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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems IV"

共 13657 条记录，以下是841-850 订阅

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Research on an Innovative Algorithm for Optimizing Intelligent image Data systems Based on Deep Learning

Research on an Innovative Algorithm for Optimizing Intellige...

引用

IEEE International conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Xiaonan Dong Yingbin Song Wuhan Technology and Business University Wuhan Hubei China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

This paper proposes an innovative algorithm for optimizing intelligent image data systems based on deep learning. The algorithm combines image feature extraction, data preprocessing and efficient optimization strategies to improve the performance and accuracy of image data processing systems. First, by designing a deep CNN architecture, important features in the image are extracted to achieve efficient completion of image recognition and classification tasks. Subsequently, a new multi-level data processing method is proposed, which can optimize image data at different levels, thereby improving processing speed and reducing noise interference. Through a series of simulation experiments, the results show that the image classification accuracy of the algorithm is improved by about 12 % , from 85.6% of the traditional method to 97.3%. In addition, the processing efficiency is improved by about 20%, the data processing time is reduced from 2.5 seconds of the traditional method to 2 seconds, and the stability of the system is significantly enhanced by introducing optimization strategies, and the stability is improved by about 18%. The optimized algorithm shows significant advantages in both accuracy and efficiency, meeting the needs of efficient intelligent image processing systems.

关键词： Deep learning Accuracy image recognition Noise Data processing Feature extraction Stability analysis Classification algorithms Optimization image classification

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ISAR IMAGING OF IFDS ECHO BASED ON GPU PARALLEL processing

ISAR IMAGING OF IFDS ECHO BASED ON GPU PARALLEL PROCESSING

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IET International Radar conference 2023, IRC 2023

作者： Lei, Junfeng Liu, Tao Tian, Biao School of Electronics and Communication Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen China

ISBN: (纸本)9781839539954

The Intermediate Frequency Direct Sampling (IFDS) mode in Inverse Synthetic Aperture Radar (ISAR) imaging preserves the original information of the echo signal and radar system characteristics. However, the massive data accompanying this mode imposes considerable computational burdens on subsequent steps. As a result, traditional ISAR imaging algorithms and implementations face challenges in achieving fast, real-time imaging in IFDS mode. This paper presents the design and implementation of an IFDS echo ISAR imaging system based on parallel computing using Graphic processing Unit (GPU). The efficiency and effectiveness of the proposed system were validated on the simulated data. Significant performance improvements compared to traditional CPU-bound ISAR imaging systems were demonstrated in the results. These improvements were achieved with minimal impact on the final quality of the two-dimensional ISAR image. © The Institution of Engineering & Technology 2023.

关键词： Radar imaging

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Application of 3D image Virtual Reconstruction in Multimedia Element Visual Communication Art Design 4

Application of 3D Image Virtual Reconstruction in Multimedia...

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4th IEEE Annual Flagship India Council International Subsections conference, INDISCON 2023

作者： Wang, Jing Wang, Xiaopeng The College of Arts College of Humanities & Sciences of Northest Normal University Changchun China Changchun Institute of Optics Fine Mechanies and Physics Chinese Academy of Seience The Space Department Changchun China

ISBN: (纸本)9798350333558

Aiming at the problem of large noise and artifacts generated by the first 3D image virtual reconstruction system, a 3D image virtual reconstruction system based on visual communication technology is proposed. In this paper, cy25400 chip and cdcvf2510 chip are used to optimize the original hardware model, expand the clock module, and improve the data processing efficiency;In the two-dimensional image, read the main data, display it in three-dimensional form from the converted data, select special areas, extract image features, count the main points of the joint, and complete the main writing;Using visual communication tools to feed back the visual content of three-dimensional images, a virtual reconstruction system of three-dimensional images is created. The effectiveness of the proposed method is tested by comparison. The experimental results show that the reconstruction accuracy of the system is always higher than that of the laser Doppler image 3D reconstruction system based on virtual machine and the DICOM image 3D reconstruction system based on MC and RC algorithms. The maximum reconstruction accuracy of this system is as high as 0.97. The user satisfaction of the system is very important, and the satisfaction is higher than 0.95. Result finding that compared with the reconstruction results of the old methods, the virtual construction of 3D images using visual communication technology can remove noise, make the image area clear, and meet the needs of customers. © 2023 IEEE.

关键词： Visual communication

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Transforming Medical Waste Management Through IoT and Machine Learning: A Path Towards Sustainability

Transforming Medical Waste Management Through IoT and Machin...

引用

Computing conference

作者： Nasir, Nahin Hossain, Mohammed Shahriar Islam, Md Saiful Islam, Md Arifiul Islam, Md Manirul Amer Int Univ Bangladesh Dhaka 1229 Bangladesh

ISBN: (纸本)9783031622793;9783031622779;9783031622762

The systematic process of detecting, gathering, and disposing of all waste in a specific manner so as not to jeopardize human or environmental life is known as waste management. If not correctly managed, medical waste, which includes items like old needles, sharps, and infectious materials, poses a serious risk to the environment and the general public's health, especially in developing and underdeveloped countries. In this study, we used IoT and machine learning technologies to automate waste identification, information tracking, and monitoring, improve the accuracy and efficiency of waste bins, and enable real-time monitoring and analysis of waste information. By utilizing smart waste bins fitted with sensors and machine learning algorithms to automatically detect and classify various forms of waste, IoT and machine learning are being applied to the management of medical waste. These intelligent bins can then notify waste management staff when they need to be emptied and can offer useful information on waste streams for analysis and practice improvement. Only persons who have been authenticated will be permitted to collect medical waste, and the system will keep a record of all data. Using IoT and machine learning technologies, we attempted to reduce the danger of unintentional exposure to hazardous materials, which enhanced overall public health and safety in addition to increasing the effectiveness of medical waste management. Overall, compared to current manual medical waste management systems, the convergence of IoT and machine learning has the potential to significantly increase the sustainability and efficiency of medical waste management, reducing the environmental impact of healthcare operations and enhancing public health.

关键词： IoT Machine learning NodeMCU ESP8266 Ultrasonic MQ-135 Rfid-rc522 HX711 MQTT Node-RED image processing Medical waste management

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Implementation of Adaptive Kernel Kalman Filter in Stone Soup 12

Implementation of Adaptive Kernel Kalman Filter in Stone Sou...

引用

12th Sensor Signal processing for Defence conference (SSPD)

作者： Wright, James S. Hopgood, James R. Davies, Mike E. Proudler, Ian K. Sun, Mengwei Univ Edinburgh Inst Digital Commun Edinburgh EH9 3FG Midlothian Scotland Dstl Dstl Porton Down Bldg 5Rm 101 Salisbury SP4 0JQ Wilts England Univ Strathclyde Dept Elect & Elect Engn Ctr Signal & Image Proc CeSIP Glasgow G1 1XW Lanark Scotland

ISBN: (纸本)9798350337327

The recently proposed adaptive kernel Kalman filter (AKKF) is an efficient method for highly nonlinear and high-dimensional tracking or estimation problems. Compared to other nonlinear Kalman filters (KFs), the AKKF has significantly improved performance, reducing computational complexity and avoiding resampling. It has been applied in various tracking scenarios, such as multi-sensor fusion and multi-target tracking. By using existing Stone Soup components, along with newly established kernel-based prediction and update modules, we demonstrate that the AKKF can work in the Stone Soup platform by being applied to a bearing-only tracking (BOT) problem. We hope that the AKKF will enable more applications for tracking and estimation problems, and the development of a whole class of derived algorithms in sensor fusion systems.

关键词： Adaptive kernel Kalman filter Tracking Stone Soup

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Content-based image Retrieval in Cyber-Physical System Modeling of Robots 11th

Content-based Image Retrieval in Cyber-Physical System Model...

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11th International conference on Recent Trends in Computing, ICRTC 2022

作者： Anantha Prabha, P. Subashree, B. Deva Priya, M. Department of Computer Science and Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9789811988240

Cyber-Physical System (CPS) is used in industries and automated plants as they have modern administration abilities and are capable of performing real-time processing in a distributed architecture. In mechanical plants, they are expected to perform image recognition and retrieval, and other Machine Learning (ML) processes for efficient functioning and screening of assembling units and products. The existing strategies like remote sensing retrieval and the Gaussian mixture model for image Retrieval (IR) are not suitable for huge datasets. The drawbacks of the existing strategies can be eliminated based on features of data by using Improvised Density Scale-Invariant Feature Transform (ID-SIFT). This approach mainly focuses on geometrical features like color, texture, size and shape-based image matching offering an improved accuracy of 89%. This approach is relatively efficient when compared to existing algorithms used in IR. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cyber Physical System

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Fire Incendie Detection A compartive study of Convolutional Neural Networks (CNN) and Support Vector Machines (SVM).

Fire Incendie Detection A compartive study of Convolutional ...

引用

International conference on image and Signal processing and their Applications (ISPA)

作者： Yasmine Guerbai Asma Saibi Department of Electrical Systems Engineering M‘Hammed Bougara University Boumerdès Boumerdès Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

In recent years, the application of artificial intelligence (AI) techniques for fire detection has gained significant attention due to its potential for enhancing early fire detection systems. This study aims to compare the performance of deep learning convolutional neural networks (CNN) and support vector machine (SVM) machine learning algorithms in the context of fire detection. We present a comprehensive analysis and evaluation of the two approaches, highlighting their strengths and weaknesses, and discussing their potential for real-world fire detection applications.

关键词： Support vector machines Deep learning Machine learning algorithms Neural networks Signal processing Vectors Convolutional neural networks

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Joint Optimization of Trajectory and image Transmission in Multi-UAV Semantic Communication Networks 29

Joint Optimization of Trajectory and Image Transmission in M...

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29th IEEE International conference on Parallel and Distributed systems, ICPADS 2023

作者： Yao, Xiancai Zheng, Jianchao Zheng, Xin Dai, Huadong Yang, Xiaolong Academy of Military Science of PLA Beijing100071 China Beijing100071 China

ISBN: (纸本)9798350330717

Semantic communication is considered the key promoter and basic paradigm of future 6G networks and applications. In this paper, we investigate a multi-unmanned aerial vehicle (UAV) semantic communication framework, where the trajectories and communication services are jointly optimized for image transmission of ground users. We aim to minimize the transmission delay while considering constraints such as the UAV's energy threshold, collision avoidance, and the bandwidth of the multi-UAV system. We propose a value decomposition based multi-agent deep reinforcement learning (VD-MADRL) algorithm to solve this problem, which explores the joint optimization scheme of UAV trajectory, channel allocation, and semantic information selection. Simulations demonstrate that the proposed algorithm greatly reduces system energy consumption and transmission delay compared to other traditional UAVs' trajectory planning algorithms. © 2023 IEEE.

关键词： joint optimization multi-agent Deep Reinforcement Learning Multi-UAV semantic communication

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image processing Using Elephant Herding Optimization: A Review 1

引用

2nd International conference on Intelligent Computing Techniques for Smart Energy systems, ICTSES 2021

作者： Soni, Vishnu Sharma, Abhay Rajpurohit, Jitendra Amity University Rajasthan Jaipur India University of Petroleum and Energy Studies Dehradun India

ISBN: (数字)9789811902529

ISBN: (纸本)9789811902512

Nature-inspired algorithms (NIAs) are very well defined for intuitive image processing operations. Among various nature-inspired algorithms, elephant herding optimization (EHO) is most preferably used as its application in image segmentation due to its high accuracy, efficient and optimized output, optimal to high speed, excellent performance, and ability of solve multi-objective optimization problems. This paper presents a review of various articles on EHO for image segmentation. For this purpose, recent development in the field of image segmentation and optimized results has been studied, and their outcomes have been presented. image segmentation, edge-based segmentation, thresholding, region-based segmentation, and morphological segmentation by mean of EHO are studied and presented with optimized outcomes. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Morphology

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A Real-Time Aerial imagery Collection, Mapping, and Remote Sensing Testbench for Uncrewed Missions

A Real-Time Aerial Imagery Collection, Mapping, and Remote S...

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International conference on Unmanned Aircraft systems (ICUAS)

作者： Calvin Coopmans Richard Snider Sadikul Alim Toki Andres Sewell Steve Petruzza Emma Montgomery Department of Electrical and Computer Engineering Utah State University Logan UT USA Department of Computer Science Utah State University Logan UT USA

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

As uncrewed aerial systems continue to grow in popularity and importance, the long-term and scalable use of these systems for remote sensing and imagery data collection remains a valuable and achievable goal. To enable these systems at scale, real-time onboard imagery processing is required. To determine the feasibility of real-time remote sensing systems, many factors must be accounted for, including the ability of the sensing and processing algorithms to operate on and collect data from real-world scenes and deliver actionable intelligence to the data consumer. In this paper, a holistic simulation system based on ROS 2 and Gazebo is presented, which allows for real-time processing algorithms to be tested and proven for flight in an accurate and extensible way. By using ROS 2 and USU AggieAir's STARDOS platform, it is possible to show how the remote sensing system and onboard, real-time processing algorithms are applicable to the aerial remote sensing task (i.e. it can demonstrate feasibility for physical deployment based on accurate simulated data processing).

关键词： Accuracy Codes Data collection Autonomous aerial vehicles Real-time systems Software Hardware Sensors Remote sensing image stitching

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