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

共 15613 条记录，以下是751-760 订阅

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Optimizing Aircraft Trajectory Prediction: A Hybrid TCN-LSSVM Model with image Recognition 43

Optimizing Aircraft Trajectory Prediction: A Hybrid TCN-LSSV...

引用

43rd Chinese Control conference, CCC 2024

作者： Sun, Haosen Ren, Yige Wang, Ruiping Gong, Zheng School of Aeronautic Science and Engineering Beihang University Beijing100191 China University of Science and Technology Beijing Beijing100083 China Nanyang Technological University School of Computer Science and Engineering Singapore637616 Singapore Beihang University School of Astronautics Beijing100191 China

ISBN: (纸本)9789887581581

This paper proposes a novel TCN-LSSVM model for precise trajectory prediction. An approach that integrates the image recognition and the trajectory prediction is designed, which improves the prediction process. By combining event and depth cameras, multi-dimensional images are transformed into single-dimensional data. This hybrid approach to information access can enhance the model's learning efficiency and reinforce ability to discern intricate patterns in movement data. The processed single-dimensional data is put into a trajectory prediction model for processing. The trajectory prediction phase includes causal convolution and regularization to optimize prediction accuracy, reduce errors and improve real-time performance, taking full advantage of the combined benefits of TCN and LSSVM to improve performance. Comprehensive comparisons are made with existing prediction algorithms, and the results demonstrate the superior accuracy of the designed TCN-LSSVM model, which reduces error margins and underscores the efficacy in leveraging image recognition for trajectory prediction. This innovative model is a significant contribution to the field and provides a powerful framework for future applications in relevant scenarios. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Prediction models

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An Improved Grey Wolf Optimization Algorithm for Heart Disease Prediction 4th

An Improved Grey Wolf Optimization Algorithm for Heart Disea...

引用

4th International conference on Advanced Intelligent Technologies, ICAIT 2023

作者： Niu, Sihan Zhou, Yifan Li, Zhikai Huang, Shuyao Zhou, Yujun Department of Computer Science Johns Hopkins University BaltimoreMD United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States School of Computer Engineering and Science Shanghai University Shanghai China School of Information Sciences University of Illinois Urbana-Champaign ChampaignIL United States

ISBN: (纸本)9789819732098

This paper presents a unique solution to challenges in medical image processing by incorporating an adaptive curve grey wolf optimization (ACGWO) algorithm into neural network backpropagation. Neural networks show potential in medical imaging but suffer from issues like overfitting and lack of interpretability due to imbalanced and scarce data. Traditional grey wolf optimization (GWO) also has its drawbacks, such as a lack of population diversity and premature convergence. This paper addresses these problems by introducing an adaptive algorithm, enhancing the standard GWO with a sigmoid function. This algorithm was extensively compared to four leading algorithms using six well-known test functions, outperforming them effectively. Moreover, by utilizing the ACGWO, we increase the robustness and generalization of the neural network, resulting in more interpretable predictions. Applied to the publicly accessible Cleveland Heart Disease dataset, our technique surpasses ten other methods, achieving 86.8% accuracy, indicating its potential for efficient heart disease prediction in the clinical setting. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Medical image processing

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A COMPARISON OF DIFFERENT ATMOSPHERIC TURBULENCE SIMULATION METHODS FOR image RESTORATION 29

A COMPARISON OF DIFFERENT ATMOSPHERIC TURBULENCE SIMULATION ...

引用

IEEE International conference on image processing (ICIP)

作者： Nair, Nithin Gopalakrishnan Mei, Kangfu Patel, vishal M. Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Atmospheric turbulence deteriorates the quality of images captured by long-range imaging systems by introducing blur and geometric distortions to the captured scene. This leads to a drastic drop in performance when computer vision algorithms like object/face recognition and detection are performed on these images. In recent years, various deep learning-based atmospheric turbulence mitigation methods have been proposed in the literature. These methods are often trained using synthetically generated images and tested on real-world images. Hence, the performance of these restoration methods depends on the type of simulation used for training the network. In this paper, we systematically evaluate the effectiveness of various turbulence simulation methods on image restoration. In particular, we evaluate the performance of two state-or-the-art restoration networks using six simulations method on a real-world LRFID dataset consisting of face images degraded by turbulence. This paper will provide guidance to the researchers and practitioners working in this field to choose the suitable data generation models for training deep models for turbulence mitigation. The implementation codes for the simulation methods, source codes for the networks and the pre-trained models are available at https://***/Nithin-GK/Turbulence-Simulations

关键词： Atmospheric turbulence mitigation deep learning face verification deblurring

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image-based System for Blood Group Classification

Image-based System for Blood Group Classification

引用

International conference on ent Computing and Control systems (ICICCS)

作者： K. Ramakrishna Reddy G. Indrani N. Pavan Kumar K. Vamshi Krishna Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

It provides a classic method for blood image based system for blood group classification techniques. The suggested approach reduces reliance on traditional processes by automating the analysis of blood sample images through the use of sophisticated image processing algorithms. This strategy seeks to offer a dependable and expandable solution that is especially useful for application in distant or resource-constrained environments.

关键词： image segmentation Histograms Control systems Classification algorithms Blood

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Performance Comparison of AI Models for Digital image captioning

Performance Comparison of AI Models for Digital Image captio...

引用

2022 International conference on Computational Modelling, Simulation and Optimization, ICCMSO 2022

作者： Kaur, Rajwinder Singh, Gurpreet Chitkara University Institute of Engineering and Technology Punjab Rajpura India

ISBN: (纸本)9798350332889

Digital image captioning field deals with the concept of interpreting a given image by the application of different Artificial Intelligence (AI) based algorithms. These kind of systems always considered the input in the form of image and replying with an output representing some text or speech samples. This output explains the content present in the image. These kind of automated systems proven good in the areas like medical image processing, online review systems, speech conversion for visually impaired persons etc. This paper represents a comparative study among four different AI models: Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM) and kNN (k-Nearest Neighbor) in context of image captioning activity. Random scenes in the form of input images has been consider to show the part of diverse nature of these algorithms. It has been observed that SVM classifier outshine as compared to rest of the models with the overall accuracy of 98%. © 2022 IEEE.

关键词： Support vector machines

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Liver Disease Prediction using various image processing Techniques and Machine Learning algorithms 1

Liver Disease Prediction using various Image Processing Tech...

引用

1st International conference on Software, systems and Information Technology, SSITCON 2024

作者： Saravanan, M.S. Prasad, Siva Rama Krishna Dhanalakshmi, S. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Guru Nanak Institutions Technical Campus Department of It Ibrahimpatnam India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Bioinformatics Chennai India

ISBN: (纸本)9798350352931

Predicting liver disease usually entails estimating a person's chance of contracting ailments related to the liver using a variety of techniques. The prediction is made using the Novel Decision Tree algorithm and the Naive Bayes algorithm. From CHAOS Grand challenge webpage, data collected for liver images. Decision tree classifiers are compared to Naive Bayes classifiers for their accuracy and precision in diagnosing liver disease in patients. When it comes to predicting liver illness, the Naive Bayes classifier is 75.15 %accurate, whereas the Novel Decision tree classifier is 80.03 %accurate. 0.022 is the most significant number. g-power is used to calculate the sample size for two group at 16. The 95 percent alpha value for the g power is 0.05. The prediction of liver disease at early stage achieved by comparing Novel Decision Tree to Naive Bayes, Novel Decision Tree outperforms the latter in terms of precision and accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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Development of SFCW Based GPR for Sub-Surface Scanning 6

Development of SFCW Based GPR for Sub-Surface Scanning

引用

6th IEEE Pune Section International conference, PuneCon 2023

作者： Ingole, Ashlesha De, Sampurna Bazil Raj, A.A. Radar System Laboratory Department of Electronics Engineering India Defence Institute of Advanced Technology Pune411025 India Rf Photonics Department of Electronics Engineering India

ISBN: (纸本)9798350324204

Ground Penetrating Radar (GPR) is a non destructive geophysical technique that uses electromagnetic waves to image the subsurface. GPR has a wide range of applications including archaeological survey, landmine and tunnel detection, glacier analysis, and many more. However, GPR systems are limited by their resolution and sensitivity. Stepped Frequency Continuous Wave (SFCW) radar is a promising technique for subsurface scanning due to its high resolution and sensitivity. SFCW radar systems are complex and require significant processing to extract important and meaningful information from received signals. This paper proposes a novel SFCW radar system based on GNU Radio and MATLAB for subsurface scanning with intelligent classification algorithms. The proposed system uses a stepped frequency waveform to achieve high resolution and employs intelligent classification algorithms to identify and classify the subsurface targets. The system is implemented using GNU Radio and MATLAB, and its performance is evaluated using simulated and real-world data. The simulated data are generated using a subsurface scattering model, while the real world data are collected from a subsurface test bed. The results demonstrate that the proposed system can effectively identify and accurately classify subsurface targets. © 2023 IEEE.

关键词： Ground penetrating radar systems

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International conference on Internet of Everything and Quantum Information processing, IEQIP 2023

International Conference on Internet of Everything and Quant...

引用

International conference on Internet of Everything and Quantum Information processing, IEQIP 2023

ISBN: (纸本)9783031619281

The proceedings contain 31 papers. The special focus in this conference is on Internet of Everything and Quantum Information processing. The topics include: Revolutionizing Agriculture: A Mobile App for Rapid Plant Disease Prediction and Sustainable Food Security;EMG Based Human Machine Integration for IoT Based Instruments;medrack: Bridging Trust and Technology for Safer Drug Supply Chain Using Ethereum and IoT;a Review on Tuberculosis Pattern Detection Based on Various Machine Learning Techniques;sensor Based Hand Gesture Identification for Human Machine Interface;an Improved Detection System Using Genetic Algorithm and Decision Tree;a Detailed Analysis of Colorectal Polyp Segmentation with U-Network;a Review on Internet of Things (IoT): Parkinson’s Disease Monitoring Device;Machine Learning-Based Prediction of Temperature Rise in Squirrel Cage Induction Motor (SCIM);quantum Many-Body Problems: Quantum Machine Learning Applications;Experimental Study on the Impact of Airborne Dust Deposition on PV Modules Using Internet of Things;bidirectional Converter with Time Utilization-Based Tariff Investigation and IoT Monitoring of Charging Parameters Based on G2V and V2G Operations;predictive Analysis of Telecom Customer Churn Using Machine Learning Techniques;baker’s Map Based Chaotic image Encryption in Military Surveillance systems;Cyber Security Investigation of GPS-Spoofing Attack in Military UAV Networks;ioT Based Enhanced Safety Monitoring System for Underground Coal Mines Using LoRa Technology;ioT Based Hydroponic System for Sustainable Organic Farming;predicting Stride Length from Acceleration Signals Using Lightweight Machine Learning algorithms;unveiling Hate: Multimodal Perspectives and Knowledge Graphs;vision-Based Toddler Activity Recognition: Challenges and Applications;automated W-Sitting Posture Detection in Toddlers.

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Prediction of Colon Cancer using DenseNet121, CNN, and REsNET50 Machine Learning Models and using image processing Techniques

Prediction of Colon Cancer using DenseNet121, CNN, and REsNE...

引用

2023 International conference on Artificial Intelligence Robotics, Signal and image processing, AIRoSIP 2023

作者： Hasan, Mahadi Islam, Jahirul Ahmed, Minhaz Hasan, Md Maruf The University of Tennessee at Chattanooga Ms in Data Analytics ChattanoogaTN United States New Mexico Institute of Mining and Technology Ms in Cse SocorroNM United States Ms in Cse *** United States The University of South Dakota Department of Comuter Science VermillionSD United States

ISBN: (纸本)9798350327472

Colon cancer is a matter of great importance in the field of global health, as it stands as one of the primary contributors to mortality rates associated with cancer. The timely identification and precise prognostication of colon cancer have the potential to significantly enhance patient outcomes and diminish mortality rates. The global public health systems face a significant challenge in addressing the impact of colon cancer, which underscores the need for efficient screening and prediction techniques. Machine learning techniques present potential solutions by utilizing extensive datasets to detect intricate patterns that may be imperceptible to human observers. This study investigates the viability of employing machine learning techniques to predict colon cancer by utilizing a dataset consisting of 10,000 colonoscopy images that have been classified into two categories: cancerous and non-cancerous cases. The main aim of this study is to assess the efficacy of various machine learning algorithms, namely DenseNet-121, CNN, and ResNet50, in the prediction of colon cancer. The models' performance was comprehensively assessed in terms of their accuracy in predicting colon cancer. Among the models that were evaluated, DenseNet-121 demonstrated exceptional performance, achieving the highest accuracy of 99.6%. © 2023 IEEE.

关键词： Lung cancer

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Determining image Deblur, Detecting Faces, and Enhancing Appearance Using image processing Techniques

Determining Image Deblur, Detecting Faces, and Enhancing App...

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IEEE International conference on Automatic Control and Intelligent systems (I2CACIS)

作者： Norazliani Md Sapari Khairul Huda Yusof Nicholas Tan Kien Lung Madihah Md Rasid Siti Maherah Hussin Centre of Electrical Energy Systems (CEES) Faculty of Electrical Engineering UTM Skudai Johor Faculty of Information Science & Engineering Management & Science University Selangor Malaysia

ISBN: (数字)9798350372106

ISBN: (纸本)9798350372113

This paper explores the utilization of MATLAB for digital signal processing (DSP) techniques in image processing tasks, focusing on image deblurring, face detection, and facial feature enhancement. Blind deconvolution methods are employed to address image blurriness, while face detection is facilitated using cascaded object detectors. Enhancements to detected facial features involve histogram equalization, smoothing filters, skin tone adjustment, and contrast enhancement techniques, followed by seamless integration using resizing methods. MATLAB serves as a robust platform for implementing and analyzing DSP algorithms, providing insights into practical solutions for common challenges in digital image processing.

关键词： visualization Histograms Digital images Signal processing algorithms Digital signal processing Skin image restoration

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