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检索条件"机构=Pattern Recognition Lab Computer Vision Group"
327 条 记 录,以下是191-200 订阅
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CRNN Based Jersey-Bib Number/Text recognition in Sports and Marathon Images
CRNN Based Jersey-Bib Number/Text Recognition in Sports and ...
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International Conference on Document Analysis and recognition
作者: Sauradip Nag Raghavendra Ramachandra Palaiahnakote Shivakumara Umapada Pal Tong Lu Mohan Kankanhalli Department of Computer Science & Engineering Kalyani Government Engineering College Kalyani India Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Norway Faculty of Computer System and Information Technology University of Malaya Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University China Department of computer science National University of Singapore Singapore
The primary challenge in tracing the participants in sports and marathon video or images is to detect and localize the jersey/Bib number that may present in different regions of their outfit captured in cluttered envi... 详细信息
来源: 评论
Non-deterministic behavior of ranking-based metrics when evaluating embeddings
arXiv
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arXiv 2018年
作者: Nicolaou, Anguelos Dey, Sounak Christlein, Vincent Maier, Andreas Karatzas, Dimosthenis Computer Vision Center Edificio O Campus UAB Bellaterra08193 Spain Pattern Recognition Lab Friedrich-Alexander-Universitat Erlangen-Nurnberg
Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis... 详细信息
来源: 评论
NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results
arXiv
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arXiv 2022年
作者: Guo, Yulan Wang, Longguang Wang, Yingqian Li, Juncheng Gu, Shuhang Timofte, Radu Chen, Liangyu Chu, Xiaojie Yu, Wenqing Jin, Kai Wei, Zeqiang Guo, Sha Yang, Angulia Zhou, Xiuzhuang Guo, Guodong Xiao, Huaxin Yan, Shen Liu, Yuxiang Cai, Hanxiao Dai, Bin Peng, Feiyue Cao, Pu Nie, Yang Yang, Lu Song, Qing Hu, Xiaotao Xu, Jun Xu, Mai Jing, Junpeng Deng, Xin Xing, Qunliang Qiao, Minglang Guan, Zhenyu Guo, Wenlong Peng, Chenxu Chen, Zan Chen, Junyang Li, Hao Chen, Junbin Li, Weijie Yang, Zhijing Li, Gen Li, Aijin Sun, Lei Zhang, Dafeng Liu, Shizhuo Zhang, Jiangtao Qu, Yanyun Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Liang, Qiaohui Lin, Jianxin Wang, Yijun Yin, Lianying Zhang, Rongju Zhao, Wei Xiao, Peng Xu, Rongjian Zhang, Zhilu Zuo, Wangmeng Guo, Hansheng Gao, Guangwei Zeng, Tieyong Kim, Joohyeok Kim, HyeonA Park, Eunpil Sim, Jae-Young Pi, Huicheng Zhang, Shunli Zhai, Jucai Zeng, Pengcheng Liu, Yang Ma, Chihao Huang, Yulin Chen, Junying National University of Defense Technology China The Chinese University of Hong Kong Hong Kong The University of Sydney Australia University of Würzburg ETH Zürich Switzerland MEGVII Technology China Peking University China Bigo Technology Pte. Ltd Singapore Smart Healthcare Innovation Lab Beijing University of Posts and Telecommunications China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Head of Institute of Deep Learning Baidu Research College of Systems Engineering National University of Defense Technology China College of Liberal Arts and Sciences National University of Defense Technology China Pattern Recognition and Intelligent Vision Lab Beijing University of Posts and Telecommunications China College of Computer Science Nankai University Tianjin China School of Statistics and Data Science Nankai University Tianjin Singapore Beihang University China Zhejiang University of Technology China Guangdong University of Technology China Tencent OVBU SRC-B Xiamen University China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan College of Computer Science and Electronic Engineering Hunan University China Harbin Institude of Technology China The Chinese University of Hong Kong Hong Kong Nanjing University of Posts and Telecommunications China Department of Electrical Engineering Ulsan National Institute of Science and Technology Korea Republic of Graduate School of Artificial Intelligence Ulsan National Institute of Science and Technology Korea Republic of Beijing Jiaotong University China City University of Hong Kong Hong Kong South China University of Technology China
In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new solutions and results. This challenge ha... 详细信息
来源: 评论
MsEDNet: Multi-Scale Deep Saliency Learning for Moving Object Detection
MsEDNet: Multi-Scale Deep Saliency Learning for Moving Objec...
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IEEE International Conference on Systems, Man, and Cybernetics
作者: Prashant W. Patil Subrahmanyam Murala Abhinav Dhall Sachin Chaudhary Indian Institute of Technology Delhi New Delhi Delhi IN Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar INDIA Learning Afffect and Semantic Image AnalysIs (LASII) Group Indian Institute of Technology Ropar INDIA
Moving object detection (foreground and background) is an important problem in computer vision. Most of the works in this problem are based on background subtraction. However, these approaches are not able to handle s... 详细信息
来源: 评论
A deep learning architecture for limited-angle computed tomography reconstruction
A deep learning architecture for limited-angle computed tomo...
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Workshops on Image processing for the medicine, 2017
作者: Hammernik, Kerstin Würfl, Tobias Pock, Thomas Maier, Andreas Institute of Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition Lab Friedrich-Alexander-University Germany Digital Safety and Security Department AIT Austrian Institute of Technology Austria
Limited-angle computed tomography suffers from missing data in the projection domain, which results in intensity inhomogeneities and streaking artifacts in the image domain. We address both challenges by a two-step de... 详细信息
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Weighted-Gradient Features for Handwritten Line Segmentation
Weighted-Gradient Features for Handwritten Line Segmentation
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International Conference on pattern recognition
作者: Vijeta Khare Palaiahnakote Shivakumara B.J. Navya G.C. Swetha D. S. Guru Umapada Pal Tong Lu Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Studies in Computer Science University of Mysore Karnataka India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China
Text line segmentation from handwritten documents is challenging when a document image contains severe touching. In this paper, we propose a new idea based on Weighted-Gradient Features (WGF) for segmenting text lines... 详细信息
来源: 评论
Adaptive Multi-Gradient Kernels for Handwritting Based Gender Identification
Adaptive Multi-Gradient Kernels for Handwritting Based Gende...
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International Workshop on Frontiers in Handwriting recognition
作者: B. J Navya Palaiahnakote Shivakumara G.C Shwetha Sangheeta Roy D. S. Guru Umapada Pal Tong Lu Department of Studies in Computer Science University of Mysore Karnataka India Faculty of Computer System and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China
Handwriting based Gender identification is challenging due to unconstrained handwriting and individual differences in writing. To solve this problem, we propose a new adaptive multi-gradient of Sobel kernels for extra... 详细信息
来源: 评论
Multi-Gradient Directional Features for Gender Identification
Multi-Gradient Directional Features for Gender Identificatio...
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International Conference on pattern recognition
作者: B.J. Navya G. C. Swetha Palaiahnakote Shivakumara Sangheeta Roy D. S. Guru Umapada Pal Tong Lu Department of Studies in Computer Science University of Mysore Karnataka India Faculty of Computer System and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China
Gender identification based on handwriting analysis has received a special attention to researchers in the field of document image analysis as it is useful for several real-time applications like forensic, population ... 详细信息
来源: 评论
A New RGB Based Fusion for Forged IMEI Number Detection in Mobile Images
A New RGB Based Fusion for Forged IMEI Number Detection in M...
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International Workshop on Frontiers in Handwriting recognition
作者: Palaiahnakote Shivakumara V. Basavaraja Harsha S. Gowda D. S. Guru Umapada Pal Tong Lu Faculty of Computer System and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Studies in Computer Science University of Mysore Mysore Karnataka India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China
As technology advances to make living comfortable for people, at the same time, different crimes also increase. One such sensitive crime is creating fake International Mobile Equipment Identity (IMEI) for smart mobile... 详细信息
来源: 评论
How many annotators do we need? - A study on the influence of inter-observer variability on the reliability of automatic mitotic figure assessment
arXiv
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arXiv 2020年
作者: Wilm, Frauke Bertram, Christof A. Marzahl, Christian Bartel, Alexander Donovan, Taryn A. Assenmacher, Charles-Antoine Becker, Kathrin Bennett, Mark Corner, Sarah Cossic, Brieuc Denk, Daniela Dettwiler, Martina Gonzalez, Beatriz Garcia Gurtner, Corinne Heier, Annabelle Lehmbecker, Annika Merz, Sophie Plog, Stephanie Schmidt, Anja Sebastian, Franziska Smedley, Rebecca C. Tecilla, Marco Thaiwong, Tuddow Breininger, Katharina Kiupel, Matti Maier, Andreas Klopfleisch, Robert Aubreville, Marc Pattern Recognition Lab Computer Sciences Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Institute of Veterinary Pathology Freie Universität Berlin Germany Institute for Veterinary Epidemiology and Biostatistics Freie Universität Berlin Department of Anatomic Pathology Animal Medical Center New York United States Department of Pathobiology University of Pennsylvania Philadelphia United States Department of Pathology University of Veterinary Medicine Hannover Germany Synlab's VPG Histology Bristol United Kingdom Veterinary Diagnostic Laboratory Michigan State University Lansing United States Idorsia Pharmaceuticals Ltd Switzerland International Zoo Veterinary Group Keighley United Kingdom Institute of Animal Pathology Vetsuisse Faculty University of Bern Switzerland IDEXX Vet Med Labor GmbH Kornwestheim Germany F. Hoffmann-La Roche Ltd Basel Switzerland Technische Hochschule Ingolstadt Ingoldstadt Germany
Density of mitotic figures in histologic sections is a prognostically relevant characteristic for many tumours. Due to high inter-pathologist variability, deep learning-based algorithms are a promising solution to imp... 详细信息
来源: 评论