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A Region-Based Representation of images in MARS

Journal of VLSI Signal processing Systems for Signal, image, and Video Technology 1998年第1-2期20卷 137-150页

作者： Servetto, Sergio D. Rui, Yong Ramchandran, Kannan Huang, Thomas S. Beckman Inst. Adv. Sci. and Technol. Univ. Illinois at Urbana-Champaign Urbana IL 61801 United States Universidad Nacional de La Plata Argentina Univ. Illinois at Urbana-Champaign United States Comp. Res. Adv. Applications Group IBM Argentina Argentina Image Formation and Processing Group Beckman Institute UIUC United States Department of Computer Science UNLP Argentina Dept. of Elec. and Comp. Engineering UIUC United States Multimedia Commun. Res. Department Bell Laboratories Murray Hill NJ United States Info. Sciences Research Department AT and T Labs. Florham Park NJ United States Department of Computer Science UIUC United States Southeast University China Tsinghua University China University of Illinois Urbana-Champaign IL United States Image Formation and Processing Group Beckman Inst. Advance Sci. Technol. UIUC United States Vis. Technol. Grp. of Microsoft Res. Redmond WA United States City College of New York United States Columbia University United States AT and T Bell Labs. United States Ctr. for Telecommunications Research Columbia University United States Elec. and Comp. Eng. Department United States Beckman Institute Coordinated Science Laboratory IL United States IEEE Signal Processing Society United States IEEE IMDSP Technical Committee United States IEEE Transactions on Image Proc. United States National Taiwan University Taipei Taiwan Massachusetts Inst. of Technology Cambridge MA United States Department of Electrical Engineering MIT United States School of Electrical Engineering United States Lab. for Info. and Signal Processing Purdue University United States Dept. of Elec. and Comp. Engineering United States Coordinated Science Laboratory United States Image Formation and Processing Group Beckman Inst. Adv. Sci. and Technol. United States MIT Lincoln Laboratory IBM Thomas J. Watson Research Center Rheinishes Landes Museum Bonn Germany Swiss Institutes of Technology Zurich Switzerland Swiss Institutes of Technology Lausanne S

We study the problem of representing images within a multimedia Database Management System (DBMS), in order to support fast retrieval operations without compromising storage efficiency. To achieve this goal, we propose new image coding techniques which combine a wavelet representation, embedded coding of the wavelet coefficients, and segmentation of image-domain regions in the wavelet domain. A bitstream is generated in which each image region is encoded independently of other regions, without having to explicitly store information describing the regions. Simulation results show that our proposed algorithms achieve coding performance which compares favorably, both perceptually and objectively, to that achieved using state-of-the-art image/video coding techniques while additionally providing region-based support.

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A Recent Survey of Vision Transformers for Medical image Segmentation

arXiv

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arXiv 2023年

作者： Khan, Asifullah Rauf, Zunaira Khan, Abdul Rehman Rathore, Saima Khan, Saddam Hussain Shah, Sahar Farooq, Umair Asif, Hifsa Asif, Aqsa Zahoora, Umme Khalil, Rafi Ullah Qamar, Suleman Asif, Umme Hani Khan, Faiza Babar Majid, Abdul Gwak, Jeonghwan Pattern Recognition Lab Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Center for Mathematical Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Avid Radio pharmaceuticals PhiladelphiaPA United States Eli Lilly and Company IndianapolisIN United States Swat Pakistan Air University E-9 Islamabad44230 Pakistan Digital Image Processing Lab Department of Computer Science Islamia College Peshawar Pakistan Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Department of Software Korea National University of Transportation Chungju27469 Korea Republic of

Medical image segmentation plays a crucial role in various healthcare applications, enabling accurate diagnosis, treatment planning, and disease monitoring. In recent years, Vision Transformers (ViTs) have emerged as a promising technique for addressing the challenges in medical image segmentation. In medical images, structures are usually highly interconnected and globally distributed. ViTs utilize their multi-scale attention mechanism to model the long-range relationships in the images. However, they do lack image-related inductive bias and translational invariance, potentially impacting their performance. Recently, researchers have come up with various ViT-based approaches that incorporate CNNs in their architectures, known as Hybrid Vision Transformers (HVTs) to capture local correlation in addition to the globa l information in the images. This survey paper provides a detailed review of the recent advancements in ViTs and HVTs for medical image segmentation. Along with the categorization of ViT and HVT-based medical image segmentation approaches we also present a detailed overview of their real-time applications in several medical image modalities. This survey may serve as a valuable resource for researchers, healthcare practitioners, and students in understanding the state-of-the-art approaches for ViT-based medical image segmentation. Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

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数字媒体技术专业实训规划的探讨

数字媒体技术专业实训规划的探讨

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第21届全国计算机新科技与计算机教育学术大会

作者： YANG Xinxin 杨欣欣 ZHOU Bing 周兵 MIAO Ru 苗茹 School of Computer and Information Engineering Henan University Kaifeng 475004 河南大学计算机与信息工程学院开封475004 Institute of Image Processing and Pattern Recognition Henan University Kaifeng 475004 河南大学图像处理与模式识别研究所开封475004

本文以河南大学计算机与信息工程学院的数字媒体技术专业的校内实训为例，探讨了如何对数字媒体技术专业进行实训。本文首先论述了实训规划，并以视频短片为例子进行了详细说明。其次通过实训效果和实训经验的总结，得出了下一步工作应... 详细信息

本文以河南大学计算机与信息工程学院的数字媒体技术专业的校内实训为例，探讨了如何对数字媒体技术专业进行实训。本文首先论述了实训规划，并以视频短片为例子进行了详细说明。其次通过实训效果和实训经验的总结，得出了下一步工作应该做些什么的结论。

关键词：高等院校数字媒体技术专业视频教学实训效果

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Microscopic Halftone image Segmentation

引用

Journal of Donghua University(English Edition) 2004年第5期000卷

作者： WANG Yong-gang YANG Jie DING Yong-sheng WANG Yong-gang(Institute of Image Processing & pattern Recognition Shanghai Jiaotong University Shanghai 200030) YANG Jie(College of Information Science and Technology Donghua University Shanghai 200051 Digitized Textile & Fashion Technology Engineering Research Center of National Education Ministry Donghua University Shanghai 200051)

Microscopic halftone image recognition and analysis can provide quantitative evidence for printing quality control and fault diagnosis of printing devices, while halftone image segmentation is one of the significant steps during the procedure. Automatic segmentation on microscopic dots by the aid of the Fuzzy C-Means (FCM) method that takes account of the fuzziness of halftone image and utilizes its color information adequately is realized. Then some examples show the technique effective and simple with better performance of noise immunity than some usual methods. In addition, the segmentation results obtained by the FCM in different color spaces are compared, which indicates that the method using the FCM in the f1f2f3 color space is superior to the rest.

关键词： image segmentation color halftone printing FCM color space

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Design and implementation of a clock recovery circuit for fast Ethernet applications

引用

Chinese Journal of Systems Engineering and Electronics 2004年第4期000卷

A circuit architechure to realize clock recovery for fast Ethernet applications is presented, whick includies system architecture, modified Mueller Muller algorithm for 100BASE-TX, phase detector for 100BASE-TX and multiple output charge pump PLL. The clock recovery circuit is verified by TSMC 0.35um 1P5M CMOS process. The results show that this clock recovery circuit exactly extracts the timing information. It has advantages over others for simple and easy implementation.

关键词： clock recovery phase locked loop adaptive equalizer

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Preface

引用

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2001年 2130卷 v页

作者： Dorffner, Georg Bischof, Horst Hornik, Kurt University of Vienna Dept. of Mecidal Cybernetics and Artificial Intelligence Freyung 6/2 Vienna Austria Technical University of Vienna Institute for Computer Aided Automation Pattern Recognition and Image Processing Group Favoritenstr. 9/1832 Vienna Austria Wirtschaftsuniversität Wien Institut für Statistik Augasse 2-6 Wien Austria

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Cross-receptive Focused Inference Network for Lightweight image Super-Resolution

arXiv

引用

arXiv 2022年

作者： Li, Wenjie Li, Juncheng Gao, Guangwei Deng, Weihong Zhou, Jiantao Yang, Jian Qi, Guo-Jun The Intelligent Visual Information Perception Laboratory Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210046 China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China The School of Communication and Information Engineering Shanghai University Shanghai200444 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210094 China The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China The Research Center for Industries of the Future The School of Engineering Westlake University Hangzhou310024 China OPPO Research SeattleWA98101 United States

Recently, Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks due to the ability of global feature extraction. However, the capabilities of Transformers that need to incorporate contextual information to extract features dynamically are neglected. To address this issue, we propose a lightweight Cross-receptive Focused Inference Network (CFIN) that consists of a cascade of CT Blocks mixed with CNN and Transformer. Specifically, in the CT block, we first propose a CNN-based Cross-Scale Information Aggregation Module (CIAM) to enable the model to better focus on potentially helpful information to improve the efficiency of the Transformer phase. Then, we design a novel Cross-receptive Field Guided Transformer (CFGT) to enable the selection of contextual information required for reconstruction by using a modulated convolutional kernel that understands the current semantic information and exploits the information interaction within different self-attention. Extensive experiments have shown that our proposed CFIN can effectively reconstruct images using contextual information, and it can strike a good balance between computational cost and model performance as an efficient model. Source codes will be available at https://***/IVIPLab/CFIN. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

引用

arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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A FAST RADIO BURST DISCOVERED IN FAST DRIFT SCAN SURVEY

arXiv

引用

arXiv 2020年

作者： Zhu, Weiwei Li, Di Luo, Rui Miao, Chenchen Zhang, Bing Spitler, Laura Lorimer, Duncan Kramer, Michael Champion, David Yue, Youling Cameron, Andrew Cruces, Marilyn Duan, Ran Feng, Yi Han, Jun Hobbs, George Niu, Chenhui Niu, Jiarui Pan, Zhichen Qian, Lei Shi, Dai Tang, Ningyu Wang, Pei Wang, Hongfeng Yuan, Mao Zhang, Lei Zhang, Xinxin Cao, Shuyun Feng, Li Gan, Hengqian Gao, Long Gu, Xuedong Guo, Minglei Hao, Qiaoli Huang, Lin Huang, Menglin Jiang, Peng Jin, Chengjin Li, Hui Li, Qi Li, Qisheng Liu, Hongfei Pan, Gaofeng Peng, Bo Qian, Hui Shi, Xiangwei Song, Jinyuo Song, Liqiang Sun, Caihong Sun, Jinghai Wang, Hong Wang, Qiming Wang, Yi Xie, Xiaoyao Yan, Jun Yang, Li Yang, Shimo Yao, Rui Yu, Dongjun Yu, Jinglong Zhang, Chengmin Zhang, Haiyan Zhang, Shuxin Zheng, Xiaonian Zhou, Aiying Zhu, Boqin Zhu, Lichun Zhu, Ming Zhu, Wenbai Zhu, Yan CAS Key Laboratory of FAST NAOC Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China CSIRO Astronomy and Space Science PO Box 76 EppingNSW1710 Australia Department of Physics and Astronomy University of Nevada Las Vegas Las VegasNV89154 United States National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China Max-Planck-Institut fr Radioastronomie Auf dem Hgel 69 BonnD-53121 Germany Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology Chestnut Ridge Research Building MorgantownWV26505 United States Image Processing and Pattern Recognition Laboratory School of Artificial Intelligence Beijing Normal University Beijing100875 China School of Information Management Dezhou University Dezhou253023 China Institute for Astronomical Science Dezhou University Dezhou253023 China

We report the discovery of a highly dispersed fast radio burst, FRB 181123, from an analysis of ∼1500 hr of drift-scan survey data taken using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The pulse has three distinct emission components, which vary with frequency across our 1.0–1.5 GHz observing band. We measure the peak flux density to be > 0.065 Jy and the corresponding fluence > 0.2 Jy ms. Based on the observed dispersion measure of 1812 cm−3 pc, we infer a redshift of ∼ 1.9. From this, we estimate the peak luminosity and isotropic energy to be . 2 × 1043 erg s−1 and . 2 × 1040 erg, respectively. With only one FRB from the survey detected so far, our constraints on the event rate are limited. We derive a 95% confidence lower limit for the event rate of 900 FRBs per day for FRBs with fluences > 0.025 Jy ms. We performed follow-up observations of the source with FAST for four hours and have not found a repeated burst. We discuss the implications of this discovery for our understanding of the physical mechanisms of FRBs. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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Current Progress and Challenges in Large-scale 3D Mitochondria Instance Segmentation

TechRxiv

引用

TechRxiv 2023年

作者： Franco-Barranco, Daniel Lin, Zudi Jang, Won-Dong Wang, Xueying Shen, Qijia Yin, Wenjie Fan, Yutian Li, Mingxing Chen, Chang Xiong, Zhiwei Xin, Rui Liu, Hao Chen, Huai Li, Zhili Zhao, Jie Chen, Xuejin Pape, Constantin Conrad, Ryan De Folter, Jozefus Nightingale, Luke Jones, Martin L. Liu, Yanling Ziaei, Dorsa Huschauer, Stephan Arganda-Carreras, Ignacio Pfister, Hanspeter Wei, Donglai The Department of Computer Science and Artificial Intelligence University of the Basque Country Donostia-San Sebastian Spain San Sebastian Spain Ikerbasque Basque Foundation for Science Bilbao Spain Biofisika Institute CSIC UPV/EHU Bilbao Spain Harvard University All-ston MA United States The Department of Molecular and Cellular Biology Harvard University CambridgeMA United States The Wellcome Centre for Integrative Neuroimaging FMRIB Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom University of Science and Technology of China Anhui China The Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai China The National Engineering Laboratory for Brain-inspired Intelligence Technology and Application University of Science and Technology of China Anhui China The Georg-August University Goettingen Germany The Center for Molecular Microscopy Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda United States The Cancer Research Technology Program Frederick National Laboratory for Cancer Research Frederick United States The Francis Crick Institute London United Kingdom The Advanced Biomedical Computational Science Group Frederick National Laboratory for Cancer Research FrederickMD United States The Computer Science Department Boston College Chestnut Hill MA United States

In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI 2021 conference. Our benchmark dataset consists of two large-scale 3D volumes, one from human and one from rat cortex tissue, which are 3,600 times larger than previously used datasets. At the time of paper submission, 257 participants had registered for the challenge, 14 teams had submitted their results, and six teams participated in the challenge workshop. Here, we present eight top-performing approaches from the challenge participants, along with our own baseline strategies. Posterior to the challenge, annotation errors in the ground truth were corrected without altering the final ranking. Additionally, we present a retrospective evaluation of the scoring system which revealed that (1) the challenge metric was permissive with the false positive predictions and (2) the size-based grouping of instances did not correctly categorize mitochondria of interest. Thus, we propose a new scoring system that better reflects the correctness of the segmentation results. Although several of the top methods are compared favorably to our own baselines, substantial errors remain unsolved for mitochondria with challenging morphologies. Thus, the challenge remains open for submission and automatic evaluation, with all volumes available for download. © 2023, CC BY.

关键词： Electron microscopy

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