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检索条件"任意字段=Proceedings of the 3rd International Conference on Control and Computer Vision"

共 18814 条记录，以下是801-810 订阅

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Designing camouflage scheme by modeling 3D virtual battlefield environment 3

Designing camouflage scheme by modeling 3D virtual battlefie...

引用

3rd international conference on computer vision and Data Mining, ICCVDM 2022

作者： Yang, Di Xie, Zhenping School of Artificial Intelligence and Computer Science Jiangnan University No.1800 Lihu Avenue Jiangsu Wuxi214122 China Jiangsu Key Laboratory of Media Design and Software Technology Jiangnan University No.1800 Lihu Avenue Jiangsu Wuxi214122 China

ISBN: (纸本)9781510661363

A camouflage design strategy based on battlefield environment twinning is proposed to address the camouflage concealment of military equipment in a realistic battlefield environment. The strategy is based on the construction of a three-dimensional battlefield environment digital twin model, and by synthesizing the digital camouflage based on the background primary color and natural camouflage decoration, a camouflage design scheme with a higher camouflage success rate can be generated to fit the realistic battlefield environment. A neural network-based image segmentation network and a target detection network are used to evaluate the performance of the camouflage design scheme. The experiment results show that the camouflage design strategy proposed in this paper has better interference and confrontation with detection technology, and it can provide the most adaptable camouflage design scheme for the target object in the real battlefield environment, which has strong practical value and strategic significance. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image segmentation

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Early Stage Skin Cancer Detection Using Image Processing 3

Early Stage Skin Cancer Detection Using Image Processing

引用

3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Rajulu, Lanke Govinda Madan, Parul Varshney, Neeraj Yadav, Dinesh Set, Ashok Kumar Thakur, Anil ResMed Digital Health Technology United States Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Gla University Department of Computer Engineering and Applications Mathura India Lloyd Institute of Engineering and Technology Greater Noida India Quantum University Uttarakhand Roorkee India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350322842

Early stage malignant cancer detection has gain utmost research importance in recent times. Carcinoma, which is currently acknowledged as the deadliest form of cancer in humans, appears in various types such as skin cancer, basal cell carcinoma, squamous cell carcinoma, and the relatively unexpected malignant melanoma. Timely identification of malignant melanoma is crucial for providing effective treatment. computer vision systems have played a significant role in medical image classification, offering valuable contributions in this field. In this study, we propose a computer-aided method that relies on images to detect melanoma, a type of skin cancer. The system takes an image of a skin lesion as input and employs innovative image processing techniques to assess and determine the presence or absence of skin cancer. By analyzing factors such as texture, size, and segmentation, lesion image analysis tools examine a wide range of features associated with melanoma, including spatial characteristics, contours, color, diameter, and more. Based on derived feature parameters, the images are classified as either indicating melanoma or representing benign skin lesions. © 2023 IEEE.

关键词： Image analysis

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TR-Former: Token Based Residual Transformer for Single Image Denoising 3

TR-Former: Token Based Residual Transformer for Single Image...

引用

3rd international conference on Consumer Electronics and computer Engineering, ICCECE 2023

作者： Li, Yihan School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350331578

In recent years, people's demand for photography has been increasing with the development of smartphones and cameras. In order to get higher-quality photos, more and more people pay attention to the field of image restoration, especially the image denoising task. The recently proposed vision transformer model, accompanied by the breakthrough of deep learning, has also been successfully applied to image restoration problems. In this paper, we first design the TokenWin Transformer Block with a local branch and a global branch to capture both the local and global features of an image. In order to improve the denoising performance of the network, we then add a Gate-Residual structure on the existing basis to obtain richer features of samples. This scheme selects more effective features of denoising to feed forward, which strengthens the network's information capture capability. Finally, the effectiveness of this method is verified by our experimental tests. © 2023 IEEE.

关键词： Image denoising

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Empirical Evaluation of Real-Time Diabetic Retinopathy Disease Detection using IoT Enabled Medical Imaging Processing Strategy 3

Empirical Evaluation of Real-Time Diabetic Retinopathy Disea...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Shifani, S. Agnes Saraswathy, N. Margaret Clemencia, A. Santhi, G.B. Giri, Jayant Al-Mousa, Mohammad Rasmi Saveetha University Saveetha School of Engineering Department of Ece Chennai India Department of Chemistry Thanjavur India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University College of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331509675

Diabetic Retinopathy (DR) poses a significant risk to vision among diabetic patients, necessitating early and accurate detection for effective intervention. This research introduced a novel methodology to detect the diabetic retinopathy in an efficient way. In contrast to the existing technique called Convolutional Neural Network (CNN), the proposed approach called Hybrid Learning assisted Diabetic Retinopathy Predictor (HLDRP) allows for easy and quick implementation and offers patients direct access to diagnostic equipment. By leveraging a hybrid learning model that incorporates both supervised and unsupervised learning techniques, HLDRP enhances the accuracy and reliability of DR diagnosis. The IoT component facilitates continuous monitoring and data acquisition from patients, while the hybrid learning model analyzes this data to detect early signs of DR and predict disease progression. Experimental results indicate that HLDRP offers superior diagnostic performance compared to traditional methods, with increased sensitivity and specificity. This integrated approach not only improves early detection but also enables personalized treatment planning, potentially transforming the management of diabetic retinopathy through innovative technology and advanced analytics. Through comprehensive evaluation using a diverse dataset of retinal images, HLDRP demonstrates significant improvements in diagnostic performance, including increased sensitivity and specificity compared to traditional detection methods. The results underscore HLDRP's potential to facilitate early intervention and personalized treatment, providing a valuable tool for healthcare professionals in managing and preventing diabetic retinopathy. © 2025 IEEE.

关键词： Convolutional neural networks

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control of the Boiler Turbine Unit Based on Active Disturbance Rejection control 3

Control of the Boiler Turbine Unit Based on Active Disturban...

引用

3rd IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic control and computer Engineering, MI-STA 2023

作者： Zrigan, Ahmed F. Shi, Gengjin Altrmal, Hossam A. Elsouri, Khaled S. Libyan Academy for Post Studies Dept. of Electrical & Computer Engineering Tripoli Libya Tsinghua University Beijing China China Power International Development Ltd. State Power Investment Corporation Beijing100080 China Higher Institute of Science and Technologies Dept. of Electrical & Electronics Engineering Tripoli Libya

ISBN: (纸本)9798350319897

The boiler turbine unit was controlled for that operating point using the optimal controller (PSO) and the controller (ADRC), and from that concordance implementation, we can choose the best controller at this operating point. This paper describes the development of operating the boiler unit at an operating point lower than the equilibrium point, by selectively selecting that point to avoid nonlinearity in this process, and it also shows us that this unit is nonlinear. © 2023 IEEE.

关键词： controllers

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A Lightweight Mamba Backbone for Diffusion Models 3

A Lightweight Mamba Backbone for Diffusion Models

引用

3rd international conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2024

作者： Zhou, Tao Yu, Yunfan Gao, Zihan Jiangnan University School of Science Wuxi214000 China Shanghai Wicresoft Co. Ltd. Wuxi Branch Wuxi214000 China Jiangnan University School of Artificial Intelligence and Computer Science Wuxi214000 China

ISBN: (纸本)9798331506582

Recent advances introduced the Visual Mamba (Vim) architecture, featuring bidirectional Mamba blocks, which has demonstrated exceptional performance across various computer vision tasks. This paper extends the application of Vim to diffusion models. However, Vim's sequential processing design overlooks spatial continuity and local structure in images. To address this, we propose AMamba, a novel diffusion model backbone that combines the computational efficiency of Vim with the powerful attention mechanisms of U- ViT, aiming to overcome U-ViT's high computational complexity and parameter count while enhancing spatial continuity modeling. By replacing U-ViT's MLPs with Vim blocks, AMamba efficiently captures sequential dependencies inherent in diffusion processes. Furthermore, time-step and conditional information are embedded directly into the input sequence, enhancing information flow during the diffusion process. The architecture also incorporates long skip connections across layers, which facilitate efficient information propagation and contribute to faster convergence. Experimental results show that AMamba achieves significantly lower FID scores on the CelebA64 dataset compared to U-ViT and Vim, while requiring fewer training iterations and significantly reducing the number of model parameters. © 2024 IEEE.

关键词： computer architecture

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Research on English Data Intelligent Retrieval Terminal System Based on computer 3D Image vision Technology 3

Research on English Data Intelligent Retrieval Terminal Syst...

引用

2023 IEEE 3rd international conference on Data Science and computer Application, ICDSCA 2023

作者： Liu, Chang Yang, Lei Xi'an Eurasia University Xi'an China

ISBN: (纸本)9798350341546

This paper introduces an embedded software for English information query based on ARM single chip microcomputer LPC2290 and Mini GUI. Integrate the Mini GUI with network communication. Remote access to MySQL using C/S architecture. The hardware and software architecture of English data intelligent retrieval terminal system is presented. The graphical interface of the system and the data extraction of the system are completed based on Mini GUI. Several main implementation links of the system are also explained. This paper presents an intelligent query terminal based on English data, which improves the user interface very well. It can not only realize the intelligent search of English data, but also solve the expensive problem of the traditional intelligent search terminal of English data. © 2023 IEEE.

关键词： 3D image English materials intelligent retrieval terminal system visual technology

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Age and Gender Classification Using Convolution Neural Network 3

Age and Gender Classification Using Convolution Neural Netwo...

引用

3rd IEEE Delhi Section Flagship conference, DELCON 2024

作者： Javeriya, Thonduru Anjum Banshal, Sumit Kumar Thangavel, Gnanaprakasam Moni, Raka Alliance University Department of Computer Science and Engineering Bangalore India Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9798331518592

This work aims to improve the accuracy of computer vision and deep learning methods for age and gender prediction in real time from individual photos or video frames. Automated techniques to recognize these characteristics have grown in significance with the emergence of social media platforms. Still, there are issues with the current approaches, especially with regard to extraneous elements that might skew forecasts, such as cosmetics, lighting, and facial expressions. Our work aims to overcome these difficulties by applying advanced algorithms that accurately assess an individual's gender and estimate their age within a given range. The goal of the project is to analyze visual input in real-time for image and video analysis by combining convolutional neural network (CNN) with open Cv. Because the model is taught to recognize and adjust to real-world situations, it is more resilient to change in environmental variables and picture quality. Our investigation shows that while determining an individual's precise age is still challenging, accuracy is much increased when estimating age ranges. Additionally, across a range of circumstances, the model produces accurate gender predictions. These results demonstrate how age and gender estimates may be enhanced by using deep learning and real-time processing techniques. Finally, this work shows that more robust age and gender prediction algorithms may be developed, which can find applications in social media and security, among other domains, by tackling the problems posed by the intrinsic heterogeneity of real-world images. Keywords-Automatic Age and Gender Identification, Real-time Processing, Deep Learning, Image and Video Analysis, Age Range Classification, Facial Feature Extraction. © 2024 IEEE.

关键词： Convolutional neural networks

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Blindness Risk Prediction caused by Diabetic Retinopathy from Retinal Image 3

Blindness Risk Prediction caused by Diabetic Retinopathy fro...

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3rd international conference on Electrical, computer and Communication Engineering, ECCE 2023

作者： Paul, Laboni Talukder, Kamrul Hasan Khulna University Computer Science and Engineering Discipline Khulna Bangladesh

ISBN: (纸本)9798350345360

Type-II diabetes is growing at an alarming rate worldwide. Eventually, it leads to visual impairment by damaging the retinal blood vessels, which is known as Diabetic Retinopathy (DR). A plethora of research is ongoing to automate the early detection of DR to help the doctor with periodic eye examinations due to advancements in AI based computer vision algorithms and camera technology. In this paper, we have proposed a comparison between two widely used very deep convolutional neural network architectures (ResNet-101 v2, InceptionResNet V2) with the comparatively new optimized and composite scalable architecture (EfficientNet B5) with while training them from scratch and using them as a pre-trained to transfer learning. We have found out the pre-trained EfficientNet B5 has outperformed our other candidates as well as available methods in the current literature by achieving accuracy of 97.78%. We also provide detailed enough information to make the result reproducible. © 2023 IEEE.

关键词： Risk assessment

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Pest and Disease Detection Based on Data Augmentation 3

Pest and Disease Detection Based on Data Augmentation

引用

2023 IEEE 3rd international conference on Data Science and computer Application, ICDSCA 2023

作者： Jia, Wenjun Yang, Nan Lu, Yiping Deng, Peng Southwest Petroleum University School of Computer Science Nanchong637001 China

ISBN: (纸本)9798350341546

Pest identification plays an increasingly important role in agricultural production, and effective control measures based on the results can reduce the impact of pests and diseases and ensure higher and more stable yields. Advances in machine learning and deep learning, especially in image recognition, provide a better way to identify pests and diseases. EfficientNet-B3 neural network was used to identify and classify healthy crops and diseased plants with 98% accuracy, and data enhancement technology was used to address the insufficient amount of data. © 2023 IEEE.

关键词： data augmentation deep learning EfficientNet-B3 network pest and disease identification

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