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Projected Sliced Wasserstein Autoencoder-based Hyperspectral Images Anomaly Detection

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arXiv 2021年

作者： Chen, Yurong Zhang, Hui Wang, Yaonan Wu, Q. M. Jonathan Yang, Yimin National Engineering Laboratory of Robot Visual Perception and Control Technology School of Robotics Hunan University Hunan Changsha410082 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B3P4 Canada Department of Computer Science Lakehead University ONP7B 5E1 Canada Vector Institute TorontoONM5G 1M1 Canada

Anomaly detection refers to identifying the observation that deviates from the normal pattern, which has been an active research area in various domains. Recently, the increasing data scale, complexity, and dimension turns the traditional representation and statistical-based outlier detection method into challenging. In this paper, we leverage the generative model in hyperspectral images anomaly detection. The gist is to model the distribution of the normal data, while the out-of-distribution sample can be viewed as the outlier. At first, the variational inference-based anomaly detection methods are investigated. We theoretically and empirically find that they are unstable due to the strong notion of distance (f-divergence) served as the regularization. Secondly, this paper introduces sliced Wasserstein distance, which is a weaker distribution measure compared with f-divergence. However, the number of randomly slicing poses a difficulty to estimate the true distance. In the end, we propose a projected sliced Wasserstein (PSW) autoencoder-based anomaly screening method. In particular, we leverage a computation-friendly eigen-decomposition method to find the principal component as slicing the high-dimensional data. Furthermore, our proposed distance can be calculated with the closed-form, even the prior distribution is not Gaussian. Comprehensive experiments conducted on various real-world hyperspectral anomaly detection benchmarks demonstrate the superior performance of our proposed method. © 2021, CC BY.

关键词： Spectroscopy

P-WAE: Generalized patch-wasserstein autoencoder for anomaly screening

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arXiv 2021年

作者： Zhang, Hui Chen, Yurong Wang, Yaonan Cao, Yihong Wu, Q.M. Jonathan Yang, Yimin National Engineering Laboratory of Robot Visual Perception and Control Technology School of Robotics Hunan University Changsha Hunan410082 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B3P4 Canada Department of Computer Science Lakehead University OntarioP7B 5E1 Canada Vector Institute TorontoM5G 1M1 Canada

To mitigate the inspector’s workload and improve the quality of the product, computer vision-based anomaly detection (AD) techniques are gradually deployed in real-world industrial scenarios. Recent anomaly analysis benchmarks progress to generative models. The aim is to model the defect-free distribution so that anomalies can be classified as out-of-distribution samples. Nevertheless, there are two disturbing factors that need researchers and deployers to prioritize: (i) the simplistic prior latent distribution inducing limited expressive capability;(ii) the collapsed mutual-dependent features resulting in poor generalization. In this paper, we propose a novel Patch-wise Wasserstein AutoEncoder (P-WAE) architecture to alleviate those challenges. In particular, a patch-wise variational inference model coupled with solving the jigsaw puzzle is designed, which is a simple yet effective way to increase the expressiveness and complexity of the latent manifold. This alleviates the blurry reconstruction problem. In addition, the Hilbert-Schmidt Independence Criterion (HSIC) bottleneck is introduced to constrain the over-regularization representation. Comprehensive experiments, conducted on the MVTec AD dataset, demonstrate the superior performance of our proposed method. © 2021, CC BY.

关键词： Anomaly detection

MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

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arXiv 2022年

作者： Li, Lei Wu, Fuping Wang, Sihan Luo, Xinzhe Martín-Isla, Carlos Zhai, Shuwei Zhang, Jianpeng Liu, Yanfei Zhang, Zhen Ankenbrand, Markus J. Jiang, Haochuan Zhang, Xiaoran Wang, Linhong Arega, Tewodros Weldebirhan Altunok, Elif Zhao, Zhou Li, Feiyan Ma, Jun Yang, Xiaoping Puybareau, Elodie Oksuz, Ilkay Bricq, Stephanie Li, Weisheng Punithakumar, Kumaradevan Tsaftaris, Sotirios A. Schreiber, Laura M. Yang, Mingjing Liu, Guocai Xia, Yong Wang, Guotai Escalera, Sergio Zhuang, Xiahai School of Data Science Fudan University Shanghai China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Departament de Matemàtiques & Informàtica Universitat de Barcelona Barcelona Spain Computer Vision Center Universitat Autònoma de Barcelona Spain School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering Northwestern Polytechnical University Xi'An China College of Electrical and Information Engineering Hunan University Changsha China National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha China College of Physics and Information Engineering Fuzhou University Fuzhou China Comprehensive Heart Failure Center Wuerzburg University Hospitals Wuerzburg Germany School of Engineering University of Edinburgh Edinburgh United Kingdom School of Robotics Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering University of California Los Angeles United States Department of Radiology and Diagnostic Imaging University of Alberta Edmonton Canada Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecomm-Unications Chongqing China ImViA Laboratory Université Bourgogne Franche-Comté Dijon France Computer Engineering Department Istanbul Technical University Istanbul Turkey Le Kremlin-Bicêtre France Department of Mathematics Nanjing University of Science and Technology Nanjing China Department of Mathematics Nanjing University Nanjing China

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore potential of solutions, as well as to provide a benchmark for future research. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. Note that MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (***/zhuangxiahai/0/myops20/). © 2022, CC BY-NC-SA.

关键词： Magnetic resonance imaging

Edge-Computing-Driven Autonomous Ubiquitous Internet of Things in Electricity: Architecture and Challenges

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Edge-Computing-Driven Autonomous Ubiquitous Internet of Thin...

IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Dongqi Liu Xiangjun Zeng Yaonan Wang Changsha Unviersity of Science & Technology Changsha P.R.China National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha P.R.China

ISBN: (数字)9781728131375

ISBN: (纸本)9781728131382

The distribution network is developing towards the direction of Internet of Things in Electricity(IoTE). As an emerging technology of the Internet of Things(IoT), edge computing has great application potential in the future IoTE. This paper presents an architecture of edge computing-driven IoTE, whose physical layer is an electrical network containing a high proportion of power electronic devices, and its information layer is a 5G-oriented power communication network containing a large amount of power terminal and mass communication data generated by the power terminal. For the physical layer, the virtual synchronous machine(VSM) algorithm are adopted to control the large number of power electronic components so as to guarantee the dynamic stability of physical power network. For the information layer, a new layered distributed information processing architecture, the Edge Computing Architecture, is introduced to address the needs of information monitoring and protection control in the smart distribution network. The architecture of the edge computing, and the applications that may be deployed in the edge computing gateways are introduced, and the challenges may be faced by the IoTE in the future development are further discussed.

关键词： Edge computing Power system stability Computer architecture Power electronics Logic gates Internet of Things Monitoring

Hierarchical Task Impedance control of a Serial Manipulator for Minimally Invasive Surgery

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Hierarchical Task Impedance Control of a Serial Manipulator ...

IEEE International Conference on Human-Machine Systems

作者： Hang Su Chenguang Yang Jiehao Li Yiming Jiang Giancarlo Ferrigno Elena De Momi Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Bristol Robotics Laboratory University of the West of England Bristol United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China National Engineering Laboratory for Robot Visual Perception and Control Hunan University Changsha China Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milano Italy

ISBN: (数字)9781728158716

ISBN: (纸本)9781728158723

Flexibility and robustness have become key points in the development of surgical robot controller for physical interactions. However, the conventional impedance control schemes unaware of the actual surgical scenario, including complex physical interaction on the robot arm, lead to the loss of accuracy. In this paper, a hierarchical task impedance control scheme is proposed for Minimally Invasive Surgery (MIS) based on an operational space formulation of a 7 DoFs redundant robot. Its redundancy is exploited to guarantee a remote center of motion (RCM) constraint and to provide a flexible workspace for the medical staff to assist physicians. In addition to the achievement of the classical whole-body impedance control, the issue of uncertain disturbances will be addressed by a decoupled adaptive approximation based on a radial basis function neural network (RBFNN) within the control framework. Task performances under the hierarchical task impedance controller were validated and compared with previous work in the literature. Experimental results showed its improved performance in terms of positional error and RCM constraint, regardless of the existing uncertain physical interaction.

关键词： Task analysis Impedance Manipulators Tools Surgery Jacobian matrices

SoccerNet 2023 Challenges Results

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arXiv 2023年

作者： Cioppa, Anthony Giancola, Silvio Somers, Vladimir Magera, Floriane Zhou, Xin Mkhallati, Hassan Deliège, Adrien Held, Jan Hinojosa, Carlos Mansourian, Amir M. Miralles, Pierre Barnich, Olivier De Vleeschouwer, Christophe Alahi, Alexandre Ghanem, Bernard Van Droogenbroeck, Marc Kamal, Abdullah Maglo, Adrien Clapés, Albert Abdelaziz, Amr Xarles, Artur Orcesi, Astrid Scott, Atom Liu, Bin Lim, Byoungkwon Chen, Chen Deuser, Fabian Yan, Feng Yu, Fufu Shitrit, Gal Wang, Guanshuo Choi, Gyusik Kim, Hankyul Guo, Hao Fahrudin, Hasby Koguchi, Hidenari Ardö, Håkan Salah, Ibrahim Yerushalmy, Ido Muhammad, Iftikar Uchida, Ikuma Be'ery, Ishay Rabarisoa, Jaonary Lee, Jeongae Fu, Jiajun Yin, Jianqin Xu, Jinghang Nang, Jongho Denize, Julien Li, Junjie Zhang, Junpei Kim, Juntae Synowiec, Kamil Kobayashi, Kenji Zhang, Kexin Habel, Konrad Nakajima, Kota Jiao, Licheng Ma, Lin Wang, Lizhi Wang, Luping Li, Menglong Zhou, Mengying Nasr, Mohamed Abdelwahed, Mohamed Liashuha, Mykola Falaleev, Nikolay Oswald, Norbert Jia, Qiong Pham, Quoc-Cuong Song, Ran Hérault, Romain Peng, Rui Chen, Ruilong Liu, Ruixuan Baikulov, Ruslan Fukushima, Ryuto Escalera, Sergio Lee, Seungcheon Chen, Shimin Ding, Shouhong Someya, Taiga Moeslund, Thomas B. Li, Tianjiao Shen, Wei Zhang, Wei Li, Wei Dai, Wei Luo, Weixin Zhao, Wending Zhang, Wenjie Yang, Xinquan Ma, Yanbiao Joo, Yeeun Zeng, Yingsen Gan, Yiyang Zhu, Yongqiang Zhong, Yujie Ruan, Zheng Li, Zhiheng Huang, Zhijian Meng, Ziyu Belgium Saudi Arabia Sportradar Norway UCLouvain Belgium EPFL Switzerland EVS Broadcast Equipment Belgium Baidu Research United States Belgium Sharif University of Technology Iran Footovision France Zewail City of Science Technology and Innovation Egypt Université Paris-Saclay CEA France Universitat de Barcelona Spain Computer Vision Center Spain Nagoya University Japan Research Center for Applied Mathematics and Machine Intelligence Zhejiang Lab China AIBrain United States OPPO Research Institute China Germany Meituan China Tencent Youtu Lab China Amazon Prime Video Sport United States Sogang University Korea Republic of The University of Tokyo Japan Spiideo Sweden University of Tsukuba Japan School of Artificial Intelligence Beijing University of Posts and Telecommunications China Normandie Univ INSA Rouen LITIS France Shanghai Jiao Tong University China Key Laboratory of Intelligent Perception and Image Understanding The Ministry of Education Xidian University China NASK - National Research Institute Poland Robo Space China Tongji University China Sportlight Technology United Kingdom School of Control Science and Engineering Shandong University China lRomul Russia Aalborg University Denmark Turing AI Cultures GmbH Germany Information Systems Technology and Design Singapore University of Technology and Design Singapore Sun Yat-sen University China

The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leader-boards are available on https://***. Baselines and development kits can be found on https://***/SoccerNet. © 2023, CC BY.

关键词： Computer vision

A decomposition-based multiobjective evolutionary algorithm for sparse reconstruction 9th

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A decomposition-based multiobjective evolutionary algorithm ...

9th International Conference on Swarm Intelligence, ICSI 2018

作者： Zhu, Jiang Cai, Muyao Tian, Shujuan Xu, Yanbing Pei, Tingrui College of Information Engineering Xiangtan University Xiangtan411105 China National Engineering Laboratory for Robot Visual Perception and Control Technology Hunan University Changsha410082 China

ISBN: (纸本)9783319938141

Sparse reconstruction is an important method aiming at obtaining an approximation to an original signal from observed data. It can be deemed as a multiobjective optimization problem for the sparsity and the observational error terms, which are considered as two conflicting objectives in evolutionary algorithm. In this paper, a novel decomposition based multiobjective evolutionary algorithm is proposed to optimize the two objectives and reconstruct the original signal more exactly. In our algorithm, a sparse constraint specific differential evolution is designed to guarantee that the solution remains sparse in the next generation. In addition, a neighborhood-based local search approach is proposed to obtain better solutions and improve the speed of convergence. Therefore, a set of solutions is obtained efficiently and is able to closely approximate the original signal. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Multiobjective optimization

Fast ORB-SLAM without Keypoint Descriptors

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arXiv 2020年

作者： Fu, Qiang Yu, Hongshan Wang, Xiaolong Yang, Zhengeng He, Yong Zhang, Hong Mian, Ajmal The National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University China The Department of Computing Science University of Alberta Canada Quanzhou Institute of Industrial Design and Machine Intelligence Innovation Hunan University China The Southern University of Science and Technology Shenzhen China The School of Mathematics and Information Science Shaanxi Normal University China The Department of Computer Science The University of Western Australia WA6009 Australia

Indirect methods for visual SLAM are gaining popularity due to their robustness to environmental variations. ORB-SLAM2 [1] is a benchmark method in this domain, however, it consumes significant time for computing descriptors that never get reused unless a frame is selected as a keyframe. To overcome these problems, we present FastORB-SLAM which is light-weight and efficient as it tracks keypoints between adjacent frames without computing descriptors. To achieve this, a two stage descriptor-independent keypoint matching method is proposed based on sparse optical flow. In the first stage, we predict initial keypoint correspondences via a simple but effective motion model and then robustly establish the correspondences via pyramid-based sparse optical flow tracking. In the second stage, we leverage the constraints of the motion smoothness and epipolar geometry to refine the correspondences. In particular, our method computes descriptors only for keyframes. We test FastORB-SLAM on TUM and ICL-NUIM RGB-D datasets and compare its accuracy and efficiency to nine existing RGB-D SLAM methods. Qualitative and quantitative results show that our method achieves state-of-the-art accuracy and is about twice as fast as the ORB-SLAM2. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical flows

Target Tracking Based on Correlation Filter for Scale Offset

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IOP Conference Series: Materials Science and engineering 2020年第1期790卷

作者： Jianhao Tan Xiaoping Ma Yaonan Wang College of Electrical and Information Engineering Hunan University Changsha 410082 China National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha 410082 China

Existing scale estimation methods mainly use fixed-size tracking box for target tracking. However, when the moving direction of the target changes from near to far or from far to near, the size of the current tracking box cannot adapt to the scale change. In order to overcome the disadvantage of invariant size of tracking box in the scale change, this paper proposes a scale offset estimation method based on correlation filter by combining the state of previous frame and current frame of the target. Select the scale proposal box in the scale layer, and adjust the position and size of the actual tracking box in real time according to the size of the proposal box, target position and scale offset. In this paper, OTB-100 is used as the dataset. The obtained tracking results show that our algorithm Ours has better tracking performance than the existing others tracking algorithm in scale variation, and it has better tracking effect and stronger robustness in drift and occlusion events.

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