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

共 15614 条记录，以下是311-320 订阅

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Design of an Automated Flotation Cell for Laboratory Operations Using Model-Based System Engineering 20

Design of an Automated Flotation Cell for Laboratory Operati...

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20th International conference on Smart Technologies, EUROCON 2023

作者： Ennawaoui, Amine Chhiti, Younes Bensitel, Mohammed Mousaid, Ilyass Ennawaoui, Chouaib Chebak, Ahmed Green Tech Institute UM6P École Nationale Superieure de Chimie Ibn Tofail University Benguerir Laboratoire des Materiaux Avances et de Genie des Precedes Kenitra Morocco Chemistry Department El Jadida Morocco Mohammed Vi Polytechnic University Benguerir Morocco El jadida Morocco Green Tech Institute Mohammed Vi Polytechnic University Benguerir Morocco

ISBN: (纸本)9781665463973

Automation in flotation cells has become an increasingly popular topic in the mineral processing industry due to its ability to increase efficiency and reduce operational costs. This paper discusses the design and implementation of an automated flotation cell system for a mineral processing plant. The system utilizes advanced control algorithms and sensors to optimize the flotation process, resulting in improved product quality and reduced energy consumption. This manuscript outlines the first stage in the creation and development of an automated flotation cell for use in educational and research settings, using model-based systems engineering (MBSE) and systems modeling language (SysML) via the CESAM modeling and architecting framework. Our case study employs this method to construct and demonstrate an operational perspective layout of the flotation cell's initial design phase. Moreover, the potential for the automation of flotation cells is highlighted as a promising avenue for improving sustainability and revolutionizing the mineral processing industry. © 2023 IEEE.

关键词： Modeling languages

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Automated Anemia Detection Through Fingernail image Analysis 4

Automated Anemia Detection Through Fingernail Image Analysis

引用

4th International conference on Power, Energy, Control and Transmission systems, ICPECTS 2024

作者： Geetha, A. Stanley, Mini Anuradha, S. Maya, P.R. Nalini, M. Sriharipriyan, P. Sri Sairam Engineering College Department of English Chennai India Sri Sairam Engineering College Electronics and Instrumentation Engineering Chennai India

ISBN: (纸本)9798331508845

Millions of people worldwide, especially in low- and middle-income nations, suffer from anemia, a common health problem. The disease is characterized by a lack of hemoglobin or red blood cells, which can result in symptoms including weakness and exhaustion as well as serious consequences if ignored. Conventional techniques for identifying anemia usually entail intrusive blood tests, which can be expensive, time-consuming, and sometimes unavailable to a large number of people. In order to overcome these obstacles, this study uses MATLAB to provide an automated, non-invasive method for detecting anemia via fingernail image analysis. This project's main goal is to develop an effective and precise anemia screening tool by utilizing machine learning and image processing methods. The fingernail photos are taken by the system and pre-processed to improve quality and guarantee consistency. The technology uses sophisticated image processing techniques to extract pertinent elements from the fingernail photos, including texture and color characteristics that indicate hemoglobin levels. Machine learning algorithms use these traits to categorize the photos as either anemic or non-anemic. MATLAB is used because of its strong skills in machine learning and image processing. From picture capture and pre-processing to feature extraction and classification, the program streamlines the whole procedure. MATLAB's extensive toolkit makes it possible to manipulate photos effectively and has strong machine learning capabilities, which makes it the perfect platform for creating an automatic detection system. The system is guaranteed to be scalable, simple to install, and accurate through the use of MATLAB. Clinical data is used to thoroughly validate the system's functionality. The machine learning models are trained and tested using images from both non-anemic individuals and patients with clinically confirmed anemia. The system's sensitivity, specificity, and accuracy are assessed to guar

关键词： Noninvasive medical procedures

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Intelligent processing algorithms for Motion Capture Data Oriented to Artificial Intelligence 2

Intelligent Processing Algorithms for Motion Capture Data Or...

引用

2nd IEEE International conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Lu, Zhenglei Beijing Information Technology College General Ability Teaching Department Physical Education Teaching Research Office Beijing China

ISBN: (纸本)9798350317558

Traditional motion capture systems are prone to environmental interference, resulting in noise and errors in the captured data. This article proposes an artificial intelligence oriented intelligent processing algorithm for motion capture data, which improves the quality of motion capture data and reduces noise and errors. This article utilizes sensors to collect a large amount of motion timing data and motion image data, preprocesses the data, and constructs a Convolutional Neural Network (CNN) Long Short Term Memory (LSTM) model. By using the CNN model to extract features from images, this paper uses the LSTM model to analyze temporal data, fuses the output of LSTM and CNN, and finally estimates the motion attitude. The data results of the test set indicate that the average mean square error of motion attitude estimation using the CNN-LSTM model is only 0.03. The use of the CNN-LSTM model can effectively improve the performance of motion data pose analysis. © 2024 IEEE.

关键词： attitude estimation convolutional neural network intelligent processing long short term memory motion capture data

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IRT and ANN Based System Identification Schemes for Model Based Fault Detection of Nonlinear systems

IRT and ANN Based System Identification Schemes for Model Ba...

引用

IEEE International conference on Aerospace Electronics and Remote Sensing Technology (ICARES)

作者： Alwagait, Mohammed Khan, Mohammad Obaidullah Imam Mohammad Ibn Saud Islamic Univ Coll Engn Elect Engn Dept Riyadh Saudi Arabia

ISBN: (纸本)9798350344004

Faults in electrical power transmission systems can cause system failures and even may cause explosions. It is desired to remove a faulty component immediately to prevent further damage to the system and the environment. To address this, a typical technique is devised based on thermal imaging and various artificial neural network algorithms. The faulty element or component will radiate or emit higher energy when compared to normal or healthy conditions, because of the higher current flow rates. The thermal image taken from such defective part of the power system will be more highlighted in the image in contrast with the normal cool background. This drastic change in the grey level values in contrast with the healthy power system's picture hints or predicts a fault in that region. To support this methodology, an exhaustive simulation is implemented and demonstrated using thermal image processing and self-learning neural network algorithms and the simulation results are compared. This analysis is performed through various types of ANN techniques, and comparisons are established between them to report the network with best prediction results on a typical 'Step' dataset and 'Realistic' dataset. A similar low score mean square error MSE is exhibited with these models, and the R-square values are closer to the best score of one in all algorithms discussed. A better graph is obtained in Levenberg-Marquadrdt training Algorithm where most of the predictions fall alongside the target;however, Bayesian regularization gives a better plot than LM with the best fit is being obtained at lesser number of iterations that is in less time.

关键词： AI Artificial Neural Network Training algorithms image Extraction IRT Power System Faults Thermal Imaging

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Latest Trends on Satellite image Segmentation 5th

Latest Trends on Satellite Image Segmentation

引用

5th International conference on Communication and Intelligent systems, ICCIS 2023

作者： Borkar, Sahil Chidrawar, Krishna Naik, Sakshi Turuk, Mousami P. Vaijapurkar, Vaibhav B. SCTRs Pune Institute of Computer Technology Pune India

ISBN: (纸本)9789819720781

For the last few decades satellite imaging technology has taken massive strides towards higher spatial resolution, larger swath coverage and almost real-time data delivery. Satellite imaging or remote sensing is extensively used in optical imaging of the globe, military surveillance, deforestation detection, etc. Multispectral imaging by satellite has enabled an exceptional understanding of the earth by imaging beyond the realm of the visible spectrum. Against this backdrop image segmentation using machine learning, deep learning models and image processing has prompted several new approaches and techniques for satellite image segmentation. This survey provides a comprehensive review of the recent literature, covering the novel approaches in image segmentation using satellite imaging as well as others. There is a broad coverage of segmentation algorithms of UNET, TSVM and Random Walker. It investigates the strengths, challenges and novel aspects and compares precisions and deliberate potential research outlooks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image segmentation

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Do We Still Need Non-Maximum Suppression? Accurate Confidence Estimates and Implicit Duplication Modeling with IoU-Aware Calibration

Do We Still Need Non-Maximum Suppression? Accurate Confidenc...

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IEEE/CVF Winter conference on Applications of Computer vision (WACV)

作者： Gilg, Johannes Teepe, Torben Herzog, Fabian Wolters, Philipp Rigoll, Gerhard Tech Univ Munich Munich Germany

ISBN: (纸本)9798350318920;9798350318937

Object detectors are at the heart of many semi- and fully autonomous decision systems and are poised to become even more indispensable. They are, however, still lacking in accessibility and can sometimes produce unreliable predictions. Especially concerning in this regard are the-essentially hand-crafted-non-maximum suppression algorithms that lead to an obfuscated prediction process and biased confidence estimates. We show that we can eliminate classic NMS-style post-processing by using IoU-aware calibration. IoU-aware calibration is a conditional Beta calibration;this makes it parallelizable with no hyperparameters. Instead of arbitrary cutoffs or discounts, it implicitly accounts for the likelihood of each detection being a duplicate and adjusts the confidence score accordingly, resulting in empirically based precision estimates for each detection. Our extensive experiments on diverse detection architectures show that the proposed IoU-aware calibration can successfully model duplicate detections and improve calibration. Compared to the standard sequential NMS and calibration approach, our joint modeling can deliver performance gains over the best NMS-based alternative while producing consistently better-calibrated confidence predictions with less complexity. The code for all our experiments is publicly available(1).

关键词： accountable algorithms algorithms ethical computer vision Explainable fair image recognition and understanding privacy-preserving

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Deep Learning-based Early Lung Cancer Detection using Multi-Model image Fusion Technique 2

Deep Learning-based Early Lung Cancer Detection using Multi-...

引用

2nd International conference on Automation, Computing and Renewable systems, ICACRS 2023

作者： Wahengbam, Monita Sriram, M. Singh, Tonjam Gunendra Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Tamil Nadu Chennai India

ISBN: (纸本)9798350340235

Lung cancer, a significant global health concern, continues to take many lives, especially as a result of late-stage diagnoses. Early detection continues to have the most significant impact on patient outcomes and mortality rates. This research examines how modern image-processing methods might transform the early diagnosis of lung cancer. To investigate the application of deep learning algorithms, computer-aided diagnosis systems, and the synergistic integration of multimodal imaging techniques to increase the accuracy and efficiency of lung cancer diagnosis. The article thoroughly examines current approaches, presents a novel framework designed for early detection, and rigorously assesses the efficacy of the suggested system using actual medical imaging data. The research has the potential to radically alter lung cancer diagnosis, encourage a new era in early intervention, and eventually help to significantly lower lung cancer mortality rates by bridging the gap between cutting-edge image processing and clinical practice. © 2023 IEEE.

关键词： Computer Aided Diagnosis Deep Learning image processing Lung Cancer Multimodal Imaging Fusion

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A Survey on Automated Disease Diagnosis and Classification of Herbal Plants Using Digital image processing 6

A Survey on Automated Disease Diagnosis and Classification o...

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6th International conference on Information systems and Computer Networks, ISCON 2023

作者： Khandelwal, Ankit Shukla, Aasheesh Sain, Mangal GLA University Electronics and Communication Dept. Mathura India Dongseo University Information Engineering Dept. Busan Korea Republic of

ISBN: (纸本)9798350346961

This paper presents a review on automated disease detection processing. The primary issue in herbal plants is the diagnosis and stratification of its diseases. The conventional process is unpredictable and inconsistent, therefore techniques for classifying plants diseases using digital method are necessary as they speed up the identification process. This literature discusses various digital methods for classifying plant diseases. The first step is image processing of herbal plants, which make use of traditional image processing techniques and algorithms to categories diseases in herbal plants based on attributes like texture, color, and shape. The second step is plant categorization based on deep learning and machine learning, such as CNN, KNN, logistic regression etc. Deep learning applications mainly lessen the reliance on designs and preprocessing techniques by joining the entire process. This review provides a basic understanding and knowledge of the current state-of-the-art researches, as well as some dynamic trends for future work.. © 2023 IEEE.

关键词： Computer vision

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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...

引用

2023 International conference on Integrated Intelligence and Communication systems, ICIICS 2023

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

ISBN: (纸本)9798350315455

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. © 2023 IEEE.

关键词： linux shell open stack remote sensing data management spark

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An image processing Based Approach Towards Recognition of Underground Cable Defects 2

An Image Processing Based Approach Towards Recognition of Un...

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2nd International conference on Smart Technologies for Power and Renewable Energy, SPECon 2024

作者： Thampi, Aparna Mahendran, N. Abraham, Linu Mariam Sidhartha, M.S. Apj Abdul Kalam Technological University Department of Electrical and Electronics Engineering Saintgits College of Engineering Kottayam Kerala India Tata Power Delhi Distribution Ltd India Digital Core Technology Kochi India

ISBN: (纸本)9798350383041

This paper presents a novel approach for detecting possible faults in underground cables at the manufacturing industry using image processing techniques. With the increasing adoption of underground cables to minimize faults and outages in power transmission systems, ensuring the quality of these cables becomes crucial. Detecting defects at the manufacturing stage can save significant costs and effort by identifying faulty cables before they are deployed in the field. We propose a system that utilizes image processing algorithms to analyze cable images and identify potential faults, such as unbalanced currents, cable sheath faults, and short circuits. The system also calculates the area of each core to ensure equal current flow, and estimates the total cross-sectional area of the cable to assess the adequacy of insulation. The proposed approach offers a non-destructive and efficient method to enhance quality control in the manufacturing process of underground cables. © 2024 IEEE.

关键词： Underground cables

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