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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VIII"

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OpenCV and its applications in Artificial Intelligent Systems 5

OpenCV and its Applications in Artificial Intelligent System...

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5th International conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2024

作者： Odeh, Ayman Odeh, Nada College Of Engineering Al Ain University Department of Software Engineering Al Ain United Arab Emirates College Of Information Technology UAEU Department of Computer Science and Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9798350354690

This paper explores the utilization of OpenCV (Open-Source Computer vision Library) in artificial intelligence (AI) systems, elucidating its pivotal role in advancing various applications across diverse domains. OpenCV, renowned for its comprehensive functionalities and real-time processing capabilities, has become instrumental in tasks such as object detection (OD), facial recognition (FR), and image processing (IP). However, despite its widespread adoption, there remains a need for a thorough examination of its integration within AI frameworks and the optimization of its functionalities. Through a comprehensive analysis, this study delineates the advantages, limitations, and potential areas for improvement of OpenCV in AI applications. Case studies exemplifying its efficacy in OD, FR, and autonomous systems further elucidate its practical implications. By delving into these aspects, this paper not only underscores the significance of OpenCV in AI but also provides valuable insights for researchers, developers, and practitioners aiming to leverage its capabilities in their endeavors. This study collectively underscores the significant role of OpenCV in enhancing the capabilities of AI systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Crop Disease and Pest Detection using Convolutional Neural Networks (CNN) 5

Crop Disease and Pest Detection using Convolutional Neural N...

引用

5th International conference on image processing and Capsule Networks, ICIPCN 2024

作者： Kalaimanivel, S. France, K. Hindustan institute of technology and science Department of Computer Applications Chennai22295014 India Hindustan institute of technology and science Department of Computer Applications Chennai India

ISBN: (纸本)9798350367171

Agriculture is often known as the art and science of nurturing soil. It involves preparing plants and animals for use in products. Agriculture is the process of growing crops and rearing animals for human consumption, fiber production, and other reasons. It is one of the oldest and most important human activities, laying the groundwork for food production and billions of people's lives throughout the world. As technology advances, additional capabilities for crop protection and disease prevention become accessible. Artificial Intelligence (AI) and machine Learning (ML) algorithms capture features such as crop and soil monitoring, crop maturity detection, autonomous weeding, intelligent crop spraying, pest and disease detection, and more. This study suggests a novel technique for automated crop disease identification by utilizing Convolutional Neural Networks (CNNs) from the field of computer vision. By performing a thorough testing and validation on separate test sets, the proposed methodology outperforms other existing methods in terms of accuracy. The Plant Village collection, maintained by the Centers for Disease Control and Prevention (CDC), includes damaged plant leaf photos and labels. The proposed method has achieved an accuracy of about 99.6%. © 2024 IEEE.

关键词： Convolutional neural networks

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Transparent machine vision Techniques For Medicinal Plant Species Identification 5

Transparent Machine Vision Techniques For Medicinal Plant Sp...

引用

5th International conference on image processing and Capsule Networks, ICIPCN 2024

作者： Kikome, Christine Okumu, Geoffrey Wagisha, Emmanuel Jjingo, Daudi Kizito, John Marvin, Ggaliwango Makerere University Computer Science Department Kampala Uganda

ISBN: (纸本)9798350367171

Medicinal plants have long been the foundation of the medical system and a source of health and healing, but many people nowadays are unaware of these priceless natural resources or the range of possible applications they provide. Traditional medical professionals known as herbalists mostly depend on their understanding of therapeutic herbs to cure a wide range of illnesses. Unfortunately, identifying medicinal plants by hand from their leaves takes a lot of time, labour, and specialised knowledge. The purpose of this project was to create and construct a web application for the identification of plant species, namely medicinal plants that are present across Uganda. On a publicly available dataset, many machine learning and deep learning models, including Convolutional Neural Networks (CNN), VGG19 and 16, Resnet50, vision Transformer (ViT), and EfficientNet B0, were trained. Many features in the dataset were extracted from each leaf such as its length, width, texture, area, and color. For the ten classes of medicinal plant species identified in the globe, the model's accuracy rates were 99%, 95.70%, 9 2. 5 8 %, 9 7. 9 0 %, 9 1. 2 0 %, and 3 5. 6 9 %, respectively. In order to improve transparency and visual explanations for its picture classifications, this model makes use of Grad-CAM and SHAP, which are explainable AI (XAI) approaches. It is anticipated that a web-based system for the automatic identification of medicinal plants will contribute significantly to the production of pharmaceutical drugs, help members of the community increase their knowledge of medicinal plants, and assist taxonomists in creating more effective methods for identifying species. © 2024 IEEE.

关键词： Medicinal chemistry

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

International Conference on Internet of Everything and Quant...

引用

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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Analysis of Impact of image Restoration and Segmentation on Classification Model 7

Analysis of Impact of Image Restoration and Segmentation on ...

引用

2023 7th International conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Vispute, Sushma Rahul Rajeswari, K. Nema, Aryan Jagtap, Arya Kulkarni, Mrugendra Mohite, Pranav PCCOE Department of Computer Engineering Pune India

ISBN: (纸本)9798350304268

A widely studied problem in computer science is the restoration, segmentation, and classification of images, which involves image processing, computer vision, and machine learning techniques. Deep learning has made significant contributions to this field, bringing machine learning closer to artificial intelligence. Deep learning has a broad range of applications, including in surveillance, healthcare, medicine, and sports. Convolutional neural networks (CNN), a combination of artificial neural networks (ANN) and deep learning techniques, have made incredible advancements in deep learning. This survey compares methods for restoring noisy images, such as wiener filter, wavelet method, and wiener filtering with BM3D technique, using Gaussian blurring and noising methods. The survey also examines the RGB colour model and YCbCr colour model for image segmentation. image classification is studied using CNN, where the survey discusses various parameters of convolutional neural networks, including activation functions and pooling methods. © 2023 IEEE.

关键词： computer vision convolutional neural network face detection face recognition image classification image restoration noise reduction segmentation

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Deep Learning Hybrid Technique for Generation of image Caption

Deep Learning Hybrid Technique for Generation of Image Capti...

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2024 International conference on Signal processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Rakshith, N. Manoj Gowda, B.K. Preetham, N. Tejas, M. Baig, Mohammed Ifraz PES college of enginnering mandya Dept. of Information Science and Engineering Mandya India

ISBN: (纸本)9798331540685

image captioning is a fascinating and demanding work with applications in many different fields, including image retrieval, organizing and finding user-interested images, etc. It has enormous potential to replace the tedious process of creating captions for photos, and it works particularly well with massive amounts of picture data. Deep neural network-based techniques have recently shown significant success in the domains of language synthesis, machine translation, and computer vision. In this research, we present a paradigm based on encoder-decoders that may produce grammatically acceptable image captions. This model uses LSTM as the decoder and VGG16 Hybrid Places 1365 as the encoder. Model is evaluated using all standard metrics such as BLEU. Experimental results indicate that the proposed model obtained a BLEU-1 score 0.603774, BLEU-2 score 0.388514, BLUE-3 score 0.244706 on Flickr8k dataset. By comparison with the state-of-the-art techniques, the suggested strategy produced a noteworthy performance. We also present the outcomes of caption creation from real sample photographs, which supports the validity of the suggested method, in order to assess the model's effectiveness even further. © 2024 IEEE.

关键词： Decoding

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Vitamin Deficiency Detection using image processing through Dermatological Symptoms

Vitamin Deficiency Detection using Image Processing through ...

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2024 IEEE International conference on Intelligent Systems and Advanced applications, ICISAA 2024

作者： Janokar, Sagar Somani, Nimish Solanki, Purvi Solanke, Samiksha Solunke, Aditya Soman, Ishan Vishwakarma Institute of Technology Department of Electronics and Telecommunications Pune India

ISBN: (纸本)9798331539948

This study proposes a way to detect vitamin deficiency by combining machine learning and image processing. Computer vision enables the system to recognise visual symptoms of specific vitamin deficiencies. The recommended approach is that the entire procedure can be subdivided into specific key steps, which are initiated from image acquisition, followed by the image preprocessing steps used to enhance their quality. It catches the confusing patterns that are directed toward various abnormalities through a pretrained Convolutional Neural Network (CNN) model. Finally, with such patterns at hand, the categorisation takes place, which in turn helps to identify specific *** extensive experimentation across a diverse dataset, the system demonstrates remarkable accuracy in detecting deficiency. Its non-invasive nature permits early screening. This proves its potential for widespread implementation and directions for future enhancement, such as dataset expansion and exploration of other advanced architectures apart from CNN. With its promising capabilities, this approach represents a significant stride towards enhancing healthcare diagnostics and preventive measures related to vitamin deficiencies. © 2024 IEEE.

关键词： Convolutional neural networks

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IPMV 2023 - Proceedings of 2023 5th International conference on image processing and machine vision

IPMV 2023 - Proceedings of 2023 5th International Conference...

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5th International conference on image processing and machine vision, IPMV 2023

ISBN: (纸本)9781450397926

The proceedings contain 16 papers. The topics discussed include: performance evaluation of recent object detection models for traffic safety applications on edge;tracking of artillery shell using optical flow;action recognition with non-uniform key frame selector;a view direction-driven approach for automatic room mapping in mixed reality;automatic gait gender classification using convolutional neural networks;deep 3D-2D convolutional neural networks combined with Mobinenetv2 for hyperspectral image classification;attention based BiGRU-2DCNN with hunger game search technique for low-resource document-level sentiment classification;strategies of multi-step-ahead forecasting for chaotic time series using autoencoder and LSTM neural networks: a comparative study;semi-supervised defect segmentation with uncertainty-aware pseudo-labels from multi-branch network;and security analysis of visual based share authentication and algorithms for invalid shares generation in malicious model.

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Remote Sensing Scene Classification with Masked image Modeling 2

Remote Sensing Scene Classification with Masked Image Modeli...

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conference on Microwave Remote Sensing - Data processing and applications II

作者： Wang, Liya Tien, Alex MITRE Corp Mclean VA 22102 USA

ISBN: (纸本)9781510666931;9781510666948

Remote sensing scene classification has been extensively studied for its critical roles in geological survey, oil exploration, traffic management, earthquake prediction, wildfire monitoring, and intelligence monitoring. In the past, the machine Learning (ML) methods for performing the task mainly used the backbones pretrained in the manner of supervised learning (SL). As Masked image Modeling (MIM), a self-supervised learning (SSL) technique, has been shown as a better way for learning visual feature representation, it presents a new opportunity for improving ML performance on the scene classification task. This research aims to explore the potential of MIM pretrained backbones on four well-known classification datasets: Merced, AID, NWPU-RESISC45, and Optimal-31. Compared to the published benchmarks, we show that the MIM pretrained vision Transformer (ViTs) backbones outperform other alternatives (up to 18% on top 1 accuracy) and that the MIM technique can learn better feature representation than the supervised learning counterparts (up to 5% on top 1 accuracy). Moreover, we show that the general-purpose MIM-pretrained ViTs can achieve competitive performance as the specially designed yet complicated Transformer for Remote Sensing (TRS) framework. Our experiment results also provide a performance baseline for future studies.

关键词： Remote sensing classification self-supervised learning (SSL) Masked image Modeling (MIM) vision Transformer (ViTs)

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Development of a Trajectory Planning Algorithm for Moving Measuring Instrument for Binding a Basic Coordinate System Based on a machine vision System

Development of a Trajectory Planning Algorithm for Moving Me...

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2023 International conference on Industrial Engineering, applications and Manufacturing, ICIEAM 2023

作者： Krotova, Nataliya Pushkov, Roman Evstafieva, Svetlana Msut Stankin Department of Computer Control Systems Moscow Russia

ISBN: (纸本)9781665475952

This article describes a solution for automating measurements on CNC machines equipped with automatic measurement tools using a vision system that simulates stereoscopic vision. The proposed method of measurement allows non-contact determination of the approximate position of the workpiece on the machine to fill in the parameters of measuring cycles. A three-stage algorithm for image processing by a software module of such a system is described, including recognition of the area with the workpiece, automatic correction of input images for successful contour recognition, determining of the contours of the workpieces and their geometric parameters, as well as calculation of the approximate dimensions of the workpieces relatively to the marks applied to the machine worktable beforehand. The calculated dimensions must be transmitted to the CNC system of the machine to run a parameterized program that calls a measuring cycle that clarifies the position of the workpiece on the machine. A model of the vision system has been implemented and tested. © 2023 IEEE.

关键词： Computer vision

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