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

共 12978 条记录，以下是621-630 订阅

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Research on Automatic Screening Algorithm of Electric Power image Recognition Data Based on Artificial Intelligence 2

Research on Automatic Screening Algorithm of Electric Power ...

引用

2nd IEEE International conference on Power, Electronics and Computer Applications, ICPECA 2022

作者： Cao, Gang Liu, Yaohong Chen, Liang Xu, Tong Yu, Ting Yang, Sixu Zhang, Yu Hu, Yi Meishan Power Supply Company of State Grid Sichuan Electric Power Company Sichuan Meishan620000 China

ISBN: (纸本)9781665442763

In this paper, artificial intelligence digital image processing technology is used to process power images to form an automatic power image screening system. This method can replace the traditional artificial power image recognition method. This paper studies the specific methods of power image processing and the feasibility of applying them to power image recognition systems. It studies various algorithms for image recognition data automatic screening, feature extraction and pattern recognition, and studies feature extraction for power images. As well as a variety of algorithms for model recognition, we have studied the monitoring and recognition algorithms for power images based on SIFT and RANSAC methods. The selected template image and the key points of the test image are roughly matched. RANSAC removes the wrong matching points. Affine transformation, positioning the set template image in the test image. © 2022 IEEE.

关键词： image recognition

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Leveraging Content Based image Retrieval Using Data Mining for Efficient image Exploration 1

引用

4th International conference on Mobile Radio Communications and 5G Networks, MRCN 2023

作者： Kaur, Jaspreet Gupta, Divya Singh, Amrinder Shah, Syed Hassan Ahmed Department of Computer Science and Engineering Chandigarh University Mohali 140413 India University Institute of Teachers Training and Research ChandigarhUniversity Punjab Mohali India California State University FullertonCA United States

ISBN: (数字)9789819707003

ISBN: (纸本)9789819706990

A content-based image Retrieval (CBIR) has become an essential tool for managing and searching large-scale images. However, the accuracy and performance of CBIR systems can be improved by combining data mining techniques. Content-based retrieval (CBR) uses the properties and characteristics of the content itself to search for and retrieve information from a big database instead of depending on text or metadata. CBR is very helpful in research, where it is necessary to swiftly and effectively examine vast amounts of data. According to the results, data mining techniques can considerably increase the retrieval process accuracy and effectiveness. Similar photos can be grouped together using clustering algorithms, common patterns of visual features can be found using association rule mining, and images can be classified using classification techniques. In order to create a system for content-based image retrieval and processing, we studied the retrieval of images from huge databases using a variety of feature extraction and matching techniques. The demand for CBIR development came as a result of the sharp rise in image database volumes and their widespread use in several applications. The description of basic feature extraction methods including texture, color, and form is provided in this study. Once these features are retrieved and then used for comparing photos based on similarity. This study suggests a cutting-edge system design for CBIR system that integrates content-based picture and color analysis with data mining methods. This work is intended to develop a segmentation module for the CBIR system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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Deep Learning based Diabetic Retinopathy Detection using image processing 1

Deep Learning based Diabetic Retinopathy Detection using Ima...

引用

1st International conference on Cognitive Computing and Engineering Education, ICCCEE 2023

作者： Rathor, Sandeep Tiwari, Ashish GLA University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350332803

The global prevalence of visual impairment and blindness due to diabetic retinopathy can be significantly reduced through improved diabetes management. Diabetic retinopathy (DR) screening is crucial as it can prevent vision loss. In recent years, personalized screening intervals considering various risk factors have been proposed, showing promising cost-effectiveness ratios. However, limited funding hampers the implementation of widespread screening programs in many countries. A groundbreaking advancement in screening methods is the application of Deep Learning, which revolutionizes the detection and classification of diabetic retinopathy while reducing costs. By automating the analysis of retinal images, Deep Learning techniques enable efficient and accurate identification of DR, enabling timely intervention and treatment. Moreover, emerging research suggests that retinal imaging can not only detect existing diabetic retinopathy but also identify individuals at risk, expanding the scope of screening beyond the detection of sight-Threatening diseases. This paves the way for proactive measures to prevent or mitigate the progression of DR. Therefore, this research aims to develop a Deep Learning-based system for diabetic retinopathy detection using image processing techniques. By leveraging the power of Deep Learning algorithms and advanced image analysis, this study seeks to improve the accuracy, efficiency, and accessibility of DR screening, ultimately enhancing patient outcomes and reducing the burden on healthcare systems. © 2023 IEEE.

关键词： image analysis

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Application of Various Novel Computer image processing (CIP) algorithms in Digital Media Art images

Application of Various Novel Computer Image Processing (CIP)...

引用

Multi-Agent systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Xian Du Shanghai Institute of Visual Arts Shanghai China

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the continuous development of digital image processing algorithms, its application scenarios have been integrated from the simple research of a single image and a single algorithm to a multi-algorithm fusion analysis paradigm. Therefore, this study proposes a digital media art image analysis framework that combines multiple computer image processing (CIP) algorithms. This breakthrough covers three core architectures: image edge extraction, image style transfer, and image compression. In the image edge extraction module, this study designed an improved edge detection algorithm based on de-noising auto-encoder. This algorithm improves the accuracy of edge detection through multi-directional feature extraction and (I, O)-fuzzy rough set optimization, while maintaining global stability. In the image style transfer module, this study proposed a dual-module network. The network includes a texture translation network and a perceptual loss network, and achieves cross-domain style transfer through a fusion model. At the same time, the algorithm optimizes the perceptual loss function to enhance semantic matching capabilities. In the image compression module, this study uses chaotic systems to construct an optimized measurement matrix. Specifically, through a distributed data parallel training framework, a compression method based on the LPAC gradient sparse algorithm is constructed. By testing the three modules separately, the experimental results confirm the effectiveness of the proposed algorithm.

关键词： Training image coding Art image analysis Translation image edge detection Digital images Semantics Media Feature extraction

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Soccer Player Tracking Using UAV imagery: A Comparative Study of Yolo and Traditional image processing algorithms

Soccer Player Tracking Using UAV Imagery: A Comparative Stud...

引用

International conference on Unmanned Aircraft systems (ICUAS)

作者： Felipe dos Anjos Rezende Thayron Miranda Hudson Pedro Augusto Fialho Silva Werikson Frederiko de Oliveira Alves André Luis Carvalho Mendes Alexandre Santos Brandão Nucleus of Specialization in Robotics Department of Electrical Engineering Universidade Federal de Viçosa Viçosa MG Brazil

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

Player tracking is a useful tool for tactical analysis and performance evaluation in soccer, providing valuable insights into player movements and team dynamics. This project investigates the feasibility of tracking players using UAVcaptured imagery, employing both YOLO and traditional image processing algorithms (TIPA). Initial validation focuses on robot soccer players due to their predictable and controllable movements. The comparative analysis considers processing time, computational cost, adaptability to environmental changes, sensitivity to lighting variations, ability to handle dynamic conditions, tracking accuracy, and real-time performance. Results indicate that, under equivalent hardware and preparation time conditions, YOLO achieves performance comparable to traditional techniques. Nonetheless, the selection of the most suitable approach should be guided by task-specific demands, available computational resources, and the time allocated for system development and deployment.

关键词： YOLO Performance evaluation Sensitivity Tracking image processing Lighting Robot sensing systems Prediction algorithms Real-time systems Sports

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YOLOv8 image processing for Evaluation of Stability algorithms Based on Neural Networks: A Sports Use Case 8th

YOLOv8 Image Processing for Evaluation of Stability Algorith...

引用

8th International conference on Inventive Communication and Computational Technologies, ICICCT 2024

作者： Rahman, Md. Habibur Mohiul Islam, A.S.M. Hasan, Abdullah Ibnah Uddin, Mahtab Ahmed, Ashek Miazee, Asif Ahammad Hossain, Yamin Department of Mathematics University of Chittagong Chittagong Bangladesh Dhaka College Dhaka Bangladesh Institute of Natural Sciences United International University Dhaka Bangladesh Department of Computer Science Maharishi International University FairfieldIA United States Department of Computer Science and Engineering VIT-AP University Amaravati India

ISBN: (纸本)9789819777099

Sports image classification is a complex problem with many different sports involved. It has subpar detection performance and challenges with feature recognition. The issue of classifying 110 different sports image categories is tackled by this study using four pre-trained models: Residual Network-50, EfficientNet-B7, Densely Connected Convolutional Network-121 (DenseNet-121), and You Only Look Once version 8 (YOLOv8). This dataset, which includes 12,500 sports images, provides a solid test base for this research. Comparing their results, ResNet-50 shows excellent performance on the training set, with an accuracy of 94.90 and 91.75% on the validation set. The EfficientNet-B7 model has an estimated accuracy of 65.48% in inference and 41.49% in training. Its underperformance may be due to its extraordinary performance and inability to represent specific gaming images accurately during the image segmentation task. DenseNet-121 performs 75.80% accuracy on the training set and 88.24% on the validation set. It does better than EfficientNet-B7 for capturing features from reflection images. The Geolovin-8 model performed well and averaged 94.90% accuracy on the training set and 97.87% on the validation set in most cases. These results demonstrate that Geolovin-8 has good image recognition and classification performance in the game. This article compares the performance of complex image classification algorithms concerning the same confidential image classification capabilities so that the required information is available. Knowing the advantages and disadvantages of these qualitative features may be necessary to understand the challenges of latent image classification and may suggest future research efforts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image classification

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Cotton Plant Disease Detection and Prevention Using Deep Learning and image processing 11

Cotton Plant Disease Detection and Prevention Using Deep Lea...

引用

11th International conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Kesani, Ruthvija Sunkavalli, Jaya Prakash Shaik, Mahamamad Rafi Yadala, Kusuma Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Information Technology Vijayawada India

ISBN: (纸本)9798350350357

One of the most important aspects of the agricultural economy is the production of cotton, which is threatened by diseases that lower crop quality and yield. Conventional techniques for diagnosing diseases are frequently subjective and labor-intensive. This paper presents a novel method for the automatic detection and prevention of cotton plant diseases that makes use of deep learning techniques. A convolutional neural network (CNN) model is trained using a dataset that includes various photos of both healthy and sick cotton plants. The suggested model offers a dependable and time-efficient solution by exhibiting high accuracy in differentiating between different diseases. Moreover, proactive disease detection is made possible by the integration of real-time monitoring systems, such as drones fitted with high-resolution cameras. Early detection lessens the need for broadspectrum antibiotics by enabling the prompt application of preventive measures, such as targeted therapies. Finally, we conduct a comprehensive computational analysis of eight cutting-edge object detection algorithms on the cotton plant dataset to identify diseases on the leaves and seven cutting-edge classification algorithms on the cotton plant datasets to determine if a leaf has a disease or not. computed results indicate that it has a high degree of object detection accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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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...

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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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