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

共 13664 条记录，以下是1461-1470 订阅

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In-depth Analysis of Information Overflows and its Role in Shaping Future Knowledge Structures 3

In-depth Analysis of Information Overflows and its Role in S...

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3rd IEEE International conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Paranthaman, V. Kamal, M.D. Raj Balaji, V. Sunil, G. Adhi College of Engineering and Technology Dept. of Mechanical Engineering TN Chennai India Velammal Institute of Technology Department of Mechanical Engineering TN Chennai India Loyola Institute of Technology Department of Mechanical Engineering TN Chennai India School of Computer Science and Artificial Intelligence Sr University Telangana Warangal India

ISBN: (纸本)9798350343274

This study provides a thorough evaluation of data deluges and how they will play a significant role in the development of future knowledge architectures. The suggested methodology is adaptable and interdisciplinary since it combines cutting-edge data mining, NLP, and network analysis methods. Data mining, NLP, network analysis, content analysis, big data analytics, cognitive mapping, SNA, MNA, QR, and iv are all compared to the proposed method, as are machine learning algorithms, qualitative research methods, and data visualization. Accuracy, relevance, depth of analysis, innovation, practical consequences, and clarity are only a few of the areas where the suggested technique shines. Its greater analytical skills are shown by its persistent outperformance of conventional approaches. Additionally, the suggested approach better accords with ethical norms and responsible research procedures, thus they are all considered as well. In summary, this study demonstrates the value of looking at information floods from several angles of study. The suggested technique not only provides cutting-edge analytic instruments for negotiating the intricacies of the digital information ecosystem, but also gives a forward-looking viewpoint by anticipating new patterns and developments. It demonstrates how crucial it is to use cutting-edge methods and evolve with the information age. © 2023 IEEE.

关键词： Natural language processing systems

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Comparative Study of Data Reduction Methods in Electrical Impedance Tomography For Hand Sign Recognition

Comparative Study of Data Reduction Methods in Electrical Im...

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International conference on Advanced Technologies for Signal and image processing (ATSIP)

作者： Bilel Ghoul Bilel Ben Atitallah Salwa Sahnoun Ahmed Fakhfakh Olfa Kanoun Measurement and Sensor Technology Faculty of Electrical Engineering and Information Technology Chemnitz University of Technology Chemnitz Germany Digital Research Centre of Sfax National School of Electronics and Telecommunications of Sfax Technopole of Sfax Laboratory of Signals Systems Artificial Intelligence and Networks Ons City Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Several studies have shown that electrical impedance tomography (EIT) on the human forearm can be used to classify hand signs without the need for cameras or gloves. However, the amount of measured data is in relatively high, leading to long execution times and high processing complexity. In this study, we investigate the influence of reducing the number of EIT measurements on the classification accuracy. Four different algorithms were used to halve the number of measurements, namely Principal Component Analysis (PCA), Chi-square tests, Minimum Redundancy Maximum Relevance (MRMR) and Laplacian scores. These algorithms were applied to a dataset of forearm EIT measurements corresponding to the practice of American Sign Language (ASL) in a healthy subject. The results show a slight drop in accuracy when using the quadratic support vector machine (SVM) classifier, with accuracy dropping from 95.3% to 93.4% in the best cases. A method based on a genetic algorithm (GA) was also used. This approach exploits the strengths of genetic algorithms in exploring a large solution space and converging on the near-optimal combination. This approach resulted in an increase in accuracy to 96.7%. The study demonstrated that the number of EIT measurements can be reduced without compromising overall accuracy.

关键词： Support vector machines Electrical impedance tomography Sign language Accuracy image recognition Laplace equations Assistive technologies

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A Comparative Study of VGG16, MobileNet, and Ensemble Methods on Fundus image-Based Cataract Detection

A Comparative Study of VGG16, MobileNet, and Ensemble Method...

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Advancement in Data Science, E-learning and Information systems (ICADEIS), International conference on

作者： Tabina Shafa Nabila Syahda Mochamad Nizar Palefi Ma'ady Berlian Rahmy Lidiawaty Department of Information Systems Telkom University Surabaya Surabaya Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Artificial Intelligence (AI) in ophthalmology has been growing, driven by the increasing volume of clinical data that can be utilized for algorithm development. image processing on fundus disease is crucial in providing visual information for cataract detection. Therefore, the application of technology that can leverage fundus images for cataract detection is still a few. In this regard, several previous studies have explored CNN algorithms for automatic cataract screening using fundus images. However, other research groups have proposed or developed new algorithms as alternatives. This research compares two CNN models, VGG16 and MobileNet, while also incorporating additional approaches such as Ensemble Learning and Hybrid Method. VGG16 and MobileNet were chosen because they are known for their high accuracy performance and computational efficiency. Source data obtained from Kaggle as many as 1000 datasets. The results of the study indicate that the model with the Ensemble Learning approach achieved the highest average accuracy, followed by MobileNet, MobileNet + SVM, VGG16, and VGG16 + SVM, with accuracies of 0.9377; 0.9150; 0.9143; 0.9129; and 0.9104, respectively. This research is expected to aid medical professionals in diagnosing cataracts more accurately and guide engineers in selecting the best machine-learning models for fundus image-based cataract detection systems.

关键词： Cataracts Support vector machines Visualization Accuracy Computational modeling Data models Ophthalmology Ensemble learning Medical diagnostic imaging Overfitting

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A Semantic Feature-Driven Multiscale Grid Dehazing Method

A Semantic Feature-Driven Multiscale Grid Dehazing Method

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6th International conference on Intelligent Computing and Signal processing (ICSP)

作者： Hao Zhang Hongmei Jin Zhanli Li Ning He Xi'an University of Science and Technology Xi'an China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Aiming at the problems of incomplete dehazing and detail distortion in the dehazing process of existing algorithms, an end-to-end dehazing method SemanticGridDehazeNet is proposed, which is semantic feature-driven and combines multi-scale feature fusion. The method consists of three modules: preprocessing, backbone, and post-processing. First, preprocessing is performed using a newly designed residual dense fusion block (RDFB) and convolutional layer. The Backbone module is based on the extended GridN et, which contains three processing scales, and semantic features extracted based on VGG16 Net are added in front of each scale to enhance learning. Each scale contains five RDFB blocks to fuse the feature map to the last column. Then an RDFB post-processing module improves the output quality. The main innovations include: (1) the introduction of the RDFB fusion module to focus on haze features, dynamically adjust the level of attention paid to channel or pixel locations to more effectively learn and utilize key information about the haze distribution in the input data; (2) Semantic features are integrated into the image dehazing process by incorporating semantic features extracted from VGG16 Net before each processing scale. This enhances multi-scale learning and enables more accurate focus on haze features, improving dehazing effectiveness. Experimental results demonstrate superior performance in target enhancement, robustness, and generalization. The method effectively removes residual hzae and addresses detail distortion issues.

关键词： Measurement Technological innovation image color analysis Fuses Semantics Signal processing algorithms Feature extraction Distortion Robustness Real-time systems

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Quantifying Stellar Energy Exchange: image Analysis with OpenCV and Machine Learning

Quantifying Stellar Energy Exchange: Image Analysis with Ope...

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Emerging Technology (INCET), International conference for

作者： B. Venkteswarulu Chaitanya Snehitha Kanagala Naga Venkata Sai Mohana Kakumanu Laxmi Narasimha Charan Sai Raghavarapu Himavath Lakshmi Visveswar Peravali Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Stellar energy exchange, fundamental to the dynamics of celestial systems, remains a captivating yet challenging area of study in astronomy. This paper introduces an innovative methodology for quantifying stellar energy exchange through sophisticated image analysis techniques, harnessing the power of OpenCV and machine learning. Our approach integrates OpenCV's robust image processing capabilities with state-of-the-art machine learning algorithms to automate the intricate task of analyzing astronomical images and predicting energy exchange rates within stellar constellations. The methodology begins with the preprocessing of astronomical images using OpenCV, where radiometric and atmospheric corrections are applied to ensure accurate representations of stellar luminance. Subsequently, features relevant to energy exchange dynamics are extracted from the processed images, employing advanced computer vision techniques. These features encapsulate intricate spatial, spectral, and radiometric characteristics of stars, providing rich representations for subsequent analysis. Machine learning algorithms are then employed to predict the energy exchange rates of stars based on the extracted features. Leveraging the power of convolutional neural networks and ensemble learning techniques, our model learns intricate patterns within the data and delivers accurate estimations of energy exchange dynamics. Through extensive experimentation on real astronomical datasets, our approach demonstrates exceptional performance, achieving precise predictions of energy exchange rates across diverse stellar systems.

关键词： Training image analysis Accuracy Machine learning algorithms Energy exchange Stars Predictive models

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Online Microscopic Imaging Combined with Machine Learning image processing for Online Monitoring of Mineral Froth Flotation Process

Online Microscopic Imaging Combined with Machine Learning Im...

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2024 8th International conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Xianfa Dong Xuezhong Wang College of New Materials and Chemical Engineering Beijing Institute of Petrochemical Technology Beijing China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

Froth flotation is an important process in the mineral processing industry for extracting valuable materials. This work investigates online microscopic imaging and machine learning based image analysis methods for real-time monitoring of the process. Previous limited work explored imaging the foam at the top surface layer of the froth flotation process. The new process imaging system in this work uses a corrosion-resistant online real-time imaging probe that can be put into the inside of the slurry and capture real-time images of bubbles. The acquired images are analyzed online using deep learning algorithms to automatically obtain key parameters of the bubbles, providing valuable data for froth flotation research and process control.

关键词： Manufacturing processes Microscopy Process control Real-time systems Minerals Materials processing Probes Surface treatment Slurries Monitoring

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image processing and Pattern Recognition-Based Car Classification for Intelligent Transportation System 6

Image Processing and Pattern Recognition-Based Car Classific...

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6th Smart Cities Symposium, SCS 2022

作者： Dange, Varsha Pophale, Sayali Raut, Nitin Shadrach, Finney Daniel Chowdhury, Subrata Ramya, G. Dutta, Pushan Vishwakarma Institute of Technology Pune India Dept. of Electronics and Communication Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India AP Chittoor India SITE School VIT Vellore India School of Engineering and Technology Amity University Kolkata India

ISBN: (纸本)9781839538544

The combination of car model classification and accelerated advances in computer vision appears to have the potential to significantly revolutionize automated transportation systems. Pattern recognition and image processing-based car classification methods have been proposed in recent years to improve the performance of smart highway toll collection and monitoring of traffic systems. Unfortunately, all these algorithms are trained on a inadequate number of features derived from tiny datasets, which do not considered for real-time road traffic circumstances. To address these difficulties, proposed system presents a classification model for vehicle based on convolutional neural networks to enhance the resilience of car classifier model in real-time applications. To achieve real-time robustness in classification systems, this proposed system offers a Stanford car dataset of 16,185 images classified into 157 common vehicle classes. To begin, the car dataset was trained using Inception-v3, VGG16, and ResNet50 to evaluate their accuracy based on performance. As a result, a comparative study was carried out to assess the effectiveness of the proposed system. © 2022 IET conference Proceedings. All rights reserved.

关键词： Convolutional neural networks

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Performance Comparison of K-SVD, Discriminative K-SVD, and Approximate K-SVD Dictionary Learning algorithms for Public Road Type Recognition

Performance Comparison of K-SVD, Discriminative K-SVD, and A...

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International conference on Signal processing and Communication (ICSC)

作者： Ashwin Rai Pubali De Saibal Ghosh Pritam Paral Amitava Chatterjee Business Analyst Deloitte Touche Tohmatsu India LLP Institute of Engineering and Management University of Engineering and Management Kolkata India Dept. of Electrical Engineering. Jadavpur University Kolkata India Dept. of Electrical Engineering. IIEST Shibpur Howrah India Electrical Engineering Department Jadavpur University Kolkata India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

In today's world, having the ability to drive has become of significant importance, especially while traveling. As a consequence, the importance of studying road safety is rising. Hence, the importance of research in designing intelligent systems to aid drivers in providing comfort, security, and safety is also gaining much momentum in recent times. On the other hand, designing Dictionary learning (DL) algorithms in conjunction with sparse representation/coding has become a forerunner in machine learning based research problems in the current decade. Many such DL based sparse solutions have been effectively proposed for a variety of signal processing and image processing algorithms. In some of our earlier works, we have shown how various dictionary learning based algorithms and system solutions can be successfully developed for home automation and ambient assisted leaving problems. Taking our research efforts in DL algorithms further, in this work we show how DL algorithms can be effectively used to categorize four common road types: urban roads, highways, residential areas, and rural areas. The algorithms have been developed based on sensory signals acquired by a driver wearing smart glasses, while driving in those road conditions. The DL based system developed can help providing guidance to a driver during driving. Three prominent varieties of DL algorithms, called K-singular value decomposition (K-SVD) algorithm, approximate K-SVD algorithm, and discriminative K-SVD algorithm, have been successfully implemented here for a benchmark, open dataset available for this problem and their suitable effectiveness has been investigated in detail. Although all the three varieties of K-SVD algorithms showed encouraging performances under different recognition problems, the discriminative K-SVD algorithm, employing orthogonal matching pursuit (OMP) algorithm at its core for sparse coding purposes, emerged as the best overall performer.

关键词： Machine learning algorithms Roads Signal processing algorithms Matching pursuit algorithms Machine learning Signal processing Approximation algorithms Safety Vehicles Smart glasses

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Design of Cloud Platform for Efficient Remote Sensing Big Data processing Based on Open Stack

Design of Cloud Platform for Efficient Remote Sensing Big Da...

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Integrated Intelligence and Communication systems (ICIICS), International conference on

作者： Yan Huang Weimeng Zhang Xi’an Traffic Engineering Institute Xi’an Shaanxi China Xi’an Technical School Xi’an Shaanxi China

The rapid preparation of computing resources is an important step to realize efficient remote sensing image processing. It takes a lot of time for researchers to prepare hardware resources and deploy computing framework. It often mistakes in the preparation and careless deployment of the framework which can lead to high research costs. To solve this problem, this paper uses Open Stack to virtualize hardware resources and uses Linux Shell scripting language to quickly prepare and deploy the Spark parallel computing environment. In this paper, based on the remote sensing image change detection method of connection condition, for example, the data processing algorithm in advance import platform algorithms library, component library, used to support the remote sensing image change detection of business flow of creation and recognition, resources rapid preparation, execution and presentation of the results of the algorithm and implement of massive remote sensing image change detection and efficient. It effectively improves the efficiency of remote sensing data management.

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A Comprehensive Analysis of Harmonizing Classical Approaches with the Cutting-Edge Techniques for Specular Reflection Detection in Medical Imaging

A Comprehensive Analysis of Harmonizing Classical Approaches...

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Data Science and Business systems (ICDSBS), International conference on

作者： Anu P M Lakshmi C Dept. of Computer Science Engineering SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798331585600

ISBN: (纸本)9798331585617

The detection and removal of Specular Reflections (SR) is spotted as a significant challenge in the field of Medical image Analysis, especially in Medical visual diagnosis for ensuring high quality images in accurate diagnosis. These reflections appear as bright spots in images, obscuring the critical tissue details and leads to mis-diagnosis. SR detection has attracted huge focus in recent years, as researchers aim to build impressive algorithms with improved performance and reduced complexity. By inculcating appropriate techniques, their impact can be minimized and consequently lead to better visualization of anatomical and pathological details, which are the prerequisite for medical diagnosis. The aim of this article is to project the various approaches adopted and the challenges in classical image processing approaches and deep learning-based techniques in SR detection and highlighting the importance of hybrid methods with their underlying principles, by comprehending the articles from various scholarly publishing platforms.

关键词： Deep learning Surveys Representation learning Visualization image analysis Accuracy Self-supervised learning Transformers Reflection Medical diagnostic imaging

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