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8th International Conference on Digital Home, ICDH 2020

作者： Li, Youxing Lan, Rushi Sun, Long Li, Ji Luo, Xiaonan Pang, Cheng Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China Guilin University of Electronic Technology National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin541004 China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin541004 China

ISBN: (纸本)9781728192345

Rain streaks noise is one of the greatest influence of outdoor scene tasks. In order to improve the effect of the outdoor scene tasks, we need to reduce the impact of the rain streaks noise while ensuring that other important details are preserved. To handle this issue, we proposed a multi-scale recurrent network (MSReNet) for single image rain removal. By divided MSReNet into two stages: (i) removal rain streaks in the first stage and (ii) restoring background-truth details in the second stage, our network can availably remove huge rain streaks and restore some important background details. Extensive experiments on both synthetic and real images demonstrated that the proposed MSReNet prominently exceeds many recent state-of-the-art methods. Taking the proposed method effectiveness, it is also attractive as a preprocessing process for some visual tasks. © 2020 IEEE.

关键词： Rain

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One-dimensional quasi bound states in the continuum with large operation bandwidth in the ω~k space for nonlinear optical applications

arXiv

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arXiv 2022年

作者： Sun, Kaili Jiang, Hui Bykov, Dmitry A. Van, Vien Levy, Uriel Cai, Yangjian Han, Zhanghua Shandong Provincial Key Laboratory of Optics and Photonic Devices Center of Light Manipulation and Applications School of Physics and Electronics Shandong Normal University Jinan250358 China Image Processing Systems Institute Branch of the Federal Scientific Research Centre "Crystallography and Photonics" Russian Academy of Sciences 151 Molodogvardeyskaya st. Samara443001 Russia Department of Electrical and Computer Engineering University of Alberta EdmontonT6G 2V4 Canada Department of Applied Physics The center for Nanoscience and Nanotechnology The Hebrew University of Jerusalem Jerusalem Israel

The phenomenon of bound state in the continuum (BIC) with infinite quality factor and lifetime has emerged in recent years in photonics as a new tool of manipulating light-matter interactions. However, most of the investigated structures only support BIC resonances at very few discrete points in the ω~k space. Even when the BIC is switched to a quasi-BIC(QBIC) resonance through perturbation, its frequency will still be located within a narrow spectral band close to that of the original BIC, restricting their applications in many fields where random or multiple input frequencies beyond the narrow band are required. In this work, we demonstrate that a new set of QBIC resonances can be supported by making use of a special binary grating consisting of two alternatingly aligned ridge arrays with the same period and zero-approaching ridge width difference on a slab waveguide. These QBIC resonances are distributed continuously over a broad band along a line in the ω~k space and can thus be considered as one-dimensional QBICs. With the Q factors generally affected by the ridge difference, it is now possible to choose arbitrarily any frequencies on the dispersion line to achieve significantly enhanced light-matter interactions, facilitating many applications where multiple input wavelengths are required, e.g. sum or difference frequency generations in nonlinear optics. Copyright © 2022, The Authors. All rights reserved.

关键词： Resonance

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Fast and accurate single-image depth estimation on mobile devices, mobile AI 2021 challenge: Report

arXiv

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arXiv 2021年

作者： Ignatov, Andrey Malivenko, Grigory Plowman, David Shukla, Samarth Timofte, Radu Zhang, Ziyu Wang, Yicheng Huang, Zilong Luo, Guozhong Yu, Gang Fu, Bin Wang, Yiran Li, Xingyi Shi, Min Xian, Ke Cao, Zhiguo Du, Jin-Hua Wu, Pei-Lin Ge, Chao Yao, Jiaoyang Tu, Fangwen Li, Bo Yoo, Jung Eun Seo, Kwanggyoon Xu, Jialei Li, Zhenyu Liu, Xianming Jiang, Junjun Chen, Wei-Chi Joya, Shayan Fan, Huanhuan Kang, Zhaobing Li, Ang Feng, Tianpeng Liu, Yang Sheng, Chuannan Yin, Jian Benavides, Fausto T. Computer Vision Lab ETH Zurich Switzerland Ltd AI Witchlabs Switzerland Tencent GY-Lab China Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Nanjing Artificial Intelligence Chip Research Institute of Automation Chinese Academy of Sciences China Black Sesame Technologies Inc. Singapore Singapore Visual Media Lab KAIST Korea Republic of Harbin Institute of Technology China Peng Cheng Laboratory China Multimedia and Computer Vision Laboratory National Cheng Kung University Taiwan Samsung Research UK United Kingdom OPPO Research Institute China ETH Zurich Switzerland

Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms. For this, the participants were provided with a new large-scale dataset containing RGB-depth image pairs obtained with a dedicated stereo ZED camera producing high-resolution depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the popular Raspberry Pi 4 platform with a mobile ARM-based Broadcom chipset. The proposed solutions can generate VGA resolution depth maps at up to 10 FPS on the Raspberry Pi 4 while achieving high fidelity results, and are compatible with any Android or Linux-based mobile devices. A detailed description of all models developed in the challenge is provided in this paper. Copyright © 2021, The Authors. All rights reserved.

关键词： computer operating systems

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Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge

arXiv

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arXiv 2025年

作者： Imran, Muhammad Krebs, Jonathan R. Sivaraman, Vishal Balaji Zhang, Teng Kumar, Amarjeet Ueland, Walker R. Fassler, Michael J. Huang, Jinlong Sun, Xiao Wang, Lisheng Shi, Pengcheng Rokuss, Maximilian Baumgartner, Michael Kirchhof, Yannick Maier-Hein, Klaus H. Isensee, Fabian Liu, Shuolin Han, Bing Nguyen, Bong Thanh Shin, Dong-Jin Ji-Woo, Park Choi, Mathew Uhm, Kwang-Hyun Ko, Sung-Jea Lee, Chanwoong Chun, Jaehee Kim, Jin Sung Zhang, Minghui Zhang, Hanxiao You, Xin Gu, Yun Pan, Zhaohong Liu, Xuan Liang, Xiaokun Tiefenthaler, Markus Almar-Munoz, Enrique Schwab, Matthias Kotyushev, Mikhail Epifanov, Rostislav Wodzinski, Marek Müller, Henning Qayyum, Abdul Mazher, Moona Niederer, Steven A. Wang, Zhiwei Yang, Kaixiang Ren, Jintao Korreman, Stine Sofia Gao, Yuchong Zeng, Hongye Zheng, Haoyu Zheng, Rui Yue, Jinghua Zhou, Fugen Liu, Bo Cosman, Alexander Liang, Muxuan Zhao, Chang Upchurch, Gilbert R. Ma, Jun Zhou, Yuyin Cooper, Michol A. Shao, Wei Department of Medicine University of Florida GainesvilleFL32611 United States Department of Surgery University of Florida GainesvilleFL32611 United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Information Science and Engineering University of Florida GainesvilleFL32611 United States Shanghai Jiao Tong University Shanghai China Electronic & Information Engineering School Harbin Institute of Technology [Shenzhen Shenzhen China Division of Medical Image Computing German Cancer Research Center [DKFZ Heidelberg Germany CO. LTD Beijing China MedAI Seoul Korea Republic of Korea University Seoul Korea Republic of Department of Radiation Oncology Yonsei Cancer Center Heavy Ion Therapy Research Institute Yonsei University College of Medicine Seoul Korea Republic of Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Medical University of Innsbruck Innsbruck Austria Novosibirsk State University Novosibirsk Russia AGH University of Krakow Krakow Poland Sierre Switzerland University of Geneva Geneva Switzerland The Sense Innovation and Research Center Sion Switzerland National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Hubei Wuhan China Aarhus University Department of Clinical Medicine Nordre Palle Juul-Jensens Blvd. 11 Aarhus8200 Denmark Shanghaitech University Pudong Shanghai China Image Processing Center Beihang University Beijing China Department of Computer Science and Engineering University of California Santa CruzCA95064 United States Department of Biostatistics University of Florida GainesvilleFL32611 United States University Health Network ON Canada Agronomy Department University of F

Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic segmentation to a binary problem, limiting their ability to measure diameters across different branches and zones. Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods. To address this gap, we organized the AortaSeg24 MICCAI Challenge, introducing the first dataset of 100 CTA volumes annotated for 23 clinically relevant aortic branches and zones. This dataset was designed to facilitate both model development and validation. The challenge attracted 121 teams worldwide, with participants leveraging state-of-the-art frameworks such as nnU-Net and exploring novel techniques, including cascaded models, data augmentation strategies, and custom loss functions. We evaluated the submitted algorithms using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD), highlighting the approaches adopted by the top five performing teams. This paper presents the challenge design, dataset details, evaluation metrics, and an in-depth analysis of the top-performing algorithms. The annotated dataset, evaluation code, and implementations of the leading methods are publicly available to support further research. All resources can be accessed at https://***. © 2025, CC BY-NC-SA.

关键词： computerized tomography

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Control-oriented fault detection of solid oxide fuel cell system unknown input on fuel supply

Control-oriented fault detection of solid oxide fuel cell sy...

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作者： Wu, Xiao-long Xu, Yuan-wu Xue, Tao Shuai, Junchao Jiang, Jianhua Deng, Zhonghua Fu, Xiaowei Li, Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Wuhan Industrial Economic Development Research Center Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China

As the solid oxide fuel cell (SOFC) system work environment is a high-temperature environment for a long time, it is difficult to obtain the SOFC stack internal state change directly. When the fault occurs, it is difficult to determine where the fault occurs. Moreover, the existing literature ignores the impact of faults, which creates many problems for SOFC system control. Therefore, a state observer-based fault detection method, which is used to detect the input flow sensor fault and the fuel input fault, is proposed. Their advantage is that they do not need data processing. To realize the fault detection, the observer is used to track the changes of SOFC stack chamber temperature. To obtain the observer estimation parameter, an approach from the actual stack structure parameters is employed to approximate the observer parameters. The results show the proposed fault detect method can judge fuel input fault type quickly and shield the disturbances signals from the sensor effectively. The proposed method also can be used to other operating points or air input fault. © 2018 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd

关键词： Fault detection

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Make your choice for multimodal knowledge graph completion

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Knowledge-Based Systems 2025年 323卷

作者： Yifan Xue Shuoyan Ren Wanqiang Cai Yingyao Ma Lotfi Senhadji Huazhong Shu Jiasong Wu Laboratory of Image Science and Technology Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing 210096 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Nanjing 210096 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China Univ Rennes INSERM LTSI - UMR 1099 F-35000 Rennes France Centre de Recherche en Information Biomédicale Sino-français (CRIBs) Univ Rennes INSERM F-35000 Rennes France

Knowledge graph completion (KGC) aims to predict missing entities in knowledge graphs by learning effective representations of entities and their relations. Recent advances have explored multimodal KGC by incorporating structural, textual, and visual information. However, two critical challenges remain unresolved: (1) modal heterogeneity, where significant differences in feature distributions across modalities hinder effective fusion; and (2) spatial heterogeneity, where embedding knowledge graphs in a single geometric space fails to capture their complex topological structures. To address these challenges, we propose ChoicE, a unified framework that leverages a mixture of experts (MoE) design for both encoding and decoding. In the encoder, the multimodal chooser preprocesses multiple modalities to derive embedding representations for each modality. These representations are then processed by distinct experts specialized for structural, textual, and visual features, facilitating effective fusion while preserving modality-specific information. In the decoder, the geometric chooser projects the unified multimodal embeddings into Euclidean, complex, or hyperbolic space, dynamically selecting the most appropriate space to model the inference patterns inherent to each query. Extensive experiments on multiple benchmark datasets demonstrate that ChoicE effectively overcomes these dilemmas and achieves state-of-the-art performance in multimodal KGC. The data and code are released at https://***/r/ChiocE-master/ .

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Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction 1

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1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical image Computing and computer-Assisted Intervention, MICCAI 2019

作者： Dzyubachyk, Oleh Koolstra, Kirsten Pezzotti, Nicola Lelieveldt, Boudewijn P. F. Webb, Andrew Börnert, Peter Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands C.J. Gorter Center for High Field MRI Department of Radiology Leiden University Medical Center Leiden Netherlands Computer Graphics and Visualization Group Delft University of Technology Delft Netherlands Philips Research Eindhoven Eindhoven Netherlands Intelligent Systems Department Delft University of Technology Delft Netherlands Philips Research Hamburg Hamburg Germany

ISBN: (数字)9783030358174

ISBN: (纸本)9783030358167

Quality assessment of different Magnetic Resonance Fingerprinting (MRF) sequences and their corresponding dictionaries remains an unsolved problem. In this work we present a method in which we approach analysis of MRF dictionaries by performing dimensionality reduction and representing them as low-dimensional point sets (embeddings). Dimensionality reduction was performed using a modification of the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm. First, we demonstrated stability of calculated embeddings that allows neglecting the stochastic nature of t-SNE. Next, we proposed and analyzed two algorithms for comparing the embeddings. Finally, we performed two simulations in which we reduced the MRF sequence/dictionary in length or size and analyzed the influence of this reduction on the resulting embedding. We believe that this research can pave the way to development of a software tool for analysis, including better understanding, optimization and comparison, of different MRF sequences. © 2019, Springer Nature Switzerland AG.

关键词： Magnetic resonance

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Extending the Measurement Error Model of a Direct Visual Odometry Algorithm to Improve its Accuracy for Planetary Rover Navigation

Extending the Measurement Error Model of a Direct Visual Odo...

引用

Applied Science and Advanced Technology (iCASAT), 2019 IEEE International Conference on

作者： Geovanni Martinez Image Processing and Computer Vision Research Laboratory (IPCV-LAB) Escuela de Ingeniería Eléctrica Universidad de Costa Rica San José Costa Rica

ISBN: (数字)9781728131108

ISBN: (纸本)9781728131115

In this paper, the accuracy of a direct monocular visual odometry algorithm is improved. The algorithm is able to determine the position and orientation of a robot directly from intensity differences measured at observation points between consecutive images, captured by a monocular camera, rigidly attached to one side of its structure, tilted downwards. The improvement was achieved by extending the stochastic model of the intensity-difference measurement error, from considering only the camera noise, to one that also considers the intensity-difference measurement error due to the 3D shape error between the assumed and the true planetary surface shape. The corresponding covariance matrix was incorporated into a Maximum Likelihood estimator. According to the experimental results on irregular surfaces, where the 3D shape error is usually large, the accuracy of the visual odometry algorithm improved by a factor of 2 but with the cost of increasing the processing time also by the same factor.

关键词： Three-dimensional displays Cameras Measurement errors Visual odometry Robot vision systems Shape

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Drugs with SMILES Similar to Coxibs

Drugs with SMILES Similar to Coxibs

引用

IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： George Kallergis Stelios Sfakianakis Michael Zervakis Marios Spanakis Digital Image and Signal Processing Laboratory School of Electrical and Computer Engineering Technical University of Crete Polytechnioupolis Chania Greece Computational Biomedicine Laboratory Institute of Computer Science Foundation for Research & Technology Hellas (FORTH) Heracklion Greece

Coxibs are a group of drugs with selective inhibition against cyclooxygenase-2 (COX-2) enzymes with increased interest from scientific community due to their side effects and potential other pharmacological mechanisms. The aim of this work is to utilize the chemical characteristics of coxibs in order to identify compounds with similar chemical structure. The approach is based on the assessment of the Simplified Molecular-Input Line-Entry System (SMILES) as adequate molecular structure representations for the identification of drug similarities. The similarity measurements are based on molecular fingerprints that were extracted from coxibs and the Maximum Consecutive Subsequence (MCS) algorithm. An ensemble of methods based on majority voting, weighting and equal weighting on the algorithms was further applied. Majority voting returned 200 similar compounds whereas weighting and equal weighting returned 53 and 27 compounds respectively. Interestingly, despite the independence of the methods, all three identified 20 common compounds. The identification of drugs with potential chemical similarity with coxibs, as revealed from similarity measurements of fingerprints and MCS scores could provide new insights for potential biological targets for coxibs or drugs that could interact with COX-2 or other biological targets of coxibs.

关键词： Drugs Fingerprint recognition Compounds Chemicals Biological information theory Inhibitors Digital images

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Reversible adversarial information hiding based on the interpretation of neural network

引用

Digital Signal processing: A Review Journal 2025年 165卷

作者： Li, Ruiping Wang, Xi Cui, Qingchen Li, Ming Information Construction and Management Office Henan Normal University Xinxiang453007 China School of Artificial Intelligence and Software Engineering Nanyang Normal University Nanyang473061 China Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image Henan Nanyang473000 China College of Information Engineering Zhoukou Vocational College of Arts and Science Zhoukou466000 China College of Computer and Information Engineering Henan Normal University Xinxiang453007 China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Xinxiang453007 China

The emerging technology of adversarial information hiding can generate adversarial example by embedding useful information instead of meaningless noise into the host image. The obtained image can deceive classification models and therefore equip the function of privacy protection. However, existing research mainly focuses on the effectiveness of attack rather than preserving the integrity of the protected image. It cannot meet the requirements in the fields such as medical imaging, financial transactions, and copyright protection where users' raw data should not be damaged. Therefore, we propose a novel reversible adversarial information hiding based on interpretability of neural networks. The Grad-CAM-generated heatmap is employed to identify a minimal set of high-impact pixels for embedding, ensuring that minor modifications can induce significant misclassification. Then, the user-defined secret bits are embedded into the identified pixels by using difference expansion. After extraction, the original image can be perfectly restored. The technologies of adversarial example and reversible information hiding are combined to accommodate wider applications. The proposed method is tested on CIFAR-10 with three different neural architectures (NiN, AlexNet, ResNet). Experimental results show that the proposed method can completely restore the cover image while ensuring the extraction of the embedded data and misleading the neural network. © 2025 Elsevier Inc.

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