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

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A Compendious Analysis of Advances in HE Methods for Contrast Enhancement 2nd

A Compendious Analysis of Advances in HE Methods for Contras...

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2nd International conference on VLSI, Communication and Signal processing, VCAS 2019

作者： vijayalakshmi, D. Nath, Malaya Kumar National Institute of Technology Puducherry Karaikal India

ISBN: (纸本)9789811568398

image enhancement is a crucial pre-processing technique for image processing applications such as satellite images, medical images, and aerial surveillance systems. The image enhancement aims to produce visual content of the image in more pleasing and more suitable for machine vision applications. Enhancement of image can be improved by either increasing the contrast of the image with a low dynamic range or by highlighting the prominent details of the image. In this paper, a comprehensive analysis of bi-histogram and two-dimensional histogram equalization-based contrast enhancement techniques is performed. The performance of the various algorithms has been validated through three different databases and various performance measures. From the compendious analysis, it can be interpreted that modeling an algorithm with lower absolute mean brightness error (AMBE) and higher contrast values can render a better-enhanced image. From the qualitative and quantitative analysis, edge enhancing bi-histogram equalization using guided image filter outperforms the other contrast enhancement techniques. © 2021, Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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Research on Water Conservancy Project Monitoring and Intelligent Identification Method Based on Computer vision Technology

Research on Water Conservancy Project Monitoring and Intelli...

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Telecommunications and Power Electronics (TELEPE), International conference on

作者： Jin Zhu Feng Yang Zhejiang Province Water Conservancy and Hydropower Technology Consultation Center Hangzhou Zhejiang China Zhejiang Zhongshui Engineering Technology Co. Ltd. Hangzhou China

ISBN: (数字)9798350369212

ISBN: (纸本)9798350380002

Monitoring and maintenance of water resources projects is essential to ensure project safety and environmental protection. Traditional monitoring methods often rely on manual inspections and sensor data, but these methods suffer from high cost, low efficiency and limited monitoring range. With the rapid development of computer vision technology, its potential for application in water conservancy engineering has gradually emerged. The purpose of this paper is to study the water conservancy project monitoring and intelligent identification methods based on computer vision technology. We first outline the basic concepts of computer vision and its application scenarios in water conservancy engineering, including structural health monitoring, environmental monitoring and disaster management. Then, the specific applications of image processing techniques, machine learning and deep learning in intelligent recognition are described in detail, especially how these techniques can be utilized to achieve an efficient and accurate real-time monitoring system. Through several case studies, this paper demonstrates the effects and advantages of the application of computer vision technology in actual water conservancy projects. Finally, it summarizes the main findings of the current study and looks forward to the future technological development and application prospects, pointing out the direction of further research. The findings of this paper show that computer vision-based monitoring and intelligent identification methods not only improve the accuracy and efficiency of monitoring, but also provide new technical means for the management and maintenance of water conservancy projects.

关键词： Deep learning Computer vision Accuracy Water conservation Real-time systems Maintenance Floods Protection Monitoring Water resources

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A Study on the Segmentation and Classification of Diabetic Retinopathy images Using the K-Means Clustering Method

A Study on the Segmentation and Classification of Diabetic R...

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IEEE Signal processing and Communications applications (SIU)

作者： Ramazan İncir Ferhat Bozkurt Gümüşhane Üniversitesi Atatürk Üniversitesi Gümüşhane Türkiye Bilgisayar Teknolojileri Bölümü Bilgisayar Mühendisliği Bölümü Erzurum Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Diabetic Retinopathy (DR) is a retinal disease caused by diabetes, representing one of the most prevalent causes of vision loss affecting millions of people worldwide. Swift detection and treatment of this condition are crucial for preventing the disease. Various deep learning and machine learning algorithms have been employed for disease detection and classification, often overlooking the data preprocessing stage. In the data preprocessing phase of this study, segmentation of important lesions, such as hard exudates, was conducted using the K-Means clustering method. The identified lesions were highlighted on the original images. The resulting dataset was then classified using pre-trained architectures, namely EfficientNetV2-M, ResNet50, MobileNet, and DenseNet121. After training on the APTOS dataset, the EfficientNetV2-M model achieved an accuracy of 95.16%. The classification results indicated the contribution of the lesion highlighting process during data preprocessing to the overall classification accuracy

关键词： Training image segmentation Diabetic retinopathy Accuracy Clustering methods Data preprocessing Signal processing

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Artificial Intelligence-Powered Internet of Things for Farming and Plant Research

Artificial Intelligence-Powered Internet of Things for Farmi...

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Electronics and Renewable Systems (ICEARS), International conference on

作者： A. Padma P. Suresh G. Narsimlu M Amarnath B. R Sreedhar Department of Mathematics Chaitanya Bharathi Institute of Technology

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The wearable fiber optic sensors for plants to monitor microenvironment and growth wearable fiber optic plant sensors for microclimate and growth monitoring. The plant agricultural sectors are facing challenges because to population expansion and climate ***-powered computer vision-based plant growth monitoring system the goal of this research project is to use computer vision to create an AI-based plant growth monitoring system. The technology will allow for automatic and real-time evaluation of plant growth and health by utilizing computer vision algorithms and artificial intelligence approaches. Conventional techniques for tracking plant growth require a lot of work and time. By employing image processing techniques to analyze plant photos and extract pertinent features leaf area, plant height, and width, the suggested solution will overcome these drawbacks. Using the collected information, machine learning methods will be used to train models that can identify patterns and forecast plant development. The system will be made to adapt to various plant species and environmental circumstances. This AI-based plant growth monitoring system will help farmers, researchers, and plant enthusiasts by offering precise and timely insights into plant health and growth. This will allow them to make well-informed decisions about plant care and optimize growth conditions. By providing an economical, scalable, and effective system with numerous applications in horticulture, agriculture, and environmental research, this research project seeks to transform plant growth monitoring.

关键词： Temperature sensors Optical fiber sensors Computer vision Plants (biology) Feature extraction Internet of Things Biomedical monitoring Monitoring Intelligent sensors Wearable sensors

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Optimizing CNN performance on CIFAR-10: a study on multi-core processing, batching techniques, and future network architectures

Optimizing CNN performance on CIFAR-10: a study on multi-cor...

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2nd International conference on Mechatronic Automation and Electrical Engineering (ICMAEE 2024)

作者： Datong Jin College of art and science The Ohio State University Columbus 43210 USA

ISBN: (数字)9781837242672

The performance of Convolutional Neural Networks (CNNs) is critically dependent on the underlying computational configurations, particularly in terms of processing capabilities and data handling techniques. As deep learning models become increasingly complex and data-intensive, the choice between single-core and multi-core processing, along with strategies such as batching, becomes pivotal. These configurations directly impact the efficiency and speed of model training and inference, which are essential for applications requiring real-time processing and high accuracy, such as image recognition and automated systems. Study Contributions:This research focuses on comparing the effects of single-core versus multi-core processing configurations on CNN performance, integrating batching techniques to observe their combined influence on reducing latency and increasing throughput. The study reveals that leveraging multi-core processing with batching substantially enhances CNN operations. Additionally, it addresses the deployment challenges of the YOLOv5 architecture, emphasizing the necessity for further architectural improvements. The paper suggests that future enhancements could include adopting deeper network structures to elaborate on complex features and employing methods like ResNet to counteract gradient vanishing problems. Moreover, it advocates for the fine-tuning of models with pre-trained datasets such as imageNet to boost robustness and accuracy, particularly demonstrated on the CIFAR-10 dataset. These recommendations aim to refine CNN capabilities, thereby advancing the broader field of machine learning and computer vision.

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Irregularity detection in net pens exploiting Computer vision 14

Irregularity detection in net pens exploiting Computer Visio...

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14th IFAC conference on Control applications in Marine Systems, Robotics, and Vehicles (CAMS)

作者： Schellewald, Christian Stahl, Annette Norwegian Univ Sci & Technol NTNU Dept Engn Cybernet N-7491 Trondheim Norway SINTEF Ocean AS Brattorkaia 17C N-7010 Trondheim Norway

Protecting the remaining wild salmon stock in Norway is of utmost importance and requires that farmed salmon cannot escape from aquaculture sites. As holes in net-cages are responsible for a large fraction of the escaped salmon the industry has to perform frequent inspections of the fish cage integrity. In this paper we propose an image processing and computer vision based attention mechanism towards a more automated fish-cage inspection. The presented algorithm allows to indicate areas in videos showing net-pen locations where potential holes are present. We show the effectivity of the approach on video-recordings of holes also in commercial fish-cages. Copyright (C) 2022 The Authors.

关键词： Hole detection Computer vision Morphological Filter ROV Fish cage Net detection irregularity detection

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Advanced Medical image processing for Lung Cancer Detection using machine Learning Algorithms

Advanced Medical Image Processing for Lung Cancer Detection ...

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International conference on Inventive Computation Technologies (ICICT)

作者： P. Karthika T. Papitha Christobel Department of Computer Science and Applications SRM Institute of Science and Technology Ramapuram Chennai

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

One of the main causes of cancer-related deaths is lung cancer, and increasing survival rates requires early detection. The use of sophisticated machine learning (ML) algorithms to improve the identification of lung cancer from chest X-rays and CT images is investigated in this study. vision Transformers (viT), Reinforcement Learning (RL), Generative Adversarial Networks (GANs), Meta-Learning, and Ensemble Learning are some of the state-of-the-art methods we use. Compared to conventional CNNs, By capturing long-range dependencies in medical pictures, vision Transformers (viT) can improve accuracy by up to 85–96% compared to typical CNN models, especially when it comes to feature extraction and categorization. The optimization of diagnostic workflows using Reinforcement Learning (RL) results in a 90% improvement in decision-making efficiency and adaptive learning capabilities, which greatly enhance real-time picture analysis. Training datasets can be enhanced with Generative Adversarial Networks (GANs), which create realistic synthetic images and increase model generalization by 90–97%. This is crucial in situations where data is scarce or unbalanced. For uncommon or underrepresented cancer cases, meta-learning improves classification accuracy by 90% by allowing models to learn from sparsely labeled data. Ensemble learning reduces bias and variation by combining many models using approaches like XGBoost, Bagging, and Stacking, increasing total accuracy by 80–95%. When compared to conventional methods, key performance indicators including precision, recall, and F1 score demonstrate significant gains, with sensitivity rising by up to 90% and specificity improving by 85%. These cutting-edge algorithms greatly improve the detection of lung cancer, facilitating quicker and more precise diagnoses and assisting clinicians in making decisions that will benefit patients.

关键词： Metalearning Computer vision Adaptation models machine learning algorithms Accuracy Lung cancer Reinforcement learning Transformers Generative adversarial networks Ensemble learning

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Omnidirectional Gradient and Its Application in Stylized Edge Extraction of Infrared image

Omnidirectional Gradient and Its Application in Stylized Edg...

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image processing, Computer vision and machine Learning (ICICML), International conference on

作者： Jun Wu Xingzhan Wei Chongqing University Chongqing China Chinese Academy of Sciences Chongqing Institute of Green and Intelligent Technology Chongqing China Chongqing School University of Chinese Academy of Sciences Chongqing China

ISBN: (纸本)9781665464697

Gradient computing is a low-level technology widely used in image processing. For large gradient magnitude, the pixel value in the field changes a lot, and for small gradient magnitude the pixel in the domain changes little. This is the basis of classical edge extraction algorithms, but it is often necessary to manually set thresholds to differentiate. This paper innovatively brings out the concept of omnidirectional gradient, which uses flexible convolution kernel radius and special law to calculate, and omnidirectional gradient pays more attention to gradient direction and analyzes the relationship and change of the gradient direction with different kernel radius. We present here an algorithm for stylized edge extraction based on omnidirectional gradient, overcoming the drawback of classical edge extraction algorithms that require manual thresholding. Experimental results show that the proposed method outperforms the classical edge extraction methods in terms of adaptive, consistent, and visually friendlier features for infrared imaging. In addition, the algorithm is fast and efficient, its result can be used as real-time input for subsequent applications.

关键词： Thresholding (Imaging) Convolution image edge detection Manuals machine learning Infrared imaging Feature extraction

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Artificial Intelligence Pathologist: The use of Artificial Intelligence in Digital Healthcare

Artificial Intelligence Pathologist: The use of Artificial I...

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IEEE Global conference on Artificial Intelligence and Internet of Things (GCAIoT)

作者： Kaddour, Asmaa Ben Ali Abdulaziz, Nidhal Univ Wollongong Fac Engn & Informat Sci Dubai U Arab Emirates

ISBN: (纸本)9781665438414

Artificial intelligence is bringing revolutionary changes to so many industries, by introducing them to a new era, full of technological advancements. The healthcare industry has been one of the most beneficial to this change, by merging digital transformation and healthcare, to form digital healthcare. Thereby introducing digital pathology, which implements image processing algorithms to help pathologists analyze and examine a diagnosis faster and more efficiently. It not only reduces the long hours pathologists used to take in laboratory analysis but also reduces human error. Therefore, healthcare digitalization has allowed the integration of computer vision into the medical field, with the use of Artificial intelligence techniques such as deep learning and machine learning algorithms. However, past research work has been limited to using AI models to diagnosis one specific disease at a time. Whereas this research work aims to develop an AI model that will automatically perform pathological analysis, to determine the diagnosis for multiple diseases from a medical image, then provide the medical report, while securing the patient's data, and assisting them with any questions they might have regarding the diagnosis. This research applies deep learning and machine learning algorithms for image classification via CNN architectures and feature extraction via Morphological properties. The model achieved great outcomes, with high accuracy and good F1-score results of 90.47% and 0.8332 respectively. The resultant model diagnoses 12 medical disorders, with an overall of 29 diagnostic cases, making it the only one of its kind in digitized healthcare applications.

关键词： Artificial Intelligence Computer vision Deep Learning machine Learning Digital Healthcare Digital Pathology image Classification Feature Extraction

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Optimization of Aquaponics System Efficiency Based on Artificial Intelligence Approach

Optimization of Aquaponics System Efficiency Based on Artifi...

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Computing and machine Intelligence (ICMI), International conference on

作者： Jayden Liu William Jiang The Harker School San Jose USA

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

This paper aims to design and implement an ML-based approach to learn from NeuroAqua - the AI and IoT-based aquaponics system set up in our previous research at both a lab setting and larger-scale Ouroboros Aquaponics Farm (Half Moon Bay, CA) to enhance system stability and efficiency. Utilizing the data gathered from the wireless sensors, a structured database was formed to store the aquaponics environmental conditions, water quality, nutrient components, and plant images. We used the ML model to find the important factors having the largest impact on plant growth and their optimal amount levels. First, computer vision with image processing was applied to develop auto plant growth monitoring and to measure plant growth rate as the target variable more accurately and automatically for ML. Then feature engineering on the input variables was performed to enhance model performance and accuracy for a smaller dataset. ML algorithms including Linear Regression, Bagging Regressor, Decision Tree, Random Forest, XGBoost and Artificial Neural Network were applied and evaluated based on key performance metrics. The findings show that XGBoost outperformed the other models with 91.6% accuracy and also had the lowest MAE. Random Forest came in second with 90.9% accuracy and then Bagging Regressor in third with 88.5% accuracy. Lastly, according to the feature importance analysis conducted on the best model of XGBoost, Nitrogen had the largest impact on plant growth, followed by Nitrate, Nitrite, Light, and Phosphorus. Hence the initial results would recommend to closely monitor these top important factors together with plant growth in NeuroAqua's monitoring applications.

关键词： Wireless communication Wireless sensor networks Accuracy Databases image processing Computational modeling Sensors

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