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International Conference on Document Analysis and recognition

作者： Nibal Nayef Yash Patel Michal Busta Pinaki Nath Chowdhury Dimosthenis Karatzas Wafa Khlif Jiri Matas Umapada Pal Jean-Christophe Burie Cheng-lin Liu Jean-Marc Ogier no affiliation The Robotics Institute Carnegie Mellon Universiry Pittsburgh USA Department of Cybernetics Czech Technical University Prague Czech Republic CVPR unit Indian Statistical Institute India Computer Vision Center Universitat Autònoma de Barcelona Spain L3i Laboratory University of La Rochelle France National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences China

With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the state-of-the-art forward, the proposed competition builds on top of the RRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline End-to-End recognition method. The real dataset consists of 20,000 images containing text from 10 languages. The challenge has 4 tasks covering various aspects of multi-lingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) end-to-end detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. This paper presents the dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge.

关键词： Task analysis Text recognition Training Benchmark testing Protocols Rendering (computer graphics)

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Patch based specular reflection removal for range images in hybrid 3-D endoscopy

Patch based specular reflection removal for range images in ...

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IEEE International Symposium on Biomedical Imaging

作者： Sven Haase Jakob Wasza Mustafa Safak Thomas Kilgus Lena Maier-Hein Hubertus Feußner Joachim Hornegger Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Div. Medical and Biological Informatics Junior Group: Computer-assisted Interventions German Cancer Research Center (DKFZ) Heidelberg Minimally Invasive Therapy and Intervention Technische Universitat Munchen Munchen Bayern DE Erlangen Graduate School in Advanced Optical Technologies (SAOT)

In minimally invasive surgery, hybrid 3-D endoscopy is an evolving field of research that aims to augment conventional video based systems by metric 3-D measurements. One crucial issue with these systems arises from specular reflections that result in overexposed RGB values and invalid range measurements. In this paper, we address this problem by registering video and range information acquired from different view points using a patch based approach. This allows to replace invalid measurements caused by specular reflections in one view with valid data from non-specular regions in the other view. In contrast to previous approaches that employ interpolation techniques our method utilizes actual scene information being advantageous in a medical environment. In our experiments, we show that our method decreases the mean absolute error in common situations on average by more than 30% compared to conventional interpolation. Moreover, for challenging scenarios we outperform interpolation by more than 1 mm and reconstruct important structures that inherently could not be restored by conventional interpolation.

关键词： Endoscopes Interpolation Image restoration Convolution Image color analysis Conferences Educational institutions

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Motion estimation in PET-MRI based on dual registration: preliminary results for human data

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EJNMMI physics 2014年第Suppl 1期1卷 A39页

作者： Michael Fieseler Thomas Kösters Christopher Glielmi Fernando Boada David Faul Matthias Fenchel Robert Grimm Xiaoyi Jiang Klaus P Schäfers European Institute for Molecular Imaging University of Münster Münster Germany. Department of Computer Science University of Münster Münster Germany. Center for Advanced Imaging Innovation and Research NYU Langone Medical Center New York USA. Siemens Medical Solutions USA New York USA. Siemens AG Healthcare Sector Kragujevac Germany. Pattern Recognition Lab University of Erlangen-Nürnberg Erlangen Germany.

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Cross-ethnicity face anti-spoofing recognition challenge: A review

arXiv

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arXiv 2020年

作者： Liu, Ajian Li, Xuan Wan, Jun Liang, Yanyan Escalera, Sergio Escalante, Hugo Jair Madadi, Meysam Jin, Yi Wu, Zhuoyuan Yu, Xiaogang Tan, Zichang Yuan, Qi Yang, Ruikun Zhou, Benjia Guo, Guodong Li, Stan Z. Faculty of Information Technology Avenida WaiLong Taipa Macau China School of Computer and Information Technology Beijing Jiaotong University Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science Beijing China Universitat de Barcelona and Computer Vision Center Barcelona Instituto Nacional de Astrofísica Óptica y Electrónica Puebla Mexico School of Software Beihang University Beijing China Institute of Deep Learning Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application Beijing Westlake University Hangzhou China

Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has achieved impressive progress recently due the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing. Recently, a multi-ethnic face anti-spoofing dataset, CASIA-SURF CeFA, has been released with the goal of measuring the ethnic bias. It is the largest up to date cross-ethnicity face anti-spoofing dataset covering 3 ethnicities, 3 modalities, 1;607 subjects, 2D plus 3D attack types, and the first dataset including explicit ethnic labels among the recently released datasets for face *** organized the Chalearn Face Anti-spoofing Attack Detection Challenge which consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks around this novel resource to boost research aiming to alleviate the ethnic bias. Both tracks have attracted 340 teams in the development stage, and finally 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Large dataset

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Deep crisp boundaries: From boundaries to higher-level tasks

arXiv

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arXiv 2018年

作者： Wang, Yupei Zhao, Xin Li, Yin Huang, Kaiqi Center for Research on Intelligent System and Engineering Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Department of Biostatistics and Medical Informatics Department of Computer Sciences Univeristy of Wisconsin-Madison Center for Research on Intelligent System and Engineering National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology 100190

Edge detection has made significant progress with the help of deep Convolutional Networks (ConvNet). These ConvNet based edge detectors have approached human level performance on standard benchmarks. We provide a systematical study of these detectors' outputs. We show that the detection results did not accurately localize edge pixels, which can be adversarial for tasks that require crisp edge inputs. As a remedy, we propose a novel refinement architecture to address the challenging problem of learning a crisp edge detector using ConvNet. Our method leverages a top-down backward refinement pathway, and progressively increases the resolution of feature maps to generate crisp edges. Our results achieve superior performance, surpassing human accuracy when using standard criteria on BSDS500, and largely outperforming state-of-the-art methods when using more strict criteria. More importantly, we demonstrate the benefit of crisp edge maps for several important applications in computer vision, including optical flow estimation, object proposal generation and semantic segmentation. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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Survey on Handwritten Mathematical Expression recognition in the Last Decade: Grammar- and Graph-Based Parsing, and the Rise of Encoder-Decoder Models and Graph Neural Networks

SSRN

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SSRN 2023年

作者： Truong, Thanh-Nghia Nguyen, Cuong Tuan Zanibbi, Richard Mouchère, Harold Nakagawa, Masaki University Research Administrator Center Department of Computer Science Rochester Institute of Technology NY United States Tokyo University of Agriculture and Technology Tokyo Japan FPT University HCMC Campus Viet Nam Document and Pattern Recognition Lab Rochester Institute of Technology New York United States LS2N - UMR CNRS 6004 University of Nantes Nantes France

Machine recognition of handwritten mathematical expressions (HMEs) is an area that has attracted interest owing to steady progress in handwriting recognition and the rapid emergence of pen- and touch-based devices. HME recognition can be considered as an extension of text recognition with a two-dimensional structure of characters and symbols. This survey examines the solutions proposed for recognizing online and offline HMEs over the last decade. A common strategy for recognizing HMEs involves splitting the recognition process into four key tasks: symbol segmentation, symbol classification, spatial relation classification, and structural analysis. Recently, encoder–decoder models using Deep Neural Networks (DNNs) have become popular and can perform all key tasks simultaneously and achieve high performance. Furthermore, evaluation methods and benchmark datasets were used to explore the implicit dependencies among key tasks. Finally, we point out some limitations of the current systems and present a future outlook on the DNN approach and the utilization of context to support HMEs parsing. © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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2023 IEEE/CVF Conference on computer vision and pattern recognition Workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

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Individualized brain mapping for navigated neuromodulation

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Chinese Medical Journal 2024年第5期137卷 508-523页

作者： Chaohong Gao Xia Wu Xinle Cheng Kristoffer Hougaard Madsen Congying Chu Zhengyi Yang Lingzhong Fan Sino–Danish College University of Chinese Academy of SciencesBeijing 100190China Brainnetome Center National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China Department of Applied Mathematics and Computer Science Technical University of DenmarkKongens Lyngby 2800Denmark Danish Research Centre for Magnetic Resonance Centre for Functional and Diagnostic Imaging and ResearchCopenhagen University Hospital Amager and HvidovreHvidovre 2650Denmark CAS Center for Excellence in Brain Science and Intelligence Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Health and Life Sciences University of Health and Rehabilitation SciencesQingdaoShandong 266000China

The brain is a complex organ that requires precise mapping to understand its structure and *** atlases provide a powerful tool for studying brain circuits,discovering biological markers for early diagnosis,and developing personalized treatments for neuropsychiatric *** techniques,such as transcranial magnetic stimulation and deep brain stimulation,have revolutionized clinical therapies for neuropsychiatric ***,the lack of fine-scale brain atlases limits the precision and effectiveness of these *** in neuroimaging and machine learning techniques have led to the emergence of stereotactic-assisted neurosurgery and navigation ***,the individual variability among patients and the diversity of brain diseases make it necessary to develop personalized *** article provides an overview of recent advances in individualized brain mapping and navigated neuromodulation and discusses the methodological profiles,advantages,disadvantages,and future trends of these *** article concludes by posing open questions about the future development of individualized brain mapping and navigated neuromodulation.

关键词： Brain atlas Individualization Navigated neuromodulation Multimodal magnetic resonance imaging Transcranial magnetic stimulation Deep brain stimulation

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Efficient Mode Transfer on a Compact Silicon Chip by Encircling Moving Exceptional Points

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Physical Review Letters 2020年第15期124卷 153903-153903页

作者： Qingjie Liu Shuyi Li Bing Wang Shaolin Ke Chengzhi Qin Kai Wang Weiwei Liu Dingshan Gao Pierre Berini Peixiang Lu School of Physics and Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan 430074 China Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan 430205 China School of Electrical Engineering and Computer Science University of Ottawa Ottawa Ontario K1N 6N5 Canada Department of Physics and Center for Research in Photonics University of Ottawa Ottawa Ontario K1N 6N5 Canada CAS Center for Excellence in Ultra-Intense Laser Science Shanghai 201800 China

Exceptional points (EPs) are branch point singularities of self-intersecting Riemann sheets, and they can be observed in a non-Hermitian system with complex eigenvalues. It has been revealed recently that dynamically encircling EPs by adiabatically changing the parameters of a system composed of lossy optical waveguides could lead to asymmetric (input-output) mode transfer. However, the length of the waveguides had to be considerable to ensure adiabatic evolution. Here we demonstrate that the parameters can change adiabatically along a smaller encircling loop by utilizing moving EPs, leading to significant shortening of the structures compared to fixed EPs. Meanwhile, the mode transmittance is remarkably improved and the transfer efficiency persists at ∼90%. Moving EPs are very promising for applications such as highly integrated broadband optical switches and convertors operating at telecommunication wavelengths.

关键词： Metamaterials Optical materials & elements Photonics Waveguides

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Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning

arXiv

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arXiv 2017年

作者： Aubreville, Marc Knipfer, Christian Oetter, Nicolai Jaremenko, Christian Rodner, Erik Denzler, Joachim Bohr, Christopher Neumann, Helmut Stelzle, Florian Maier, Andreas Pattern Recognition Lab Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Department of Oral and Maxillofacial Surgery University Medical Center Hamburg-Eppendorf Germany Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Department of Oral and Maxillofacial Surgery University Medical Center Erlangen Friedrich-Alexander- Universität Erlangen-Nürnberg Germany Computer Vision Group Friedrich-Schiller-Universität Jena Germany Department of Otorhinolaryngology Head and Neck Surgery University Medical Center Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Germany First Department of Internal Medicine University Medical Center Mainz Johannes Gutenberg-Universität Mainz Germany

Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, sufficient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and an reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC in situ. We present and evaluate a novel automatic approach for a highly accurate OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3 % (sensitivity 86.6 %, specificity 90 %). Copyright © 2017, The Authors. All rights reserved.

关键词： Diagnosis

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