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2018 Colour and Visual Computing Symposium, CVCS 2018

作者： Ansari-Asl, Majid Ansari, Keivan Color Imaging Laboratory University of Granada E-Granada Spain Department of Color Imaging Color Image Processing Iranian Institute for Color Science and Technology Tehran Iran

ISBN: (纸本)9781538656457

Accurate measurement of the human skin color is very important in many multidisciplinary applications such as medicine for diagnostic purposes and prostheses color matching, cosmetology for makeup and skin care, computer graphics for skin rendering, and computer vision for skin detection and tracking. It must be noted that in a previous study we found that the Iranian skin colors are not truly encompassed by the Pantone SkinTone Guide, so the present study was carried out. In this research, a pilot dataset of Iranian skin color was collected to analyze its properties due to the variations in different parameters which affect the skin color and also to compare Iranian skin color gamut with different international ethnicities. The skin spectral data for a group of 60 subjects were taken at three reference locations on the face: the forehead, the left cheek, and the right cheek. The measurements were done using a spectrophotometer after filling in a questionnaire consists of a consent form in addition to name, gender, age, parent ethnicities, and blood type of the individuals. Afterward, the spectral data were converted into L∗ (Lightness), a∗ (Redness) and b∗ (Yellowness) color coordinates. These coordinate values of skin colors were analyzed according to gender, age, body part, and blood type. The results showed variations in skin color due to gender, age, body part, and ethnicity differences. Iranian males are found to have a little darker and more reddish and yellowish skin than females, and it is reported that the age increase causes a decrease in lightness and yellowness and an increase in redness for the collected data. Moreover, cheeks are identified lighter and less reddish than foreheads. A comprehensive skin color gamut is presented for Iranian population which can be used to develop a national skin color guide. © 2018 IEEE.

关键词： Color matching

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EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks

arXiv

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arXiv 2020年

作者： Meng, Lubin Huang, Jian Zeng, Zhigang Jiang, Xue Yu, Shan Jung, Tzyy-Ping Lin, Chin-Teng Chavarriaga, Ricardo Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Brainnetome Center National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China La Jolla CA United States Center for Advanced Neurological Engineering Institute of Engineering in Medicine Ucsd La Jolla CA United States Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Australia Zhaw DataLab Zürich University of Applied Sciences Winterthur8401 Switzerland

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is implementable in practice and has never been considered before. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs and calling for urgent attention to address it. Copyright © 2020, The Authors. All rights reserved.

关键词： Electroencephalography

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Fractal-scaling non-parametric processing of spatial-temporal flow of real polarimetric information in the radio systems

Fractal-scaling non-parametric processing of spatial-tempora...

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2019 International Conference on Intelligent Computing, Automation and Systems, ICICAS 2019

作者： Potapov, Alexander A. Liu, Weiping German, Vitaliy A. Feng, Tianhua Rekhviashvili, Sergo Sh. V.A. kotel'Nikov Institute of Radio Engineering and Electronics Russian Academy of Sciences Moscow Russia JNU-IREE RAS Joint Laboratory of Information Techniques and Fractal Signal Processing Jinan University Guangzhou China Department of Electronic Engineering College of Information Science and Technology Jinan University Guangzhou China Department of Theoretical and Mathematical Physics Institute of Applied Mathematics and Automation KBSC RAS Nalchik Russia

ISBN: (数字)9781728161068

ISBN: (纸本)9781728161068

关键词： Fractals

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Application of fractional calculus for impact oscillator modeling

Application of fractional calculus for impact oscillator mod...

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2019 International Conference on Intelligent Computing, Automation and Systems, ICICAS 2019

作者： Rekhviashvili, Sergo Pskhu, Arsen Potapov, A. Feng, Tianhua Narozhnov, Viktor Department of Theoretical and Mathematical Physics Institute of Applied Mathematics and Automation KBSC RAS Nalchik Russia JNU-IREE RAS Joint Laboratory of Information Techniques and Fractal Signal Processing Jinan University Guangzhou China Department of Fractional Calculus Institute of Applied Mathematics and Automation KBSC RAS Nalchik Russia Institute of Radioengineering and Electronics RAS Moscow Russia Department of Electronic Engineering College of Information Science and Technology Jinan University Guangzhou China Laboratory of Measurement Automation Institute of Applied Mathematics and Automation KBSC RAS Nalchik Russia

ISBN: (纸本)9781728161068

A new theoretical model for describing an impact oscillator that is based on the use of a fractional derivative is constructed. The acoustic signals that occur when the probe strikes the surface of metals are experimentally investigated. It is shown that acoustic signals can be used to diagnose elastic properties of materials. © 2019 IEEE.

关键词： Energy dissipation

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URBAN AREA IMPERVIOUS SURFACE ESTIMATION BY SUBPIXEL UNMIXING

URBAN AREA IMPERVIOUS SURFACE ESTIMATION BY SUBPIXEL UNMIXIN...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Bai Xue Shuhan Chen Chia-Chen Liang Shengwei Zhong Peter F. Hu Chein-I Chang Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore USA Zhejiang University China Department of Information Engineering Harbin Institute of Technology Harbin China Department of Anesthesiology University of Maryland School of Medicine Baltimore MD USA

Urban impervious surface area (ISA) is a key index toward urban eco-system and sustainable urban planning strategy. In this paper, a subpixel approach is proposed to estimate urban ISA values using linear spectral mixture analysis (LSMA)-based hyperspectral imaging techniques. In doing so the concept of virtual dimensionality (VD) is used to first estimate the number of endmembers, then an endmember finding approach is implemented to find VD-determined number of endmembers in a hyperspectral image. Finally, nonnegativity constrained least squares (NCLS) is performed for endmember unmixing. The hyperspectral image used in our approach provides a larger number of spectral dimensions than a multispectral image does so that a sufficient number of endmembers can be found from a hyperspectral image for ISA estimation. What is more, a relationship between ISA values and fractional endmember abundances can be further constructed by linear regression.

关键词： Hyperspectral imaging Training Estimation Linear regression Urban areas

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Network Resilience

arXiv

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arXiv 2020年

作者： Liu, Xueming Li, Daqing Ma, Manqing Szymanski, Boleslaw K. Eugene Stanley, H. Gao, Jianxi Key Laboratory of Image Information Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China School of Reliability and Systems Engineering Beihang University Beijing100191 China College of Safety Science and Engineering Civil Aviation University of China Tianjin 300300 China Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States Network Science and Technology Center Rensselaer Polytechnic Institute TroyNY12180 United States Center for Polymer Studies Department of Physics Boston University BostonMA02215 United States

Many systems on our planet shift abruptly and irreversibly from the desired state to an undesired state when forced across a "tipping point". Some examples are mass extinctions within ecosystems, cascading failures in infrastructure systems, and changes in human and animal social networks. The ability to avoid such regime shifts or to recover quickly from such a non-resilient state demonstrates a system's resilience;system resilience is a quality that enables a system to adjust its activities to retain its basic functionality when errors and failures occur. In the past 50 years, attention has been paid almost exclusively to low-dimensional systems;scholars have focused on the calibration of the resilience functions of such systems and the identification of indicators of early warning signals based on two to three connected components. In recent years, taking advantage of network theory and the availability of lavish real datasets, network scientists have begun to explore real-world complex networked multidimensional systems, as well as their resilience functions and early warning indicators. This report presents a comprehensive review of resilience functions and regime shifts in complex systems in domains such as ecology, biology, society, and infrastructure. The research approach includes empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also review the definitions of some ambiguous terms, including robustness, resilience, and stability. Copyright © 2020, The Authors. All rights reserved.

关键词： Complex networks

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Global Rice Multi-Class Segmentation Dataset (RiceSEG): A Comprehensive and Diverse High-Resolution RGB-Annotated images for the Development and Benchmarking of Rice Segmentation Algorithms

arXiv

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arXiv 2025年

作者： Zhou, Junchi Wang, Haozhou Kato, Yoichiro Nampally, Tejasri Rajalakshmi, P. Balram, M. Katsura, Keisuke Lu, Hao Mu, Yue Yang, Wanneng Gao, Yangmingrui Xiao, Feng Chen, Hongtao Chen, Yuhao Li, Wenjuan Wang, Jingwen Yu, Fenghua Zhou, Jian Wang, Wensheng Hu, Xiaochun Yang, Yuanzhu Ding, Yanfeng Guo, Wei Liu, Shouyang Engineering Research Center of Plant Phenotyping Ministry of Education Jiangsu Collaborative Innovation Center for Modern Crop Production Academy for Advanced Interdisciplinary Studies Sanya Institute of Nanjing Agricultural University Nanjing Agricultural University Nanjing China Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan Department of Artificial Intelligence Indian Institute of Technology Hyderabad India Department of Electrical Engineering Indian Institute of Technology Hyderabad India Institute of Biotechnology Professor Jayashankar Telangana Agricultural State University Hyderabad India Graduate School of Agriculture Kyoto University Kyoto Japan Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China National Key Laboratory of Crop Genetic Improvement National Center of Plant Gene Research Hubei Key Laboratory of Agricultural Bioinformatics Huazhong Agricultural University Wuhan China State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China the Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing China Center for Geospatial Information Shenzhen Institutes of Advanced Technology Chinese Academy of Science Shenzhen China School of Information and Electrical Engineering Shenyang Agricultural University Shenyang China Rice Research Institute Jilin Academy of Agricultural Sciences Changchun China Institute of Crop Sciences National Key Facility for Crop Gene Resources and Genetic Improvement Chinese Academy of Agricultural Sciences Beijing China Yuan Long Ping High-Tech Agriculture Co. Ltd. Changsha China

Developing computer vision–based rice phenotyping techniques is crucial for precision field management and accelerating breeding, thereby continuously advancing rice production. Among phenotyping tasks, distinguishing image components is a key prerequisite for characterizing plant growth and development at the organ scale, enabling deeper insights into eco-physiological processes. However, due to the fine structure of rice organs and complex illumination within the canopy, this task remains highly challenging, underscoring the need for a high-quality training dataset. Such datasets are scarce, both due to a lack of large, representative collections of rice field images and the time-intensive nature of annotation. To address this gap, we established the first comprehensive multi-class rice semantic segmentation dataset, RiceSEG. We gathered nearly 50,000 high-resolution, ground-based images from five major rice-growing countries (China, Japan, India, the Philippines, and Tanzania), encompassing over 6,000 genotypes across all growth stages. From these original images, 3,078 representative samples were selected and annotated with six classes (background, green vegetation, senescent vegetation, panicle, weeds, and duckweed) to form the RiceSEG dataset. Notably, the sub-dataset from China spans all major genotypes and rice-growing environments from the northeast to the south. Both state-of-the-art convolutional neural networks and transformer-based semantic segmentation models were used as baselines. While these models perform reasonably well in segmenting background and green vegetation, they face difficulties during the reproductive stage, when canopy structures are more complex and multiple classes are involved. These findings highlight the importance of our dataset for developing specialized segmentation models for rice and other crops. The RiceSEG dataset is publicly available at ***. © 2025, CC BY.

关键词： Semantic Segmentation

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Learning tubule-sensitive CNNs for pulmonary airway and artery-vein segmentation in CT

arXiv

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arXiv 2020年

作者： Qin, Yulei Zheng, Hao Gu, Yun Huang, Xiaolin Yang, Jie Wang, Lihui Yao, Feng Zhu, Yue-Min Yang, Guang-Zhong Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province School of Computer Science and Technology Guizhou University Guiyang China Department of Thoracic Surgery Shanghai Chest Hospital Shanghai Jiao Tong University Shanghai China Université de Lyon INSA Lyon CREATIS CNRS INSERM UMR 5220 VilleurbanneU1206 France Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background. We present a CNNs-based method for accurate airway and artery-vein segmentation in non-contrast computed tomography. It enjoys superior sensitivity to tenuous peripheral bronchioles, arterioles, and venules. The method first uses a feature recalibration module to make the best use of features learned from the neural networks. Spatial information of features is properly integrated to retain relative priority of activated regions, which benefits the subsequent channel-wise recalibration. Then, attention distillation module is introduced to reinforce representation learning of tubular objects. Fine-grained details in high-resolution attention maps are passing down from one layer to its previous layer recursively to enrich context. Anatomy prior of lung context map and distance transform map is designed and incorporated for better artery-vein differentiation capacity. Extensive experiments demonstrated considerable performance gains brought by these components. Compared with state-of-the-art methods, our method extracted much more branches while maintaining competitive overall segmentation performance. Codes and models are available at http://***/News/56. © 2020, CC BY.

关键词： Computerized tomography

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AIM 2019 challenge on real-world image super-resolution: Methods and results

arXiv

引用

arXiv 2019年

作者： Lugmayr, Andreas Danelljan, Martin Timofte, Radu Fritsche, Manuel Gu, Shuhang Purohit, Kuldeep Kandula, Praveen Suin, Maitreya Rajagopalan, A.N. Joon, Nam Hyung Won, Yu Seung Kim, Guisik Kwon, Dokyeong Hsu, Chih-Chung Lin, Chia-Hsiang Huang, Yuanfei Sun, Xiaopeng Lu, Wen Li, Jie Gao, Xinbo Bell-Kligler, Sefi Computer Vision Lab ETH Zurich Indian Institute Of Technology Madras India Image Communication & Signal Processing Laboratory Hanyang University Seoul Korea Republic of CVML Chung-Ang University Department Management Information Systems National Pingtung University of Science and Technology Department of Electrical Engineering National Cheng-Kung University Xidian University Weizmann Institute of Science Israel

This paper reviews the AIM 2019 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided in the challenge. In Track 1: Source Domain the aim is to super-resolve such images while preserving the low level image characteristics of the source input domain. In Track 2: Target Domain a set of high-quality images is also provided for training, that defines the output domain and desired quality of the superresolved images. To allow for quantitative evaluation, the source input images in both tracks are constructed using artificial, but realistic, image degradations. The challenge is the first of its kind, aiming to advance the state-of-the-art and provide a standard benchmark for this newly emerging task. In total 7 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem. Copyright © 2019, The Authors. All rights reserved.

关键词： Optical resolving power

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Power Equalization Scheme for Multi-User Radiative Focusing Wireless Power Transfer

Power Equalization Scheme for Multi-User Radiative Focusing ...

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International Conference on Microwave and Millimeter Wave technology Proceedings

作者： Jinlong Bao Deshuang Zhao Weiping Cao Bing Li Bing-Zhong Wang Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin Institute of Applied Physics University of Electronic Science and Technology of China Chengdu School of Electrical Engineering Southwest JiaoTong University Chengdu

Radiative focusing wireless power transfer (RFWPT) has attracted an increasing attention due to its potential of powering multi-users wirelessly without power cables at a long distance. However, it is still challenging to realize efficient equalized power transfer among users. In this paper, we present an efficient power equalization scheme for multi-user RFWPT system. First, we build a multi-user RFWPT model based on the time reversal focusing theory. Second, we derive the efficiency formula and the conditions for efficient multi-user wireless power transfer, and then present a receiving power equalization scheme for multi-users with the deduced efficiency formula. Finally, we numerically verify the power equalization scheme by full-wave electromagnetic simulations.

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