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检索条件"机构=The Computer Vision and Pattern Recognition Laboratory"
211 条 记 录,以下是101-110 订阅
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Robust recovery of eigenimages in the presence of outliers and occlusions
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Journal of Computing and Information Technology 1996年 第1期4卷 25-38页
作者: Leonardis, Aleš Bischof, Horst Department for Pattern Recognition and Image Processing Technical University Vienna Treitlstraße 3/1832 ViennaA-1040 Austria Computer Vision Laboratory Faculty of Computer and Information Science University of Ljubljana Tržaška 25 Ljubljana1000 Slovenia
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
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Locating high-density clusters with noisy queries
Locating high-density clusters with noisy queries
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International Conference on pattern recognition
作者: Chen Cao Shifeng Chen Changqing Zou Jianzhuang Liu Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Information Engineering Chinese University of Hong Kong China
Semi-supervised learning (SSL) relies on a few labeled samples to explore data's intrinsic structure through pairwise smooth transduction. The performance of SSL mainly depends on two folds: (1) the accuracy of la... 详细信息
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Zero-Shot Video Restoration and Enhancement Using Pre-Trained Image Diffusion Model
arXiv
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arXiv 2024年
作者: Cao, Cong Yue, Huanjing Liu, Xin Yang, Jingyu School of Electrical and Information Engineering Tianjin University Tianjin China Computer Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland
Diffusion-based zero-shot image restoration and enhancement models have achieved great success in various tasks of image restoration and enhancement. However, directly applying them to video restoration and enhancemen... 详细信息
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Learning to avoid poor images: Towards task-aware C-arm cone-beam CT trajectories
arXiv
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arXiv 2019年
作者: Zaech, Jan-Nico Gao, Cong Bier, Bastian Taylor, Russell Maier, Andreas Navab, Nassir Unberath, Mathias Laboratory for Computational Sensing and Robotics Johns Hopkins University Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Computer Vision Laboratory Eidgenössische Technische Hochschule Zürich
Metal artifacts in computed tomography (CT) arise from a mismatch between physics of image formation and idealized assumptions during tomographic reconstruction. These artifacts are particularly strong around metal im... 详细信息
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Motion constraint patterns
Motion constraint patterns
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IEEE Workshop on Qualitative vision
作者: C. Fermuller Department for Pattern Recognition and Image Processing Institute for Automation Technical University of of Vienna Vienna Austria Computer Vision Laboratory Center for Automation Research University of Maryland College Park MD USA
The problem of egomotion recovery has been treated by using as input local image motion, with the published algorithms utilizing the geometric constraint relating 2-D local image motion (optical flow, correspondence, ... 详细信息
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Box-driven Class-wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation
Box-driven Class-wise Region Masking and Filling Rate Guided...
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IEEE/CVF Conference on computer vision and pattern recognition
作者: Chunfeng Song Yan Huang Wanli Ouyang Liang Wang Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences (CASIA) The University of Sydney SenseTime Computer Vision Research Group
Semantic segmentation has achieved huge progress via adopting deep Fully Convolutional Networks (FCN). However, the performance of FCN based models severely rely on the amounts of pixel-level annotations which are exp... 详细信息
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Towards Accurate Scene Text recognition With Semantic Reasoning Networks
Towards Accurate Scene Text Recognition With Semantic Reason...
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Conference on computer vision and pattern recognition (CVPR)
作者: Deli Yu Xuan Li Chengquan Zhang Tao Liu Junyu Han Jingtuo Liu Errui Ding School of Artificial Intelligence University of Chinese Academy of Sciences National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Department of Computer Vision Technology(VIS) Baidu Inc.
Scene text image contains two levels of contents: visual texture and semantic information. Although the previous scene text recognition methods have made great progress over the past few years, the research on mining ... 详细信息
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Learning to predict context-adaptive convolution for semantic segmentation
arXiv
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arXiv 2020年
作者: Liu, Jianbo He, Junjun Ren, Jimmy S. Qiao, Yu Li, Hongsheng CUHK-SenseTime Joint Laboratory Chinese University of Hong Kong Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences SenseTime Research
Long-range contextual information is essential for achieving high-performance semantic segmentation. Previous feature re-weighting methods [34] demonstrate that using global context for re-weighting feature channels c... 详细信息
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Co-Clustering Image Features and Semantic Concepts
Co-Clustering Image Features and Semantic Concepts
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IEEE International Conference on Image Processing
作者: Manjeet Rege Ming Dong Farshad Fotouhi Department of Computer Science Machine Vision & Pattern Recognition Laboratory Wayne State University Detroit MI USA Database & Multimedia Systems Group Wayne State University Detroit MI USA
In this paper, we present a novel idea of co-clustering image features and semantic concepts. We accomplish this by modelling user feedback logs and low-level features using a bipartite graph. Our experiments demonstr... 详细信息
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Finding a Semantic Structure Interactively in Image Databases
Finding a Semantic Structure Interactively in Image Database...
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IEEE International Conference on Multimedia and Expo (ICME)
作者: Manjeet Rege Ming Dong Farshad Fotouhi Machine Vision & Pattern Recognition Laboratory Department of Computer Science Wayne State University Detroit MI USA Database & Multimedia Systems Group Department of Computer Science Wayne State University Detroit MI USA
We present a new approach to organize an image database by finding a semantic structure interactively based on multi-user relevance feedback. By treating user relevance feedbacks as weak classifiers and combining them... 详细信息
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