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International Conference on computer Vision (ICCV)

作者： Xu Zou Sheng Zhong Luxin Yan Xiangyun Zhao Jiahuan Zhou Ying Wu College of Life Science and Technology Huazhong University of Science & Technology (HUST) Wuhan China National Key Laboratory of Science & Technology on Multi-Spectral Information Processing HUST Wuhan China School of Aritificial Intelligence and Automation HUST Wuhan China Department of Electrical and Computer Engineering Northwestern University IL USA

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Heatmap regression-based models have significantly advanced the progress of facial landmark detection. However, the lack of structural constraints always generates inaccurate heatmaps resulting in poor landmark detection performance. While hierarchical structure modeling methods have been proposed to tackle this issue, they all heavily rely on manually designed tree structures. The designed hierarchical structure is likely to be completely corrupted due to the missing or inaccurate prediction of landmarks. To the best of our knowledge, in the context of deep learning, no work before has investigated how to automatically model proper structures for facial landmarks, by discovering their inherent relations. In this paper, we propose a novel Hierarchical Structured Landmark Ensemble (HSLE) model for learning robust facial landmark detection, by using it as the structural constraints. Different from existing approaches of manually designing structures, our proposed HSLE model is constructed automatically via discovering the most robust patterns so HSLE has the ability to robustly depict both local and holistic landmark structures simultaneously. Our proposed HSLE can be readily plugged into any existing facial landmark detection baselines for further performance improvement. Extensive experimental results demonstrate our approach significantly outperforms the baseline by a large margin to achieve a state-of-the-art performance.

关键词： Robustness Heating systems Detectors Face Shape Mathematical model

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Rotation invariant point cloud analysis: Where local geometry meets global topology

arXiv

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arXiv 2019年

作者： Zhao, Chen Yang, Jiaqi Xiong, Xin Zhu, Angfan Cao, Zhiguo Li, Xin National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown26506 United States

Point cloud analysis is a fundamental task in 3D computer vision. Most previous works have conducted experiments on synthetic datasets with well-aligned data;while real-world point clouds are often not pre-aligned. How to achieve rotation invariance remains an open problem in point cloud analysis. To meet this challenge, we propose an approach toward achieving rotation-invariant (RI) representations by combining local geometry with global topology. In our local-global-representation (LGR)-Net, we have designed a two-branch network where one stream encodes local geometric RI features and the other encodes global topology-preserving RI features. Motivated by the observation that local geometry and global topology have different yet complementary RI responses in varying regions, two-branch RI features are fused by an innovative multi-layer perceptron (MLP) based attention module. To the best of our knowledge, this work is the first principled approach toward adaptively combining global and local information under the context of RI point cloud analysis. Extensive experiments have demonstrated that our LGR-Net achieves the state-of-the-art performance on various rotation-augmented versions of ModelNet40, ShapeNet, ScanObjectNN, and S3DIS. Copyright © 2019, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Subspace Support Vector Data Description

arXiv

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arXiv 2018年

作者： Sohrab, Fahad Raitoharju, Jenni Gabbouj, Moncef Iosifidis, Alexandros Laboratory of Signal Processing Tampere University of Technology Finland Department of Engineering Electrical and Computer Engineering Aarhus University Denmark

This paper proposes a novel method for solving one-class classification problems. The proposed approach, namely Subspace Support Vector Data Description, maps the data to a subspace that is optimized for one-class classification. In that feature space, the optimal hypersphere enclosing the target class is then determined. The method iteratively optimizes the data mapping along with data description in order to define a compact class representation in a low-dimensional feature space. We provide both linear and non-linear mappings for the proposed method. Experiments on 14 publicly available datasets indicate that the proposed Subspace Support Vector Data Description provides better performance compared to baselines and other recently proposed one-class classification methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Data description

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Generating ultradense pair beams using 400 GeV/c protons

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Physical Review Research 2021年第2期3卷 023103-023103页

作者： C. D. Arrowsmith N. Shukla N. Charitonidis R. Boni H. Chen T. Davenne A. Dyson D. H. Froula J. T. Gudmundsson B. T. Huffman Y. Kadi B. Reville S. Richardson S. Sarkar J. L. Shaw L. O. Silva P. Simon R. M. G. M. Trines R. Bingham G. Gregori Department of Physics University of Oxford Parks Road Oxford OX1 3PU United Kingdom CINECA High-Performance Computing Department Via Magnanelli 6/3 40033 Casalecchio di Reno - Bologna Italy European Organization for Nuclear Research (CERN) CH-1211 Geneva 23 Switzerland University of Rochester Laboratory for Laser Energetics Rochester New York 14623 USA Lawrence Livermore National Laboratory 7000 East Avenue Livermore California 94550 USA Rutherford Appleton Laboratory Chilton Didcot OX11 0QX United Kingdom Science Institute University of Iceland Dunhaga 3 IS-107 Reykjavik Iceland Department of Space and Plasma Physics School of Electrical Engineering and Computer Science KTH Royal Institute of Technology SE-100 44 Stockholm Sweden Max-Planck-Institut für Kernphysik Saupfercheckweg 1 D-69117 Heidelberg Germany Atomic Weapons Establishment Aldermaston Reading Berkshire RG7 4PR United Kingdom GoLP/Instituto de Plasmas e Fusão Nuclear Instituto Superior Técnico Universidade de Lisboa 1049-001 Lisboa Portugal Department of Physics University of Strathclyde Glasgow G4 0NG United Kingdom

An experimental scheme is presented for generating low-divergence, ultradense, relativistic, electron-positron beams using 400 GeV/c protons available at facilities such as HiRadMat and AWAKE at CERN. Preliminary Monte Carlo and particle-in-cell simulations demonstrate the possibility of generating beams containing 1013–1014 electron-positron pairs at sufficiently high densities to drive collisionless beam-plasma instabilities, which are expected to play an important role in magnetic field generation and the related radiation signatures of relativistic astrophysical phenomena. The pair beams are quasineutral, with size exceeding several skin depths in all dimensions, allowing the examination of the effect of competition between transverse and longitudinal instability modes on the growth of magnetic fields. Furthermore, the presented scheme allows for the possibility of controlling the relative density of hadrons to electron-positron pairs in the beam, making it possible to explore the parameter spaces for different astrophysical environments.

关键词： Beam instabilities Cosmic ray acceleration Cosmic ray composition & spectra Cosmic ray sources Gamma ray astronomy Gamma ray bursts High-energy-density plasmas laboratory studies of space & astrophysical plasmas Optical, UV, & IR astronomy plasma instabilities plasma-beam interactions Relativistic multiple-particle dynamics

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A java code protection scheme via dynamic recovering runtime instructions 18th

A java code protection scheme via dynamic recovering runtime...

引用

18th International Conference on Algorithms and Architectures for Parallel processing, ICA3PP 2018

作者： Jiajia, Sun Jinbao, Gao Yu-an, Tan Yu, Zhang Xiao, Yu School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China School of Electrical and Information Engineering Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing100044 China China University of Mining and Technology Beijing100083 China Department of Computer Science and Technology Shandong University of Technology ZiboShandong255022 China

ISBN: (纸本)9783030050627

As Android operating system and applications on the device play important roles, the security requirements of Android applications increased as well. With the upgrade of Android system, Android runtime mode (ART mode) has gradually become the mainstream architecture of the Android operating system. ART introduces several improvements in Android, but it also introduces new ways to enhance malicious activities. This paper proposed a confidential finer granularity protection scheme for application programs under ART mode of ROOT Android devices. Taking Java method as the protection granularity, the protection scheme increased the accuracy of protecting targets. In addition, the protection scheme provided a more thorough protection for applications by combining dynamic loading technology and encryption technology in ART mode, and improved the security of Android applications. Experiments showed that the proposed protection scheme is effective. © Springer Nature Switzerland AG 2018.

关键词： Android (operating system)

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Quality Inspection of Phalaenopsis Hybrids Using Hyperspectral Band Selection Techniques

Quality Inspection of Phalaenopsis Hybrids Using Hyperspectr...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yen-Chieh Ouyang Bo-Han Chen Meng-Chueh Lee Tsang-Sen Liu Mang Ou-Yang Hsian-Min Chen Chao-Cheng Wu Chia-Hsien Wen Min-Shao Shih Chein-I Chang Yung-Jhe Yan Department of Electrical Engineering National Chung Hsing University Taiwan Institute of communication Engineering National Chung Hsing University Taiwan Taiwan Agriculture Research Institute Nantou Taiwan Department of Electrical and Computer Engineering National Chiao-Tung University Hsinchu City Taiwan Center for Quantitative Imaging in Medicine Taichung Veterans General Hospital Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Management Providence University Taiwan Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore MD USA

Fusarium wilt on Phalaenopsis is a disease that makes farmers suffer seriously. Although Phalaenopsis does not die immediately with Fusarium wilt, it seriously decreases the quality that buyers cannot accept. In this paper, we introduce an emerging method to detect Fusarium wilt at the base of Phalaenopsis stems. The detection model divides Phalaenopsis samples into two categories, healthy and infection. The band selection (BS) processing technique based on band prioritization (BP) is applied to extract significant bands and eliminate redundant bands. Subsequently, some algorithms which are constrained energy minimization (CEM), spectral information divergence(SID) and SeQuential N-FINDER to detect the Fusarium wilt, and we hope the research would help farmers decrease their losses.

关键词： Hyperspectral imaging Support vector machines Diseases Signal to noise ratio Hybrid fiber coaxial cables Correlation

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OCTAVA: An open-source toolbox for quantitative analysis of optical coherence tomography angiography images

arXiv

引用

arXiv 2021年

作者： Untracht, Gavrielle R. Matos, Rolando Dikaios, Nikolaos Bapir, Mariam Durrani, Abdullah K. Butsabong, Teemapron Campagnolo, Paola Sampson, David D. Heiss, Christian Sampson, Danuta M. Optical+Biomedical Engineering Laboratory School of Electrical Electronic and Computer Engineering The University of Western Australia 35 Stirling Highway PerthWA6009 Australia Surrey Biophotonics Advanced Technology Institute School of Physics School of Biosciences and Medicine University of Surrey Surrey GuildfordGU2 7XH United Kingdom Department of Biochemical Sciences Department of Clinical and Experimental Medicine School of Biosciences and Medicine University of Surrey Surrey GuildfordGU2 7XH United Kingdom Mathematics Research Center Academy of Athens Athens106 79 Greece Surrey and Sussex Healthcare NHS Trust East Surrey Hospital Surrey RedhillRH1 5RH United Kingdom Surrey Biophotonics Centre for Vision Speech and Signal Processing School of Biosciences and Medicine The University of Surrey GuildfordGU2 7XH United Kingdom

Optical coherence tomography angiography (OCTA) performs non-invasive visualization and characterization of microvasculature in research and clinical applications mainly in ophthalmology and dermatology. A wide variety of instruments, imaging protocols, processing methods and metrics have been used to describe the microvasculature, such that comparing different study outcomes is currently not feasible. With the goal of contributing to standardization of OCTA data analysis, we report a user-friendly, open-source toolbox, OCTAVA (OCTA Vascular Analyzer), to automate the pre-processing, segmentation, and quantitative analysis of en face OCTA maximum intensity projection images in a standardized workflow. We present each analysis step, including optimization of filtering and choice of segmentation algorithm, and definition of metrics. We perform quantitative analysis of OCTA images from different commercial and non-commercial instruments and samples and show OCTAVA can accurately and reproducibly determine metrics for characterization of microvasculature. Wide adoption could enable studies and aggregation of data on a scale sufficient to develop reliable microvascular biomarkers for early detection, and to guide treatment, of microvascular disease. © 2021, CC BY-NC-SA.

关键词： Optical tomography

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Ranking-based deep cross-modal hashing

arXiv

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arXiv 2019年

作者： Liu, Xuanwu Yu, Guoxian Domeniconi, Carlotta Wang, Jun Ren, Yazhou Guo, Maozu College of Computer and Information Sciences Southwest University Chongqing China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan China Department of Computer Science George Mason University Fairfax United States SMILE Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China

Cross-modal hashing has been receiving increasing interests for its low storage cost and fast query speed in multi-modal data retrievals. However, most existing hashing methods are based on hand-crafted or raw level features of objects, which may not be optimally compatible with the coding process. Besides, these hashing methods are mainly designed to handle simple pairwise similarity. The complex multilevel ranking semantic structure of instances associated with multiple labels has not been well explored yet. In this paper, we propose a ranking-based deep cross-modal hashing approach (RDCMH). RDCMH firstly uses the feature and label information of data to derive a semi-supervised semantic ranking list. Next, to expand the semantic representation power of hand-crafted features, RDCMH integrates the semantic ranking information into deep cross-modal hashing and jointly optimizes the compatible parameters of deep feature representations and of hashing functions. Experiments on real multi-modal datasets show that RDCMH outperforms other competitive baselines and achieves the state-of-the-art performance in cross-modal retrieval applications. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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Detection of Fusarium wilt on Phalaenopsis stem base region using band selection techniques 38

Detection of Fusarium wilt on Phalaenopsis stem base region ...

引用

38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018

作者： Lee, Meng-Chueh Ma, Kenneth-Yeonkong Ouyang, Yen-Chieh Ou-Yang, Mang Guo, Horng-Yuh Liu, Tsang-Sen Chen, Hsian-Min Wu, Chao-Cheng Chang, Chgein-I Department of Communications Engineering National Chung Hsing University Taichung Taiwan Department of Electrical Engineering National Chung Hsing University Taichung Taiwan National Chiao-Tung University Department of Electrical and Computer Engineering Hsinchu City Taiwan Taiwan Agriculture Research Institute Nan-Tou Taiwan Center for Quantitative Imaging in Medicine Taichung Veterans General Hospital Taichung Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States

ISBN: (纸本)9781538671504

Phalaenopsis is a significant agriculture product with high economic value in Taiwan. However, the fusarium wilt causes Phalaenopsis leaves turning yellow, thinning, water loss, and finally died. This paper presents an emerging method to detect fusarium wilt on Phalaenopsis stem base. In order to build the detection models, the hyperspectral databases are generated form two statues of Phalaenopsis samples, which are health and disease sample. We applied band selection (BS) processing base on band prioritization (BP) and band de-correlation (BD) to extract the significant bands and eliminate the redundant bands. Then, three algorithms were used, orthogonal subspace projection (OSP), constrain energy minimization (CEM), and support vector machine (SVM) to detect the fusarium wilt. © 2018 IEEE

关键词： Diseases Hyperspectral imaging Support vector machines Agriculture Databases Entropy

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Mitigate the obstructing effect of vehicles on the propagation of VANETs safety-related information

Mitigate the obstructing effect of vehicles on the propagati...

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作者： Huang, Rui Wu, Jing Long, Chengnian Zhu, Yanmin Li, Bo Lin, Yi-Bing Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Computer Science School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Computer Science and Information Engineering National Chiao Tung University Taiwan

As physical obstacles, vehicles have significant impacts on the efficient propagations of safety-related information in vehicular ad hoc networks (VANETs) by frequently obstructing the LOS links between transmitters and receivers. Obstructing effect will diminish the effective coverage of safety-related information and incur severe impact on road safety. However, this impact has not been addressed effectively. In this paper, we first present the definition of broadcast efficiency as the metric for quantizing the obstructing effect. We investigate the optimization issue for mitigating the impact of obstructing effect on the propagation of safety-related information and propose a graph theory-based optimization algorithm. For a distributed implementation of this optimization in VANETs, we further propose the maximum broadcast efficiency relaying (MBER) algorithm. MBER maximizes the effective coverage of information, meanwhile, meets delay and reliability constraints by incorporating broadcast efficiency and propagation distance in relay contention. Simulation results demonstrate that MBER promotes the effective coverage of safety-related information in VANETs with varying vehicular distribution. © 1967-2012 IEEE.

关键词： Vehicular ad hoc networks

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