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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Non-bayesian social learning with uncertain models

arXiv

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arXiv 2019年

作者： Hare, James Z. Uribe, César A. Kaplan, Lance Jadbabaie, Ali Signal and Image processing branch US Army Research Laboratory AdelphiMD20783 United States Laboratory for Information and Decision Systems Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States

Non-Bayesian social learning theory provides a framework that models distributed inference for a group of agents interacting over a social network. In this framework, each agent iteratively forms and communicates beliefs about an unknown state of the world with their neighbors using a learning rule. Existing approaches assume agents have access to precise statistical models (in the form of likelihoods) for the state of the world. However in many situations, such models must be learned from finite data. We propose a social learning rule that takes into account uncertainty in the statistical models using second-order probabilities. Therefore, beliefs derived from uncertain models are sensitive to the amount of past evidence collected for each hypothesis. We characterize how well the hypotheses can be tested on a social network, as consistent or not with the state of the world. We explicitly show the dependency of the generated beliefs with respect to the amount of prior evidence. Moreover, as the amount of prior evidence goes to infinity, learning occurs and is consistent with traditional social learning theory. Copyright © 2019, The Authors. All rights reserved.

关键词： Bayesian networks

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Applying of Luneburg lens for multi-beam TEM-horn antenna

Applying of Luneburg lens for multi-beam TEM-horn antenna

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IET International Radar Conference 2018, IRC 2018

作者： Bobreshov, Anatoly M. Uskov, Grigory K. Lysenko, Nikolay A. Sbitnev, Nikita S. Potapov, Alexander A. Voronezh Russia V.A. Kotel'nikov Institute of Radio Engineering and Electronics Russian Academy of Sciences Moscow Russia JNU-IREE Joint Laboratory of Fractal Method and Signal Processing Department of Electronic Engineering College of Information Science and Technology JiNan University Guangzhou China

Here, a multi-beam TEM-horn antenna with beam direction is proposed. Presented construction allows obtaining scanning beam and complicated antenna patterns, thus it can be used as substitute of ultra-wideband phased a... 详细信息

关键词： Antenna phased arrays

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A reconfigurable system learning for data classification using parallel processing

A reconfigurable system learning for data classification usi...

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2015 International Conference on Artificial Intelligence, ICAI 2015 - WORLDCOMP 2015

作者： Moreira, E.M. Maciel, C.D. Moreira, E.M. Zanoni, F.S. Federal Institute of Education Science and Technology of São Paulo São João da Boa Vista São Paulo Brazil Signal Processing Laboratory Department of Electrical Engineering University of São Paulo São Carlos São Paulo Brazil Systems Engineering and Information Technology Institute Federal University of Itajubá Itajubá Minas Gerais Brazil

ISBN: (纸本)1601324073

This paper presents a System Learning with a task scheduler, which makes possible the utilization of several classification and validation methods, allowing the distribution of tasks between the module systems. This architecture is structured of such way that the classifications obtained through a specific technique can be reutilized in parallel by the same algorithm or by other techniques, producing new classifications through the refinement of the results achieved and expanding the use in databases with different characteristics. The results demonstrated that through a learning system, the complexity of the analysis of great databases is minimized, allowing to verify basis with different structures and to increase the methods applied in the analysis of each structure. It favors the comparison between the methodologies and provides more reliable results. © 2019 ICAI 2015 - WORLDCOMP 2015. All rights reserved.

关键词： Classification (of information)

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AIM 2020 Challenge on image Extreme Inpainting 16th

AIM 2020 Challenge on Image Extreme Inpainting

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Workshops held at the 16th European Conference on Computer Vision, ECCV 2020

作者： Ntavelis, Evangelos Romero, Andrés Bigdeli, Siavash Timofte, Radu Hui, Zheng Wang, Xiumei Gao, Xinbo Shin, Chajin Kim, Taeoh Son, Hanbin Lee, Sangyoun Li, Chao Li, Fu He, Dongliang Wen, Shilei Ding, Errui Bai, Mengmeng Li, Shuchen Zeng, Yu Lin, Zhe Yang, Jimei Zhang, Jianming Shechtman, Eli Lu, Huchuan Zeng, Weijian Ni, Haopeng Cai, Yiyang Li, Chenghua Xu, Dejia Wu, Haoning Han, Yu Nadim, Uddin S. M. Jang, Hae Woong Ahmed, Soikat Hasan Yoon, Jungmin Jung, Yong Ju Li, Chu-Tak Liu, Zhi-Song Wang, Li-Wen Siu, Wan-Chi Lun, Daniel P. K. Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Narang, Pratik Mandal, Murari Chauhan, Pranjal Singh Computer Vision Lab ETH Zürich Zürich Switzerland CSEM Neuchâtel Switzerland School of Electronic Engineering Xidian University Xi’an China Image and Video Pattern Recognition Laboratory School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Baidu Inc. Beijing China Beijing China Dalian University of Technology Dalian China Adobe San Jose United States Rensselaer Polytechnic Institute Troy United States Peking University Beijing China Lab Gachon University Seongnam Korea Republic of Centre for Multimedia Signal Processing Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong China Indian Institute of Technology Madras Chennai India BITS Pilani Pilani India MNIT Jaipur Jaipur India

ISBN: (纸本)9783030670696

This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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A Research on the Control System of High-Speed Homopolar Motor with Solid Rotor Based on Flywheel Energy Storage

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Complexity 2020年 2020卷

作者： Jing, Lili Yu, Yandong Xue, Xiaochuan Key Laboratory of High Speed Signal Processing and Internet of Things Technology Application Jining Normal University Ulanqab Inner Mongolia 012000 China Department of Computer Science Jining Normal University Ulanqab Inner Mongolia 012000 China Systems Engineering Research Institute of China State Shipbuilding Corporation Beijing 100191 China

In view of the defects of the motors used for flywheel energy storage such as great iron loss in rotation, poor rotor strength, and robustness, a new type of motor called electrically excited homopolar motor is adopted in this paper for flywheel energy storage. Compared to general motors, this motor has the advantages of simple structure, high rotor strength, and low iron loss in rotation. A double closed-loop PI governing system of the new motor was designed, modeled, and simulated with this motor as the controlled object on simulation platform. The simulation result shows that the PI-controlled electrically excited homopolar motor system realized fast speed governing and good system performance, achieved the purpose of quick charge by flywheel energy storage, verified the correctness and effectiveness of the design scheme, and provided data reference and theoretical basis for the practice of future flywheel energy storage experiments. © 2020 Lili Jing et al.

关键词： Digital storage

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Estimating crop primary productivity with sentinel-2 and landsat 8 using machine learning methods trained with radiative transfer simulations

arXiv

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arXiv 2020年

作者： Wolanin, Aleksandra Camps-Valls, Gustau Gómez-Chova, Luis Mateo-García, Gonzalo van der Tol, Christiaan Zhang, Yongguang Guanter, Luis Section 1.4 Remote Sensing GFZ German Research Centre for Geosciences Helmholtz-Centre Potsdam Germany Image Processing Laboratory Universitat de València València Spain Faculty of ITC University of Twente Enschede Netherlands Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System Sciences Nanjing University Nanjing210023 China

Satellite remote sensing has been widely used in the last decades for agricultural applications, both for assessing vegetation condition and for subsequent yield prediction. Existing remote sensing-based methods to estimate gross primary productivity (GPP), which is an important variable to indicate crop photosynthetic function and stress, typically rely on empirical or semi-empirical approaches, which tend to over-simplify photosynthetic mechanisms. In this work, we take advantage of all parallel developments in mechanistic photosynthesis modeling and satellite data availability for an advanced monitoring of crop productivity. In particular, we combine process-based modeling with the soil-canopy energy balance radiative transfer model (SCOPE) with Sentinel-2 and Landsat 8 optical remote sensing data and machine learning methods in order to estimate crop GPP. With this approach, we by-pass the need for an intermediate step to retrieve the set of vegetation biophysical parameters needed to accurately model photosynthesis, while still accounting for the complex processes of the original physically-based model. Several implementations of the machine learning models are tested and validated using simulated and flux tower-based GPP data. Our final neural network model is able to estimate GPP at the tested flux tower sites with r2 of 0.92 and RMSE of 1.38 gC d−1 m−2, which outperforms empirical models based on vegetation indices. The first test of applicability of this model to Landsat 8 data showed good results (r2 of 0.82 and RMSE of 1.97 gC d−1 m−2), which suggests that our approach can be further applied to other sensors. Modeling and testing is restricted to C3 crops in this study, but can be extended to C4 crops by producing a new training dataset with SCOPE that accounts for the different photosynthetic pathways. Our model successfully estimates GPP across a variety of C3 crop types and environmental conditions even though it does not use any local information from

关键词： Photosynthesis

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Measurement of liquid flow rate among the annular flow in vertical tee junction

arXiv

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arXiv 2020年

作者： Huang, Shuo Fu, Tianyu Zhang, Guanmin Sun, Yu Laboratory of Signal and Image Processing School of Biological Sciences and Medical Engineering Southeast University Nanjing210096 China International Laboratory for Children's Medical Imaging Research School of Biological Sciences and Medical Engineering Southeast University Nanjing210096 China School of Energy and Power Engineering Shandong University Jinan250061 China Institute of Cancer and Genomic Science University of Birmingham BirminghamB15 2TT United Kingdom

Since the liquid flow rate of the annular flow is closely related to the heat exchange efficiency, it has great significance to measure the liquid flow rate of the annular flow in vertical tee junction. In order to acquire the liquid flow rate of the annular flow in vertical tee junction, a measurement method has been designed, which implements the digital subtraction method to measure the thickness of the liquid film under the visible light and to apply the image feature matching algorithm to obtain the liquid velocity field. Moreover, the accuracy of the liquid film velocity field as well as the spatial and temporal stability of the mass flow rate is tested by proposed algorithms in this study. Experimental results show that the measurement error of our method is approximately 5% in the lower section of the main pipe and the branch pipe, and lower than 15% in the upper section of the main pipe. Therefore, this method has a high accuracy in comparison with other measurement approaches. Our method can be applied to measure and analyse the shape and property of the annular flow in the vertical tee junction. Copyright © 2020, The Authors. All rights reserved.

关键词： Liquid films

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Range-segment Motion Compensation based on Integrated INS for Wide-swath Synthetic Aperture Sonar

Range-segment Motion Compensation based on Integrated INS fo...

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第二届先进电子材料、计算机与材料工程国际学术会议（AEMCME 2019）

作者： Yu Zhang Rongxing Zhong Jiyuan Liu Linzhe Wei Peng Wang Pengfei Zhang Institute of Acoustics Chinese Academy of Sciences Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing Chinese Academy of Sciences University of Chinese Academy of Sciences

Motion errors have severe effect on synthetic aperture sonar(SAS) imagery if worse than a fraction of a wavelength along the entire aperture. Not only currents, but vehicle instability randomly causes periodic motion errors in the synthetic aperture and grating lobes in the SAS images as well. Displaced phase center antenna referred to as micro-navigation has been developed with multiple-receiver SAS for a long time, but still has challenges against larger motion errors. The integrated inertial navigation system comprising an inertial navigation system(INS) and other aided sensors such as a Doppler Velocity Log(DVL) and a surface ship's Global Position System(GPS) is an unavoidable and trade-off solution to the problem of motion estimate, which is definitely more robust and reliable with the application of the multi-sensor data fusion. Besides, the motion error is variant along the slant range. For a better resolution of the wide-swath SAS, a range-segment motion compensation method based on the integrated inertial navigation system is presented in the paper. This method relies on the multi-sensor data fusion of the integrated inertial navigation system via Kalman filter and is found to significantly outperform the conventional method.

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Multi-adapter RGBT tracking

arXiv

引用

arXiv 2019年

作者： Li, Chenglong Lu, Andong Zheng, Aihua Tu, Zhengzheng Tang, Jin School of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Industrial Image Processing and Analysis of Anhui Province Institute of Physical Science and Information Technology Anhui University Hefei230601 China

The task of RGBT tracking aims to take the complementary advantages from visible spectrum and thermal infrared data to achieve robust visual tracking, and receives more and more attention in recent years. Existing works focus on modality-specific information integration by introducing modality weights to achieve adaptive fusion or learning robust feature representations of different modalities. Although these methods could effectively deploy the modality-specific properties, they ignore the potential values of modality-shared cues as well as instance-aware information, which are crucial for effective fusion of different modalities in RGBT tracking. In this paper, we propose a novel Multi-Adapter convolutional Network (MANet) to jointly perform modality-shared, modality-specific and instance-aware feature learning in an end-to-end trained deep framework for RGBT tracking. We design three kinds of adapters within our network. In a specific, the generality adapter is to extract shared object representations, the modality adapter aims at encoding modality-specific information to deploy their complementary advantages, and the instance adapter is to model the appearance properties and temporal variations of a certain object. Moreover, to reduce computational complexity for real-time demand of visual tracking, we design a parallel structure of generic adapter and modality adapter. Extensive experiments on two RGBT tracking benchmark datasets demonstrate the outstanding performance of the proposed tracker against other state-of-the-art RGB and RGBT tracking algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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