfer Model for Cross-Vie...
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ieee conference on computer vision and pattern recognition (cvpr)
作者: Rahmani, Hossein Mian, Ajmal Univ Western Australia Comp Sci & Software Engn Nedlands WA 6009 Australia
This paper concerns action recognition from unseen and unknown views. We propose unsupervised learning of a non-linear model that transfers knowledge from multiple views to a canonical view. The proposed Non-linear Kn... 详细信息
来源: 评论
Closed-loop object recognition using reinforcement learning
Closed-loop object recognition using reinforcement learning
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1966 ieee computer Society conference on computer vision and pattern recognition
作者: Peng, J Bhanu, B UNIV CALIF RIVERSIDE COLL ENGNRIVERSIDECA 92521
Current computer vision systems whose basic methodology is open-loop or filter type typically use image segmentation followed by object recognition algorithms. These systems are not robust for most real-world applicat... 详细信息
来源: 评论
Supervised Mid-Level Features for Word Image Representation
Supervised Mid-Level Features for Word Image Representation
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ieee conference on computer vision and pattern recognition (cvpr)
作者: Gordo, Albert Xerox Res Ctr Europe Comp Vis Grp Meylan France
This paper addresses the problem of learning word image representations: given the cropped image of a word, we are interested in finding a descriptive, robust, and compact fixed-length representation. Machine learning... 详细信息
来源: 评论
Tracking non-rigid, moving objects based on color cluster flow
Tracking non-rigid, moving objects based on color cluster fl...
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1997 ieee computer Society conference on computer vision and pattern recognition (cvpr 97)
作者: Heisele, B Kressel, U Ritter, W Daimler-Benz AG Ulm Germany
In this contribution we present an algorithm for tracking non-rigid, moving objects in a sequence of colored images, which were recorded by a non-stationary camera. The application background is vision-based driving a... 详细信息
来源: 评论
Beyond Spatial Pooling: Fine-Grained Representation Learning in Multiple Domains
Beyond Spatial Pooling: Fine-Grained Representation Learning...
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ieee conference on computer vision and pattern recognition (cvpr)
作者: Li, Chi Reiter, Austin Hager, Gregory D. Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA
Object recognition systems have shown great progress over recent years. However, creating object representations that are robust to changes in viewpoint while capturing local visual details continues to be a challenge... 详细信息
来源: 评论
Web-Scale Training for Face Identification
Web-Scale Training for Face Identification
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ieee conference on computer vision and pattern recognition (cvpr)
作者: Taigman, Yaniv Yang, Ming Ranzato, Marc'Aurelio Wolf, Lior Facebook AI Res Menlo Pk CA 94025 USA Tel Aviv Univ Tel Aviv Israel
Scaling machine learning methods to very large datasets has attracted considerable attention in recent years, thanks to easy access to ubiquitous sensing and data from the web. We study face recognition and show that ... 详细信息
来源: 评论
Are textureless scenes recoverable?
Are textureless scenes recoverable?
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1997 ieee computer Society conference on computer vision and pattern recognition (cvpr 97)
作者: Sundaram, H Nayar, S Columbia Univ New York United States
It is widely accepted that textureless surfaces cannot be recovered using passive sensing techniques. The problem is approached by viewing image formation as a Sully three-dimensional mapping. It is shown that the len... 详细信息
来源: 评论
Sparse Output Coding for Large-Scale Visual recognition
Sparse Output Coding for Large-Scale Visual Recognition
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26th ieee conference on computer vision and pattern recognition (cvpr)
作者: Zhao, Bin Xing, Eric P. Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA
Many vision tasks require a multi-class classifier to discriminate multiple categories, on the order of hundreds or thousands. In this paper, we propose sparse output coding, a principled way for large-scale multi-cla... 详细信息
来源: 评论