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arXiv 2019年

作者： Wei, Xian Shen, Hao Li, Yuanxiang Tang, Xuan Jin, Bo Zhao, Lijun Murphey, Yi Lu Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences China Technical University of Munich Germany and fortiss GmbH Munich Germany Shanghai Key Lab for Trustworthy Computing School of Computer Science and Software Engineering East China Normal University China School of Aeronautics & Astronautics Shanghai Jiao Tong University Shanghai200240 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150006 China Department of Electrical and Computer Engineering University of Michigan-Dearborn DearbornMI48128 United States

This work investigates the problem of efficiently learning discriminative low-dimensional representations of multi-class large-scale image objects. We propose a generic deep learning approach by taking advantages of Convolutional Neural Networks (CNN), sparse dictionary learning, and orthogonal projections. CNN is not only powerful on feature extraction, but also robust to spatial variance and changes. Sparse dictionary learning is well known for disentangling nonlinear underlying discriminative factors in data. The orthogonal projection is a notable efficient tool to project multi-class data onto low dimensional discriminative subspace. The proposed procedure can be summarized as follows. At first, a CNN is employed to extract high-dimensional (HD) preliminary convolutional features. Secondly, to avoid the high computational cost of direct sparse coding on HD CNN features, we present to learn sparse representation (SR) in an orthogonal projected space over a task-driven sparsifying dictionary. We then exploit the discriminative projection on SR. The whole learning process is treated as a joint optimization problem of trace quotient maximization, which involves the CNN parameters, the orthogonal projection on CNN features, the dictionary and the discriminative projection on sparse codes. The related cost function is well defined on product manifold of the Stiefel manifold, the Oblique manifold, and the Grassmann manifold. It is optimized via a geometrical stochastic gradient descent algorithm. Finally, the quality of dictionary and projections learned by the proposed approach is further investigated in terms of image classification. The experimental results show that the approach can achieve a strongly competitive performance with state-of-the-art image classification methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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Aggregation Signature for Small Object Tracking

arXiv

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arXiv 2019年

作者： Liu, Chunlei Ding, Wenrui Yang, Jinyu Murino, Vittorio Zhang, Baochang Han, Jungong Guo, Guodong School of Electrical and Information Engineering Beihang University Beijing China Unmanned System Research Institute Beihang University Beijing China School of Computer Science University of Birmingham British United Kingdom University of Verona Verona Italy Pattern Analysis and Computer Vision department Istituto Italiano di Tecnologia Genoa Italy School of Automation Science and Electrical Engineering Beihang University Beijing China Shenzhen Academy of Aerospace Technology Shenzhen China WMG Data Science Group University of Warwick CoventryCV4 7AL United Kingdom Institute of Deep Learning Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application

—Small object tracking becomes an increasingly important task, which however has been largely unexplored in computer vision. The great challenges stem from the facts that: 1) small objects show extreme vague and variable appearances, and 2) they tend to be lost easier as compared to normal-sized ones due to the shaking of lens. In this paper, we propose a novel aggregation signature suitable for small object tracking, especially aiming for the challenge of sudden and large drift. We make three-fold contributions in this work. First, technically, we propose a new descriptor, named aggregation signature, based on saliency, able to represent highly distinctive features for small objects. Second, theoretically, we prove that the proposed signature matches the foreground object more accurately with a high probability. Third, experimentally, the aggregation signature achieves a high performance on multiple datasets, outperforming the state-of-the-art methods by large margins. Moreover, we contribute with two newly collected benchmark datasets, i.e., small90 and small112, for visually small object tracking. The datasets will be available in https://***/bczhangbczhang/. Copyright © 2019, The Authors. All rights reserved.

关键词： Large dataset

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Human intention estimation based on hidden Markov model motion validation for safe flexible robotized warehouses

arXiv

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arXiv 2018年

作者： Petković, Tomislav Puljiz, David Marković, Ivan Hein, Björn University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Laboratory for Autonomous Systems and Mobile Robotics Unska 3 ZagrebHR-10000 Croatia Karlsruhe Institute of Technology Institute for Anthropomatics and Robotics Intelligent Process Automation and Robotics Lab Kaiserstraße 12 Deutschland KarlsruheDE-76131

With the substantial growth of logistics businesses the need for larger warehouses and their automation arises, thus using robots as assistants to human workers is becoming a priority. In order to operate efficiently and safely, robot assistants or the supervising system should recognize human intentions in real-time. Theory of mind (ToM) is an intuitive human conception of other humans' mental state, i.e., beliefs and desires, and how they cause behavior. In this paper we propose a ToM based human intention estimation algorithm for flexible robotized warehouses. We observe human's, i.e., worker's motion and validate it with respect to the goal locations using generalized Voronoi diagram based path planning. These observations are then processed by the proposed hidden Markov model framework which estimates worker intentions in an online manner, capable of handling changing environments. To test the proposed intention estimation we ran experiments in a real-world laboratory warehouse with a worker wearing Microsoft Hololens augmented reality glasses. Furthermore, in order to demonstrate the scalability of the approach to larger warehouses, we propose to use virtual reality digital warehouse twins in order to realistically simulate worker behavior. We conducted intention estimation experiments in the larger warehouse digital twin with up to 24 running robots. We demonstrate that the proposed framework estimates warehouse worker intentions precisely and in the end we discuss the experimental results. Copyright © 2018, The Authors. All rights reserved.

关键词： Virtual reality

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Federated Learning Enables Big Data for Rare Cancer Boundary Detection

arXiv

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arXiv 2022年

作者： Pati, Sarthak Baid, Ujjwal Edwards, Brandon Sheller, Micah Wang, Shih-Han Reina, G. Anthony Foley, Patrick Gruzdev, Alexey Karkada, Deepthi Davatzikos, Christos Sako, Chiharu Ghodasara, Satyam Bilello, Michel Mohan, Suyash Vollmuth, Philipp Brugnara, Gianluca Preetha, Chandrakanth J. Sahm, Felix Maier-Hein, Klaus Zenk, Maximilian Bendszus, Martin Wick, Wolfgang Calabrese, Evan Rudie, Jeffrey Villanueva-Meyer, Javier Cha, Soonmee Ingalhalikar, Madhura Jadhav, Manali Pandey, Umang Saini, Jitender Garrett, John Larson, Matthew Jeraj, Robert Currie, Stuart Frood, Russell Fatania, Kavi Huang, Raymond Y. Chang, Ken Balana, Carmen Capellades, Jaume Puig, Josep Trenkler, Johannes Pichler, Josef Necker, Georg Haunschmidt, Andreas Meckel, Stephan Shukla, Gaurav Liem, Spencer Alexander, Gregory S. Lombardo, Joseph Palmer, Joshua D. Flanders, Adam E. Dicker, Adam P. Sair, Haris I. Jones, Craig K. Venkataraman, Archana Jiang, Meirui So, Tiffany Y. Chen, Cheng Heng, Pheng Ann Dou, Qi Kozubek, Michal Lux, Filip Michálek, Jan Matula, Petr Keřkovský, Miloš Kopřivová, Tereza Dostál, Marek Vybíhal, Václav Vogelbaum, Michael A. Mitchell, J. Ross Farinhas, Joaquim Maldjian, Joseph A. Yogananda, Chandan Ganesh Bangalore Pinho, Marco C. Reddy, Divya Holcomb, James Wagner, Benjamin C. Ellingson, Benjamin M. Cloughesy, Timothy F. Raymond, Catalina Oughourlian, Talia Hagiwara, Akifumi Wang, Chencai To, Minh-Son Bhardwaj, Sargam Chong, Chee Agzarian, Marc Falcão, Alexandre Xavier Martins, Samuel B. Teixeira, Bernardo C.A. Sprenger, Flávia Menotti, David Lucio, Diego R. LaMontagne, Pamela Marcus, Daniel Wiestler, Benedikt Kofler, Florian Ezhov, Ivan Metz, Marie Jain, Rajan Lee, Matthew Lui, Yvonne W. McKinley, Richard Slotboom, Johannes Radojewski, Piotr Meier, Raphael Wiest, Roland Derrick, Murcia Fu, Eric Haas, Rourke Thompson, John Ormond, David Ryan Badve, Chaitra Sloan, Andrew E. Vadmal, Vachan Waite, Kristin Colen, Rivka R. Pei, Linmin Ak, Murat Srinivasan, Ashok Bapuraj, J. Rajiv Rao, Arvind Wang, Nicholas Yoshiaki, Ota Moritani, Tos University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University of Munich Bavaria Munich Germany Intel Corporation Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Heidelberg Germany Department of Neuropathology Heidelberg University Hospital Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Neurology Clinic Heidelberg University Hospital Heidelberg Germany Department of Radiology & Biomedical Imaging University of California San Francisco San FranciscoCA United States Symbiosis Center for Medical Image Analysis Symbiosis International University Maharashtra Pune India Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Karnataka Bangalore India Department of Radiology School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Medical Physics School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Neuroradiology Clinical Neuroscience Center University Hospital Zurich University of Zurich Zurich Switzerland Leeds Teaching Hospitals Trust Department of Radiology Leeds United Kingdom Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Catalan Institute of Oncology Badalona Spain Consorci MAR Parc de Salut de Barcelona Catalonia Spain Josep Trueta University Hospital Girona Spain

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing. © 2022, CC BY.

关键词： Tumors

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Comparison of brain connectomes using geodesic distance on manifold: A twins study

arXiv

引用

arXiv 2019年

作者： Yamin, A. Dayan, M. Squarcina, L. Brambilla, P. Murino, V. Diwadkar, V. Sona, D. Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture Università degli Studi di Genova Italy Scientific Institute IRCCS "E. Medea" Bosisio Parini Italy Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico Università di Milano Italy Department of Computer Science Università di Verona Italy Department of Psychiatry & Behavioral Neuroscience Wayne State University DetroitMI United States Neuroinformatics Laboratory Fondazione Bruno Kessler Trento Italy

fMRI is a unique non-invasive approach for understanding the functional organization of the human brain, and task-based fMRI promotes identification of functionally relevant brain regions associated with a given task. Here, we use fMRI (using the Poffenberger Paradigm) data collected in mono- and dizygotic twin pairs to propose a novel approach for assessing similarity in functional networks. In particular, we compared network similarity between pairs of twins in task-relevant and task-orthogonal networks. The proposed method measures the similarity between functional networks using a geodesic distance between graph Laplacians. With method we show that networks are more similar in monozygotic twins compared to dizygotic twins. Furthermore, the similarity in monozygotic twins is higher for task-relevant, than task-orthogonal networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Brain

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LaG-DESIQUE: A local-and-global blind image quality evaluator without training on human opinion scores 6th

LaG-DESIQUE: A local-and-global blind image quality evaluato...

引用

6th CCF Academic Conference on Big Data, CCF Big Data 2018

作者： Liu, Ruyi Zhang, Yi Chandler, Damon M. Miao, Qiguang Liu, Tiange School of Computer Science and Technology Xidian University XianShaanxi710071 China Xian Key Laboratory of Big Data and Intelligent Vision XianShaanxi710071 China School of Electronic and Information Engineering Xian Jiaotong University XianShaanxi710049 China Department of Electrical and Electronic Engineering Shizuoka University HamamatsuShizuoka432-8561 Japan School of Information Science and Engingeering Yanshan University QinhuangdaoHebei066004 China

ISBN: (纸本)9789811329210

This paper extends our previous DESIQUE [1] algorithm to a local-and-global way (LaG-DESIQUE) to blindly measure image quality without training on human opinion scores. The local DESIQUE extracts block-based log-derivative features and evaluates image quality through measuring the multivariate Gaussian distance between selected natural and test image patches. The global DESIQUE extracts image-based log-derivative features and image quality is estimated based on a two-stage framework, which was trained on a set of regenerated distorted images with their quality scores estimated by MAD [2] algorithm. The overall quality is the weighted average of local and global DESIQUE scores. Test on several image databases demonstrates that LaG-DESIQUE performs competitively well in predicting image quality. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Image quality

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New design techniques for globally convergent simultaneous localization and mapping: Analysis and implementation

引用

Workshop on Sensing and Control for Autonomous Vehicles: Applications to Land, Water and Air Vehicles, 2017

作者： Lourenço, Pedro Guerreiro, Bruno Batista, Pedro Oliveira, Paulo Silvestre, Carlos Laboratory for Robotics and Engineering Systems Institute for Systems and Robotics Lisbon Portugal Department of Electrical and Computer Engineering Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal Department of Mechanical Engineering Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China

ISBN: (纸本)9783319553719

This chapter presents an overview of algorithms deeply rooted in a sensor-based approach to the SLAM problem that provide global convergence guarantees and allow for the use of partially observable landmarks. The presented algorithms address the more usual range-and-bearing SLAM problem, either in 2-D using a LiDAR or in 3-D using an RGB-D camera, as well as the range-only and bearing-only SLAM problems. For each of these formulations a nonlinear system is designed, for which state and output transformations are considered together with augmented dynamics, in such a way that the underlying system structure can be regarded as linear time-varying for observability analysis and filter design purposes. This naturally allows for the design of Kalman filters with, at least, globally asymptotically stable error dynamics, for which several experimental and simulated trials are presented to highlight the performance and consistency of the obtained filters. © Springer International Publishing AG 2017.

关键词： Kalman filters

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Deep representation of industrial components using simulated images

Deep representation of industrial components using simulated...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Seong-Heum Kim Gyeongmin Choe Byungtae Ahn In So Kweon Robotics and Computer Vision Laboratory School of Electrical Engineering KAIST Daejeon Republic of Korea

In this paper, we present a visual learning framework to retrieve a 3D model and estimate its pose from a single image. To increase the quantity and quality of training data, we define our simulation space in the near infrared (NIR) band, and utilize the quasi-Monte Carlo (MC) method for scalable photorealistic rendering of manufactured components. Two types of convolutional neural network (CNN) architectures are trained over these synthetic data and a relatively small amount of real data. The first CNN model seeks the most discriminative information and uses it to classify industrial components with fine-grained shape attributes. Once a 3D model is identified, one of the category-specific CNNs is tested for pose regression in the second phase. The mixed data for learning object categories is useful in domain adaptation and attention mechanism in our system. We validate our data-driven method with 88 component models, and the experimental results are qualitatively demonstrated. Also, the CNNs trained with various conditions of mixed data are quantitatively analyzed.

关键词： Three-dimensional displays Rendering (computer graphics) Computational modeling Solid modeling Shape Cameras Adaptation models

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Introduction

Advances in Industrial Control

引用

Advances in Industrial Control 2018年第9783319610207期 1-18页

作者： Orsag, Matko Korpela, Christopher Oh, Paul Bogdan, Stjepan Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Electrical Engineering and Computer Science United States Military Academy West PointNY United States Department of Mechanical Engineering University of Nevada Las Vegas Las VegasNV United States

Aerial robotics has reached significant maturity in the last few years. Thus, the number of new unmanned aerial vehicles (UAV) and unmanned aerial systems (UAS), applications, and companies producing them has increased in a very important way. The number of publications and presentations in conferences has also experienced a large increase. Moreover, almost every day we can find articles, videos, and news in the general media related to drones. © Springer International Publishing AG 2018.

关键词： Antennas

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Deep learning methods for lung cancer segmentation in whole-slide histopathology images - The ACDC@lungHP challenge 2019

arXiv

引用

arXiv 2020年

作者： Li, Zhang Zhang, Jiehua Tan, Tao Teng, Xichao Sun, Xiaoliang Li, Yang Liu, Lihong Xiao, Yang Lee, Byungjae Li, Yilong Zhang, Qianni Sun, Shujiao Zheng, Yushan Yan, Junyu Li, Ni Hong, Yiyu Ko, Junsu Jung, Hyun Liu, Yanling Chen, Yu-Cheng Wang, Ching-Wei Yurovskiy, Vladimir Maevskikh, Pavel Khanagha, Vahid Jiang, Yi Feng, Xiangjun Liu, Zhihong Li, Daiqiang Schüffler, Peter J. Yu, Qifeng Chen, Hui Tang, Yuling Litjens, Geert College of Aerospace Science and Engineering National University of Defense Technology Changsha410073 China Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands and ScreenPoint Medical Eindhoven Nijmegen5600 MB Netherlands Pingan Technology Shenzhen China Lunit Inc. Seoul Korea Republic of School of Electrical Engineering and Computer Science Queen Mary University of London London United Kingdom Image Processing Center School of Astronautics Beihang University Beijing102206 China Beijing Advanced Innovation Center for Biomedical Engineering Beijing100191 China Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Image Processing Center School of Astronautics Beihang University Beijing102206 China AstLab School of Automatationm Beihang University Beijing China Department of R&D Center Arontier Co. Ltd. Seoul Korea Republic of Frederick National Laboratory United States Center of Computer Vision and Medical Imaging Graduate Institute of Biomedical Engineering Graduate institute of Applied Science and Technology National Taiwan University of Science and Technology AI Explore Taipei Taiwan Research Department Sky-chain Global Yekaterinburg Russia Audio Solutions Team Motorola Solutions Inc. PlantationFL United States Second Xiangya Hospital Central South University Changsha China Hunan Biotechnology Ltd. Changsha China Hunan Cancer Hospital Central South University Changsha China Memorial Sloan Kettering Cancer Center United States First Hospital of Changsha City Changsha China Radboud University Medical Center Nijmegen Netherlands

Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) of cancer tissue in whole slide imaging (WSI), using an annotated dataset of 150 training images and 50 test images from 200 patients. This paper reviews this challenge and summarizes the top 10 submitted methods for lung cancer segmentation. All methods were evaluated using the false positive rate, false negative rate, and DICE coefficient (DC). The DC ranged from 0.7354±0.1149 to 0.8372±0.0858. The DC of the best method was close to the inter-observer agreement (0.8398±0.0890). All methods were based on deep learning and categorized into two groups: multi-model method and single model method. In general, multi-model methods were significantly better (p Copyright © 2020, The Authors. All rights reserved.

关键词： Biological organs

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