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CrossFuse: Learning Infrared and Visible Image Fusion by Cross-Sensor Top-K Vision Alignment and Beyond

IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Shi, Yukai Shi, Cidan Weng, Zhipeng Tian, Yin Xian, Xiaoyu Lin, Liang Guangdong University of Technology School of Information Engineering Guangzhou510006 China Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Hefei230601 China CRRC Academy Co. Ltd Beijing China Sun Yat-sen University School of Computer Science Guangzhou510006 China

Infrared and visible image fusion (IVIF) is increasingly applied in critical fields such as video surveillance and autonomous driving systems. Significant progress has been made in deep learning-based fusion methods. However, these models frequently encounter out-of-distribution (OOD) scenes in real-world applications, which severely impact their performance and reliability. Therefore, addressing the challenge of OOD data is crucial for the safe deployment of these models in open-world environments. Unlike existing research, our focus is on the challenges posed by OOD data in real-world applications and on enhancing the robustness and generalization of models. In this paper, we propose an infrared-visible fusion framework based on Multi-View Augmentation. For external data augmentation, Top-k Selective Vision Alignment is employed to mitigate distribution shifts between datasets by performing RGB-wise transformations on visible images. This strategy effectively introduces augmented samples, enhancing the adaptability of the model to complex real-world scenarios. Additionally, for internal data augmentation, self-supervised learning is established using Weak-Aggressive Augmentation. This enables the model to learn more robust and general feature representations during the fusion process, thereby improving robustness and generalization. Extensive experiments demonstrate that the proposed method exhibits superior performance and robustness across various conditions and environments. Our approach significantly enhances the reliability and stability of IVIF tasks in practical applications. © 1991-2012 IEEE.

关键词： Image fusion

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Highly-Accurate Robot Calibration Based on Plane Constraint via Integrating Square-Root Cubature Kalman filter and Levenberg-Marquardt Algorithm

Highly-Accurate Robot Calibration Based on Plane Constraint ...

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IEEE International Conference on Networking, Sensing and Control

作者： Tinghui Chen Shuai Li Hao Wu School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing School University of Chinese Academy of Sciences Chongqing China Faculty of Science and Engineering and also affiliated to Zienkiewicz Centre for Computational Engineering Swansea University Swansea UK College of Computer and Information science Southwest University Chongqing China

ISBN: (纸本)9781665472449

In the field of modern industrial manufacturing, industrial robots are indispensable intelligent automatic mechanical equipment for advanced industrial production. However, due to long-term mechanical wear and structural deformation, the absolute positioning accuracy is low, which greatly hinders the development of the manufacturing industry. Calibrating the kinematic parameters of the robot is an effective way to address it. However, the main measuring equipment such as laser trackers and coordinate measuring machines are expensive and need special personnel to operate. Additionally, in the measurement process, due to the influence of extensive environmental factors, measurement noises are generated affecting the calibration accuracy of the robot. Based on these, we have done the following work: a) developing a robot calibration method based on plane constraint to simplify measurement steps; b) employing square-root culture Kalman filter (SCKF) algorithm for reducing the influence of measurement noises; c) proposing a novel algorithm for identifying kinematic parameters based on SCKF algorithm and Levenberg-Marquardt (LM) algorithm to achieve the high calibration accuracy; d) adopting the dial indicator as the measuring equipment for slashing costs. Enough experiments verify the effectiveness of the proposed calibration algorithm and experimental platform.

关键词： Coordinate measuring machines Laser radar Service robots Robot kinematics Laser noise Radar tracking Laser modes

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Precise Interception Flight Targets by Image-based Visual Servoing of Multicopter

arXiv

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arXiv 2024年

作者： Yan, Hailong Yang, Kun Cheng, Yixiao Wang, Zihao Li, Dawei School of Computer Science Northwestern Polytechnical University Xi’an710129 China School of Automation Science and Electrical Engineering School of Future Aerospace Technology Institute of Unmanned Systems Beihang University Beijing100191 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China

Vision-based interception using multicopters equipped strapdown camera is challenging due to camera-motion coupling and evasive targets. This paper proposes a method integrating Image-Based Visual Servoing (IBVS) with proportional navigation guidance (PNG), reducing the multicopter’s overload in the final interception phase. It combines smoother trajectories from the IBVS controller with high-frequency target 2D position estimation via a delayed Kalman filter (DKF) to minimize the impact of image processing delays on accuracy. In addition, a field-of-view (FOV) holding controller is designed for stability of the visual servo system. Experimental results show a circular error probability (CEP) of 0.089 m (72.8% lower than the latest relevant IBVS work) in simulations and over 80% interception success under wind conditions below 4 m/s in real world. These results demonstrate the system’s potential for precise low-altitude interception of non-cooperative targets. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual servoing

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Sign Language Detection Using Action Recognition LSTM Deep Learning Model

Sign Language Detection Using Action Recognition LSTM Deep L...

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Network, Multimedia and Information technology (NMITCON) International Conference on

作者： N Snehalatha S Mohana Kumar H Kavitha S Nisha Puttabore Gowda Sj Krishna Prasad Dept. of Computer Science and Engineering JSS Academy of Technical Education Bangalore India Dept. of CSE(Cyber Security) Ramaiah Institute of Technology Bangalore India Dept. of Information Science Siddaganga Institute of Technology Tumukur India Dept. of CSE(AI and ML) Ramaiah Institute of Technology Bangalore India Dept. of ME Ramaiah Institute of Technology Bangalore India Dept. of EC University Visvesvaraya College of Engineering Bangalore India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

This project aims to develop a system that can recognize sign language gestures in real-time using computer vision techniques. The system is designed to bridge the communication gap between individuals with different hearing abilities by enabling accurate and effective communication through sign language. To achieve this, an image datasets is created, consisting of labeled images of sign language gestures for alphabets, digits, or other signs. This datasets is used to train a deep neural network using transfer learning techniques. Transfer learning involves leveraging the knowledge gained from pre-trained models, such as VGG-16 and ResNet, to accelerate the training process and improve the accuracy of the model. The trained deep neural network is then deployed in a real- time setting where it processes video feeds or live camera feeds to recognize and interpret sign language gestures. The system is designed to be user-friendly, with an intuitive interface that can be easily used by individuals with hearing difficulties or those who do not understand sign language The accuracy of the system is evaluated using various performance metrics such as accuracy, precision, recall, and F1 score. The system is tested in different environments, including noisy and low-light settings, to ensure that it can recognize sign language gestures accurately. Overall, this work demonstrates the potential of computer vision and deep learning techniques to improve communication for individuals with hearing difficulties. It also highlights the importance of developing accessible technologies that can enable effective communication and improve the quality of life for individuals with disabilities

关键词： Deep learning Sign language computer vision Accuracy Face recognition Transfer learning Auditory system Real-time systems Mobile applications Feeds

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A Survey on Physical Adversarial Attack in computer Vision

arXiv

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arXiv 2022年

作者： Wang, Donghua Yao, Wen Jiang, Tingsong Tang, Guijian Chen, Xiaoqian College of Computer Science and Technology Zhejiang University Hangzhou China Defense Innovation Institute Chinese Academy of Military Science Beijing China College of Aerospace Science and Engineering National University of Defense Technology Changsha China

Over the past decade, deep learning has revolutionized conventional tasks that rely on hand-craft feature extraction with its strong feature learning capability, leading to substantial enhancements in traditional tasks. However, deep neural networks (DNNs) have been demonstrated to be vulnerable to adversarial examples crafted by malicious tiny noise, which is imperceptible to human observers but can make DNNs output the wrong result. Existing adversarial attacks can be categorized into digital and physical adversarial attacks. The former is designed to pursue strong attack performance in lab environments while hardly remaining effective when applied to the physical world. In contrast, the latter focus on developing physical deployable attacks, thus exhibiting more robustness in complex physical environmental conditions. Recently, with the increasing deployment of the DNN-based system in the real world, strengthening the robustness of these systems is an emergency, while exploring physical adversarial attacks exhaustively is the precondition. To this end, this paper reviews the evolution of physical adversarial attacks against DNN-based computer vision tasks, expecting to provide beneficial information for developing stronger physical adversarial attacks. Specifically, we first proposed a taxonomy to categorize the current physical adversarial attacks and grouped them. Then, we discuss the existing physical attacks and focus on the technique for improving the robustness of physical attacks under complex physical environmental conditions. Finally, we discuss the issues of the current physical adversarial attacks to be solved and give promising directions. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Self-adaptive vision-language model for 3D segmentation of pulmonary artery and vein

arXiv

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arXiv 2025年

作者： Guo, Xiaotong Yang, Deqian Wang, Dan Zhao, Haochen Li, Yuan Sui, Zhilin Zhou, Tao Zhang, Lijun Meng, Yanda State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Department of Thoracic Surgery National Clinical Research Center for Cancer Cancer Hospital Shenzhen Hospital China School of Intelligent Science and Technology Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou310024 China R&D Center Guangxi Huayi Artificial Intelligence Medical Technology Co. Ltd. China School of Computer Science and Engineering Beihang University Beijing China Department of Computer Science University of Exeter Exeter United Kingdom Department of Cardiovascular & Metabolic Medicine University of Liverpool Liverpool United Kingdom Guangzhou Jiayi Software Technology Co. Ltd. China

Accurate segmentation of pulmonary structures is crucial in clinical diagnosis, disease study, and treatment planning. Significant progress has been made in deep learning-based segmentation techniques, but most require much labeled data for training. Consequently, developing precise segmentation methods that demand fewer labeled datasets is paramount in medical image analysis. The emergence of pre-trained vision-language foundation models, such as CLIP, recently opened the door for universal computer vision tasks. Exploiting the generalization ability of these pre-trained foundation models on downstream tasks, such as segmentation, leads to unexpected performance with a relatively small amount of labeled data. However, exploring these models for pulmonary artery-vein segmentation is still limited. This paper proposes a novel framework called Language-guided self-adaptive Cross-Attention Fusion Framework. Our method adopts pre-trained CLIP as a strong feature extractor for generating the segmentation of 3D CT scans, while adaptively aggregating the cross-modality of text and image representations. We propose a s pecially designed adapter module to fine-tune pre-trained CLIP with a self-adaptive learning strategy to effectively fuse the two modalities of embeddings. We extensively validate our method on a local dataset, which is the largest pulmonary artery-vein CT dataset to date and consists of 718 labeled data in total. The experiments show that our method outperformed other state-of-the-art methods by a large margin. Our data and code will be made publicly available upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

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A 52-Gb/s Sub-1-pJ/bit PAM4 Receiver in 40-nm CMOS for Low-Power Interconnects

IEEE Open Journal of Circuits and Systems

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IEEE Open Journal of Circuits and Systems 2021年 2卷 46-55页

作者： Wang, Can Wang, Li Zhang, Zhao Mahmoudabadi, Milad Kalantari Shi, Weimin Yue, C. Patrick Department of Electronic and Computer Engineering HKUST-Qualcomm Optical Wireless Lab Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Superlattices and Microstructures Institute of Semiconductors Chinese Academy of Sciences Beijing China HKUST Shenzhen Research Institute Shenzhen518057 China

This article presents a quarter-rate source-synchronous PAM-4 receiver for energy-efficient chip-to-module communication. A novel single-stage multiple peaking continuous-time linear equalizer (MP-CTLE) using feedback enabled multiple peaking scheme for both high-frequency equalization (HF-EQ) and low-frequency equalization (LF-EQ) is proposed to improve the BER performance and overall energy efficiency. The LF-EQ of the MP-CTLE eliminates the need for many DFE taps in SR/VSR applications to save power and area. A 1-tap feedforward equalizer (FFE) is used to further compensate for the high-frequency loss. We also use a ring oscillator based wide bandwidth phase-locked loop (WBW-PLL) as the multiphase clock generator (MPCG) in the clock and data recovery loop to save power with acceptable phase accuracy. Fabricated in 40-nm CMOS technology, the prototype receiver chip achieves error-free operation up to 52 Gb/s PAM-4 with superior bit efficiency of 0.126pJ/bit/s/dB while compensating 7.3-dB channel loss at 13GHz. With the proposed single-stage MP-CTLE, the receiver extends the error-free operation from PRBS-7 to PRBS-9. The BER bathtub curve at 10-6 is improved from 0.018 UI to around 0.1 UI. © 2020 IEEE.

关键词： Pulse amplitude modulation

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Order-preserving pattern mining with forgetting mechanism

arXiv

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arXiv 2024年

作者： Li, Yan Ma, Chenyu Gao, Rong Wu, Youxi Li, Jinyan Wang, Wenjian Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin300400 China School of Artificial Intelligence Hebei University of Technology Tianjin300400 China School of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Computer and Information Technology Shanxi University Taiyuan237016 China Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology Hefei230009 China

Order-preserving pattern (OPP) mining is a type of sequential pattern mining method in which a group of ranks of time series is used to represent an OPP. This approach can discover frequent trends in time series. Existing OPP mining algorithms consider data points at different time to be equally important;however, newer data usually have a more significant impact, while older data have a weaker impact. We therefore introduce the forgetting mechanism into OPP mining to reduce the importance of older data. This paper explores the mining of OPPs with forgetting mechanism (OPF) and proposes an algorithm called OPF-Miner that can discover frequent OPFs. OPF-Miner performs two tasks, candidate pattern generation and support calculation. In candidate pattern generation, OPF-Miner employs a maximal support priority strategy and a group pattern fusion strategy to avoid redundant pattern fusions. For support calculation, we propose an algorithm called support calculation with forgetting mechanism, which uses prefix and suffix pattern pruning strategies to avoid redundant support calculations. The experiments are conducted on nine datasets and 12 alternative algorithms. The results verify that OPF-Miner is superior to other competitive algorithms. More importantly, OPF-Miner yields good clustering performance for time series, since the forgetting mechanism is employed. Copyright © 2024, The Authors. All rights reserved.

关键词： Miners

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Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection

arXiv

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arXiv 2024年

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-Sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Counteracting Unobserved Cyber-Attacks in AGC and HVDC Schemes Using a Multi-Agent Framework

Counteracting Unobserved Cyber-Attacks in AGC and HVDC Schem...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： P. Chandrakala B Reddy Gautham Krishna Shalini Y. Nigam V. Mathan Kumar Sourav Rampal Department of EEE Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of Civil Engineering JAIN School of Engineering and Technology Deemed to be University Bangalore Karnataka India Computer Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

A novel approach to safeguard the AGC circlet and supremacy modulation controller of a high voltage direct current system from cyber-attacks is discussed in this research employing a multi-agent-based architecture. To achieve this, we propose adding a Master Agent (MA) and many Slave Agents (SAs) into the current AGC layer. For intrusion detection, the wide-area signals of the MA are processed employing a OCC. Used in the problem domain to minimize attacks by the SAs, are SVR models that utilize local knowledge. The suggested method moves to another training method for OCC which excludes the use of instances that are contaminated by noise in favor of protected data. The next step is to propagate a structure that the AGC system may employ for setting the online SVR models. When evaluating the tested solution’s performance, we consider a set of test scenarios that reflect the secured case, which includes normal and stressed system behavior; the attacked case, which pertains to various types of threats targeting data integrity and availability, as well as grid states. Interestingly, there is decent accuracy in identifying the assaults using the OCC, specifically the SVDDM. It is also observed that the SVR models operate well and optimally in terms of performance and at the same time effectively counter cyber-attacks.

关键词： Training HVDC transmission Noise Modulation Intrusion detection Reinforcement learning Main-secondary Vectors Real-time systems Cyberattack

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