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

共 16443 条记录，以下是1161-1170 订阅

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Research on Deep Learning-Based SAR image Denoising Algorithm 10

Research on Deep Learning-Based SAR Image Denoising Algorith...

引用

10th International conference on Dependable systems and Their Applications, DSA 2023

作者： Hu, Shengnan Mao, Ruihan Li, Hua Wuhan University of Technology Hubei Wuhan China

ISBN: (纸本)9798350304770

Synthetic Aperture Radar (SAR) has the advantages of high penetration, high resolution, all-weather, and all-time, but due to the special imaging mechanism of SAR images, there are a large number of coherent speckle noises in the images. If not processed, it will greatly limit the subsequent use of data. In order to improve the use of SAR images, noise removal processing is needed. In order to choose the appropriate noise removal algorithm, this paper compares the performance of three noise removal algorithms: multiplication model, enhanced Lee-sigma filter, and CNN trained on SAR images (SAR-CNN). The results show that the multiplication model has better noise removal ability and ability to extract effective information. © 2023 IEEE.

关键词： Synthetic aperture radar

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An analysis comparing CT and X-ray images for COVID-19 identification employing deep learning and image processing methods 3

An analysis comparing CT and X-ray images for COVID-19 ident...

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3rd International conference on Smart Technologies and systems for Next Generation Computing, ICSTSN 2024

作者： Prabu, R. Dhinakar, P. Bharath Institute of Higher Education and Research Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350391565

This study presents a comparative analysis of deep learning approaches for the identification of COVID-19 pneumonia in medical imaging, specifically focusing on CT and X-ray modalities. To aid healthcare professionals in quickly identifying infected individuals during the COVID-19 pandemic, accurate and efficient diagnostic tools are urgently required. In order to identify COVID-19 more quickly and accurately, deep learning algorithms combined with medical imaging data produced results. Computed Tomography (CT) and X-ray imaging are commonly used modalities for assessing lung involvement in COVID-19 patients, each offering unique advantages and challenges. Leveraging deep learning techniques, such as convolutional neural networks (CNNs), this analyzes the performance of models trained on large datasets of labeled CT and X-ray images. Our analysis includes preprocessing methods, model architectures, training procedures, and evaluation metrics to assess the efficacy of these approaches. Furthermore, this discusses the clinical implications, challenges, and ethical considerations associated with integrating deep learning-based diagnostic tools into routine clinical practice. © 2024 IEEE.

关键词： COVID-19

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

International Conference on Internet of Everything and Quant...

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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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On-Line System of Garbage image-Orientated Intelligent Classification, Submission and Examination

On-Line System of Garbage Image-Orientated Intelligent Class...

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IEEE International conference on e-Business Engineering (ICEBE)

作者： Jiayin Tian Yaozhi Wang Jiaxin Liu Yan Chen School of Computer Science and Technology Xi'an Jiaotong University Xi'an Shaanxi China Shaanxi Key Lab of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an Shaanxi China Shaanxi Key Lab of Big Data Knowledge Engineering School of Computer Science and Technology Xi'an Jiaotong University Xi'an Jiaotong University Xi'an Shaanxi China

ISBN: (数字)9798350365856

ISBN: (纸本)9798350365863

In a world brimming with new products continually, novel waste types are ubiquitous. This makes current image-based garbage classification systems difficult to perform well due to the long-tailed effects of distribution of garbage types, and necessitates an urgent and efficient garbage classification with abilities of detecting new and rare wastes and class-incremental learning for environmental sustainability. Therefore, we propose a framework of Online System of Garbage image-Oriented Intelligent Classification, Submission, and Examination, facilitating the incremental garbage classification efforts. In which, to identify novel garbage effectively, we also introduced few-shot object detection method with two key algorithms: Two-Stage Object Detection Learning Algorithm and Dynamic Query-based Incremental Few-shot Learning Algorithm. Our experiment results show that Both outperform the current existing ones in dataset, MS COCO. Then, a strategy of Class-Incremental learning based Residual Network is proposed to meet the need of new waste class-incremental learning. The experimental results support our strategy. Finally, a prototype system employed the above algorithms and the strategy is described.

关键词： image recognition Heavily-tailed distribution Heuristic algorithms Green products Prototypes Object detection Classification algorithms Object recognition Few shot learning Residual neural networks

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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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Indian Currency Recognition System Using image processing Techniques 9

Indian Currency Recognition System Using Image Processing Te...

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9th International conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Garkoti, Pravidhi Mishra, Pranshu Rakesh, Nitin Payal Kaur, Mandeep Nand, Parma Sharda University Department of Computer Science Set India

ISBN: (纸本)9789380544441

In today's modern world, an intelligent system is the main requirement for current automation systems like money exchange machines, electronic banking, etc. Such automated currency recognition systems helps in reducing the human efforts to recognize the monetary value of the currency. The currency notes are sometimes blurry or damaged that makes the recognition task difficult. The correct algorithms and features extraction and selection methods are very important to select in the solution. The practical implementation of these systems should be less complex, simple with good efficiency and speed. In this paper, an automated currency system is proposed using image processing techniques. image processing techniques are used to improve the quality of currency image so that correct information can be extracted. Basically currency is scanned for obtaining image, pre-processing operations are performed which include blurring, noise removal etc. Then the boundary detection and cropping is completed so the feature extraction is the last process after which the result is displayed. By these phases the currency recognition is processed and helps us to recognize currency easily. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Feature extraction

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

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

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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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