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

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Pneumonia Classification using deep learning: a comparative study

Pneumonia Classification using deep learning: a comparative ...

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

作者： Mohamed Lakhdar Tiar Nadjiba Terki Juan Jose Dominguez-Jimenez IL3CUB Laboratory University of Mohamed Khider Biskra Algeria Electrical Engineering Department University of Mohamed Khider Biskra Algeria Dept. Computer Languages and Systems University of Cadiz Escuela Superior de Ingenieria Cadiz Spain

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Pneumonia is an infectious disease of the lungs, caused by viruses, bacteria or fungi. Pneumonia is distinguished by acute inflammation of the lung tissue, causing the consolidation of the terminal bronchioles and alveoli. According to the WHO (the World Health Organization), this disease causes about 4 million deaths. Among the methods of diagnosing pneumonia uses a chest X-ray. This is widely used to visualize pulmonary abnormalities. This work aims at the detection and characterization of pneumonia by the development of computer-assisted diagnosis systems (DAOC). We use deep learning algorithms and chest X-ray images from a pneumonia database to examine the classification of pneumonia. To improve the accuracy, we compare several deep learning models. This research contributes to meeting the growing demand for medical personnel by addressing the global incidence of chest disorders.

关键词： Deep learning Visualization Pneumonia Lung Signal processing algorithms Medical services Signal processing

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Comparative analysis of three advanced deep learning algorithms for Multiple Sclerosis lesion segmentation in FLAIR MRI

Comparative analysis of three advanced deep learning algorit...

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International conference on Signal processing Proceedings (ICSP)

作者： Yi Zhu Thomas Grenier Chantal Revol-Muller CREATIS University of Lyon Lyon France

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

This paper addresses the pressing need for enhanced tools in the diagnosis and management of Multiple Sclerosis (MS), particularly in the accurate detection and segmentation of MS lesions. Leveraging recent advances in deep learning, we evaluate the performance of three state-of-the-art algorithms, focusing on their potential to improve both precision and efficiency in MS lesion segmentation from medical images. Our study provides critical insights into the strengths and limitations of each model, offering valuable guidance for future applications of AI in MS diagnosis and treatment.

关键词： Deep learning image segmentation Accuracy Multiple sclerosis Computational modeling Signal processing algorithms Signal processing Real-time systems Lesions Reliability

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Gaussian Blur through Parallel Computing

Gaussian Blur through Parallel Computing

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International conference on image processing and Vision Engineering (IMPROVE)

作者： Ibrahim, Nahla M. Abou ElFarag, Ahmed Kadry, Rania Arab Acad Sci & Technol & Maritime Transport Dept Comp Engn Alexandria Egypt

ISBN: (纸本)9789897585111

Two dimensional 2D convolution is one of the most complex calculations and memory intensive algorithms used in image processing. In our paper, we present the 2D convolution algorithm used in the Gaussian blur which is a filter widely used for noise reduction and has high computational requirements. Since, single threaded solutions cannot keep up with the performance and speed needed for image processing techniques. Therefore, parallelizing the image convolution on parallel systems enhances the performance and reduces the processing time. This paper aims to give an overview on the performance enhancement of the parallel systems on image convolution using Gaussian blur algorithm. We compare the speed up of the algorithm on two parallel systems: multi-core central processing unit CPU and graphics processing unit GPU using Google Colaboratory or "colab".

关键词： CUDA Parallel Computing image Convolution Gaussian Blur Google Colaboratory

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A Novel Artistic image Compression Algorithm Based on Multi-Scale Adaptive Discrete Wavelet Transform

A Novel Artistic Image Compression Algorithm Based on Multi-...

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

作者： Yijun Li China Academy of Art Yuhang District Hangzhou Zhejiang China

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

How to compress information is a challenge in the digital age, and the image compression has a wide range of application scenarios. In order to solve the challenges of low compression efficiency and high loss rate of the traditional algorithms, this study proposes a compression algorithm based on multi-scale adaptive discrete wavelet transform. The core of the proposed algorithm is to make reasonable use of the multiresolution analysis characteristics of discrete wavelet transform. Based on the feature extraction, the image information is decomposed into high-frequency and low-frequency parts. This operation can stably and effectively implement frequency domain threshold processing and reduce redundant data. The proposed algorithm optimizes the encoding efficiency of image features to the greatest extent by introducing block difference features and designing a new iterative function system. In view of the high redundancy and high complexity of the artistic images, the traditional wavelet analysis is improved and optimized, and a multi-scale adaptive model is introduced. In the experiment, the test used multiple sets of artistic image test sets and compared them with mainstream algorithms (JPEG, VAE, etc.). In PSNR analysis, the proposed algorithm achieves better performance.

关键词： image coding Wavelet transforms Transforms Discrete wavelet transforms Feature extraction Wavelet analysis Wavelet domain Compression algorithms Frequency-domain analysis Collaborative intelligence

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International conference on Emerging Trends and Applications in Artificial Intelligence, ICETAI 2023

International Conference on Emerging Trends and Applications...

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International conference on Emerging Trends and Applications in Artificial Intelligence, ICETAI 2023

ISBN: (纸本)9783031567278

The proceedings contain 47 papers. The special focus in this conference is on Emerging Trends and Applications in Artificial Intelligence. The topics include: Artificial Neural Network Model of Nonlinear Behavior of Micro-ring Gyroscopes;a Framework for Knowledge Representation Integrated with Dynamic Network Analysis;time Series Forecasting Using Parallel Randomized Fuzzy Cognitive Maps and Reservoir Computing;review of Offensive Language Detection on Social Media: Current Trends and Opportunities;Text Mining and Sentimental Analysis to Distinguish systems Thinkers at Various Levels: A Case Study of COVID-19;ADHD Prediction in Children Through Machine Learning algorithms;commonsense Validation and Explanation for Arabic Sentences;predicting Students Answers Using Data Science: An Experimental Study with Machine Learning;arabic News Articles Classification Using Different Word Embeddings;tree Fruit Load Calculation with image processing Techniques;prediction and Analysis of Water Quality Using Machine Learning Techniques;comparative Analysis of Feature Selection Techniques with Metaheuristic Grasshopper Optimization Algorithm;supermarket Shopping with the Help of Deep Learning;A Decision Support System for Detecting FIP Disease in Cats Based on Machine Learning Methods;a Numerical Simulation for the Ankle Foot Orthosis Using the Finite Element Technique with the Aid of an Experimental Program;numerical and Experimental Simulations of Damage Identification in Carbon/Kevlar Hybrid Fiber-Reinforced Polymer Plates Using the Free Vibration Measurements;computer Modelling of the Gait Cycle Patterns for a Drop Foot Patient for the Composite a Polypropylene Ankle-Foot Orthoses;arabic Sign Language Alphabet Classification via Transfer Learning;evaluation of Chemical Data by Clustering Techniques;novel Quantum Key Distribution Method Based on Blockchain Technology;smart Parking System Based on Dynamic and Optimal Resource Allocation;spatio-Angular Resolution Trade-Off in Face

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ADHHI airborne hyperspectral imager: camera structure and geometric correction 28

ADHHI airborne hyperspectral imager: camera structure and ge...

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conference on image and Signal processing for Remote Sensing XXVIII

作者： Dong, Jieke Duan, Yansong Zhou, Qi Zhao, Xinbo Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China

ISBN: (纸本)9781510655386;9781510655379

For decades, the design of remote sensors has to make trade-offs among many characteristics such as the field of view (FOV), spatial resolution, spectral resolution, radiometric resolution, and the number of bands. It's inevitable to weaken some characteristics to enhance others. Moreover, these problems lead to using multi-sources of remote sensing data in practical projects where a single sensor can't meet the relevant requirements. The Airborne Dual-mode High-resolution Hyperspectral imager (ADHHI) provides a new solution to the above limitations by the technology of multi-camera stitching. In this way, many excellent but conflicting characteristics can be separated into different imaging sub-systems, and are combined together during data processing. Supported by related processing algorithms and software, ADHHI embeds many excellent characteristics into one system, such as high spatial resolution, high spectral resolution, and high radiometric resolution. Firstly, this paper picks some common imaging sensors to illustrate the problem of conflicting characteristics. Secondly, we introduce the camera structure and sensor parameters of ADHHI. Then, we sketch out the data processing workflow and elaborate on relevant principles and results of the whole geometric correction, such as homospectral stitching and hetero-spectral registration. Finally, this airborne hyperspectral imager's advantages and application prospects are concluded.

关键词： geometric correction image stitching hyperspectral imager airborne pushbroom

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Segmentation of Electrical Discharge Processes Based on the image Analysis of Plasma-Electrolyte Oxidation 19

Segmentation of Electrical Discharge Processes Based on the ...

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conference on Electro-Optical and Infrared systems - Technology and Applications XIX

作者： Zhukov, S. Voronin, V. Semenishchev, E. Chudinov, D. Zhukova, O. Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia

ISBN: (纸本)9781510655461

In the process of plasma-electrolytic synthesis, a new physical surface is synthesized, consisting of a metal oxide layer of a modified surface and the synthesis of elements of a set of electrolyte plasma, the nodal sources of the components of which are the components of the electrodes (electrolyte and metal surface). In this regard, the classification of plasma electro-discharge processes based on analyzing optical and electrical sensor data using machine learning methods is an actual task. It can be used for intelligent control algorithms of the sensor layers operations and conduct analytical and quantitative studies of the properties of nodal substances. The paper presents the experimental analysis of video and electrical parameters of the oxygen process, automated processing of the basic features of images of plasma-electrolyte discharges, and a segmentation approach of the electric-discharge machining. This approach can help create microsensor elements and materials and systems for intelligent modeling and launching of electrochemical methods for creating an electrolyte plasma and directed synthesis of substances. To test the performance of the proposed algorithm, the database STANKIN is used.

关键词： segmentation image processing electrical discharge plasma-electrolyte oxidation

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Comparison of feature selection algorithms for Data classification problems 8

Comparison of feature selection algorithms for Data classifi...

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8th International conference on Information Technology and Nanotechnology, ITNT 2022

作者： Tislenko, M.D. Gaidel, A.V. Kupriyanov, A.V. Samara National Research University Samara Russia Image Processing Systems Institute RAS-Branch of the FSRC 'Crystallography and Photonics' RAS Samara Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

This article discusses various feature selection algorithms, namely SelectKBest with different statistical criteria and Random Forest algorithm, compares classification accuracy with and without feature selection algorithms on different datasets. As a result, it was found that in the general case, Random Forest algorithm shows the best classification, but on some datasets it is better to use SelectKBest with certain statistical criteria. © 2022 IEEE.

关键词： Classification (of information)

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Experimental Results of Local Backprojection for Monostatic THz SAR Imaging

Experimental Results of Local Backprojection for Monostatic ...

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2025 International conference on Mobile and Miniaturized Terahertz systems, ICMMTS 2025

作者： Ivanenko, Yevhen Batra, Aman Vu, Viet Thuy Pettersson, Mats I. Kaiser, Thomas Blekinge Institute of Technology Karlskrona371 79 Sweden University of Duisburg-Essen Duisburg470 57 Germany

ISBN: (纸本)9798350365832

The use of THz frequencies has enabled the opportunity to perform synthetic-aperture radar (SAR) imaging at the sub-mm level. It is of great interest for applications where high-resolution remote sensing in short range is required. However, with the increase of the operating frequencies from microwave to THz, the SAR image formation algorithms work with a larger amount of data and become more sensitive to phase errors that can be caused by insufficient signal sampling rate or physical factors that cause platform deviations. This motivates the use of fast image formation capable to handle phase errors. In this paper, we present the experimental results on the performance of the local backprojection (LBP) algorithm for processing THz SAR signals. The LBP algorithm has been tested with the real data in the frequency range 0.325-0.5 THz. The results demonstrate the efficiency of the LBP algorithm for the SAR scene reconstruction and highlight the necessity of the use of two times higher signal upsampling to achieve reconstruction accuracy similar to the global backprojection algorithm. © 2025 IEEE.

关键词： image sampling

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Wirelessly Controlled Plant Health Monitoring and Medicate System Based on IoT Technology 1

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1st International conference on Advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Ayalew, Lijaddis Getnet Mattihalli, Channamallikarjuna Asmare, Fanuel Melak Bahir Dar Univ Fac Elect & Comp Engn Bahir Dar Inst Technol Bahir Dar Ethiopia

ISBN: (数字)9783030960407

ISBN: (纸本)9783030960407;9783030960391

The availability of good quality fruits and vegetables is paramount in preventing starvation and minimizing outbreaks of diseases which leads to improving quality of life. One of the major obstacles of the mentioned availability is plant leaf disease. Although manpower plays a vital role in detecting such problems it is time-intensive, expensive, and very inefficient. Thus, developing a mechanism to vigorously monitor leaf's health and detect diseases of plant leaves at early stages is mandatory so that one can produce plenty. In this contribution, a system that detects leaf disease is developed using image processing algorithms, the k-nearest neighbor (KNN), support vector machine (SVM), and multilayer perception (MLP) machine learning algorithms are compared based on plant disease detection and classification systems performances. We also developed a prototype of simple-to-install technology that can recognize leaf diseases and allow medicine flow based on the results. This paper presents a smart plant health monitoring system that takes into account humidity, temperature, and soil contents.

关键词： image processing IoT KNN MLP SVM

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