检索结果-内蒙古大学图书馆

58th Annual International Telemetering Conference, ITC 2023

作者： Loreto Cornídez, Alan Manuel Fuentes Gutiérrez, Rubén Diego College of Electrical and Computer Engineering The University of Arizona TucsonAZ85721 United States

Modern day computer vision applications are frequently implemented using machine learning approaches. While these implementations can perform very well, the performance is heavily dependent on sufficient and accurate training data. Due to a lack of adequate training data, the Arizona Autonomous Vehicles Club (AZA) decided to implement the generalized hough transform to detect shapes in a live video feed from an unmanned aerial system (UAS). The hough transform is computationally intensive and since real-time performance is required, a serial approach may not have the execution speed necessary for the application. image processing techniques include matrix multiplication and convolution operations which are highly parallelizable. Therefore, the algorithm was parallelized and implemented on a graphics processing unit (GPU). Performance profiling was done on both machine learning and traditional approaches where execution time and accuracy were compared. © 2023 International Foundation for Telemetering. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Guest Editorial: Smart Measurement in machine vision for Challenging applications

引用

IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE 2023年第8期26卷 3-3页

作者： Venkatesan, C. AI-Turjman, Fadi Pelusi, Danilo HKBK Coll Engn Dept ECE Bengaluru India Near East Univ Dept Artificial Engn Istanbul Turkiye Univ Teramo Dept CSE Teramo Italy

Smart measurements are widely deployed in many applications due to the technology advancement. For various industrial applications, automated inspection and analysis based on the image is provided by machine vision. For the measurements in these applications, sensors must be connected. machine vision tries to creatively combine already existing technology and use them to address current issues. The term "measurement" is frequently used to refer to many tasks and is the cornerstone of industrial automation and security deployment. This Special Issue of Instrumentation & Measurement Magazine addresses some novel achievements in the measurement and instrumentation science and technology fields. It advances machine vision concerning production, application of smart materials, measurement and estimation techniques, etc. The variety of selected papers reflects the efforts made by the authors to focus either on methodological aspects or technical issues. In particular, three papers have been accepted for publication, reflecting several aspects of the abovementioned fields by covering machine vision and image processing technology.

关键词： Special issues and sections Smart devices Measurement techniques machine vision

来源：评论

学校读者我要写书评

暂无评论

A comprehensive study of feature extraction techniques for plant leaf disease detection

引用

MULTIMEDIA TOOLS AND applications 2022年第1期81卷 367-419页

作者： Vishnoi, Vibhor Kumar Kumar, Krishan Kumar, Brajesh Gurukula Kangri Vishwavidyalaya Dept Comp Sci Haridwar 249404 Uttarakhand India MJP Rohilkhand Univ Dept Comp Sci & IT Bareilly 243006 Uttar Pradesh India

Agriculture has been the most primary source of the livelihood of man for thousands of years. Even today, it provides subsistence to about 50% of the world population. Plant diseases are the serious cause of big losses to crop production every year worldwide. It is necessary to keep the plants healthy at various stages of their growth/development to deal with the financial losses from plant diseases. Symptoms of infections are visible mainly at plant leaves;thus leaves are commonly used to detect and identify the diseases. Detecting the disease through visual observation is itself a challenging task and requires a lot of human expertise. image processing techniques along with computational intelligence or soft computing techniques can be used to provide a better assistance for disease detection to the farmers. A disease in plants can be detected based on its symptoms extracted in the form of features. Feature extraction techniques thus play a vital role in such systems. The paper emphasizes on the review of hand-crafted and deep learning based feature extraction with their merits and demerits. It provides a comprehensive discussion on a variety of image features such as color, texture, and shape for various disorders in different cultures.

关键词： Computer vision image processing Plant leaf diseases Feature extraction Classification machine learning

来源：评论

学校读者我要写书评

暂无评论

VashaNet: An automated system for recognizing handwritten Bangla basic characters using deep convolutional neural network

引用

machine LEARNING WITH applications 2024年 17卷

作者： Raquib, Mirza Hossain, Mohammad Amzad Islam, Md Khairul Miah, Md Sipon Noakhali Sci & Technol Univ Dept Informat & Commun Engn Noakhali 3814 Bangladesh Islamic Univ Dept Biomed Engn Kushtia 7003 Bangladesh Islamic Univ Dept Informat & Commun Technol Kushtia 7003 Bangladesh Univ Carlos III Madrid Dept Signal Theory & Commun Leganes 28911 Madrid Spain Univ Galway Sch Comp Sci Galway H91 TK33 Ireland

Automated character recognition is currently highly popular due to its wide range of applications. Bengali handwritten character recognition (BHCR) is an extremely difficult issue because of the nature of the script. Very few handwritten character recognition (HCR) models are capable of accurately classifying all different sorts of Bangla characters. Recently, image recognition, video analytics, and natural language processing have all found great success using convolutional neural network (CNN) due to its ability to extract and classify features in novel ways. In this paper, we introduce a VashaNet model for recognizing Bangla handwritten basic characters. The suggested VashaNet model employs a 26 -layer deep convolutional neural network (DCNN) architecture consisting of nine convolutional layers, six max pooling layers, two dropout layers, five batch normalization layers, one flattening layer, two dense layers, and one output layer. The experiment was performed over 2 datasets consisting of a primary dataset of 5750 images, CMATERdb 3.1.2 for the purpose of training and evaluating the model. The suggested character recognition model worked very well, with test accuracy rates of 94.60% for the primary dataset, 94.43% for CMATERdb 3.1.2 dataset. These remarkable outcomes demonstrate that the proposed VashaNet outperforms other existing methods and offers improved suitability in different character recognition tasks. The proposed approach is a viable candidate for the high efficient practical automatic BHCR system. The proposed approach is a more powerful candidate for the development of an automatic BHCR system for use in practical settings.

关键词： Artificial intelligence Character recognition Computer vision Deep convolutional neural network image processing

来源：评论

学校读者我要写书评

暂无评论

Kidney medicine meets computer vision: a bibliometric analysis

引用

INTERNATIONAL UROLOGY AND NEPHROLOGY 2024年第10期56卷 3361-3380页

作者： Chen, Junren Chen, Rui Chen, Liangyin Zhang, Lei Wang, Wei Zeng, Xiaoxi Sichuan Univ West China Hosp Dept Nephrol Chengdu 610041 Sichuan Peoples R China Sichuan Univ West China Hosp West China Biomed Big Data Ctr Chengdu 610041 Sichuan Peoples R China Sichuan Univ Sch Comp Sci Chengdu 610065 Sichuan Peoples R China Sichuan Univ Med X Ctr Informat Chengdu 610041 Sichuan Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Chengdu Univ Informat Technol Sch Automat Chengdu 610225 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Sichuan Peoples R China

Background and objectiveRapid advances in computer vision (CV) have the potential to facilitate the examination, diagnosis, and treatment of diseases of the kidney. The bibliometric study aims to explore the research landscape and evolving research focus of the application of CV in kidney medicine *** Web of Science Core Collection was utilized to identify publications related to the research or applications of CV technology in the field of kidney medicine from January 1, 1900, to December 31, 2022. We analyzed emerging research trends, highly influential publications and journals, prolific researchers, countries/regions, research institutions, co-authorship networks, and co-occurrence networks. Bibliographic information was analyzed and visualized using Python, Matplotlib, Seaborn, HistCite, and *** was an increasing trend in the number of publications on CV-based kidney medicine research. These publications mainly focused on medical image processing, surgical procedures, medical image analysis/diagnosis, as well as the application and innovation of CV technology in medical imaging. The United States is currently the leading country in terms of the quantities of published articles and international collaborations, followed by China. Deep learning-based segmentation and machine learning-based texture analysis are the most commonly used techniques in this field. Regarding research hotspot trends, CV algorithms are shifting toward artificial intelligence, and research objects are expanding to encompass a wider range of kidney-related objects, with data dimensions used in research transitioning from 2D to 3D while simultaneously incorporating more diverse data *** present study provides a scientometric overview of the current progress in the research and application of CV technology in kidney medicine research. Through the use of bibliometric analysis and network visualization, we elucidate emerging trends, key so

关键词： Bibliometric analysis Computer vision Artificial intelligence Kidney medicine

来源：评论

学校读者我要写书评

暂无评论

Cotton Leaf Disease Based on image processing Using Deep Learning

Cotton Leaf Disease Based on Image Processing Using Deep Lea...

引用

2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Menaga, D. Shri Roshan, B.T. Shri Vikaas, S.R. St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350319019

One of the most important occupations in India is agriculture. Out of all the crops, cotton is the best and is crucial to the agricultural economy of the country. In India, 40-50 million people work in the cotton trade and processing, while six million farmers directly depend on the crop. The cotton leaf disease has grown in importance over the last few decades, resulting in losses to crops, farming operations, and financial resources. To achieve this aim, we first need to acquire different images of cotton plants. We can use image processing techniques to analyze dead leaf images and extract features like color, texture, and other characteristics with the Deep CNN model's assistance. In addition to being less expensive and more straightforward, automatic disease detection supports machine vision, which offers image-based automated process control and inspection. To properly train the algorithm, we will be using a dataset of approximately 1752(approximately 440 images in each class) images classified into different categories according to the diseases. This model will be developed using tools present in Anaconda such as Jupyter Notebook, Spyder etc. The results of this project will demonstrate whether using it in real-time applications is feasible and whether traditional or manual disease and pest identification could benefit from the use of IT- based solutions. © 2024 IEEE.

关键词： Cotton

来源：评论

学校读者我要写书评

暂无评论

applications of Diffusion Model image Restoration in the Field of Heritage Restoration: Overview and Outlook 3

Applications of Diffusion Model Image Restoration in the Fie...

引用

3rd International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2023

作者： Li, Yiran Nanjing University of Finance and Economics Qixia District Jiangsu Province Nanjing China

ISBN: (纸本)9781643684444

The restoration of museum heritage is an important task with significant cultural and historical value;however, traditional methods of restoration are frequently constrained by the extent of the damage to the heritage as well as the constraints of the restoration techniques. In recent years, a method of restoration known as the diffusion model, which is based on computer vision and machine learning, has been gradually applied to the field of museum relics restoration and has shown enormous potential in relic restoration. This method of restoration was developed in the 1980s and is still in use today. In the field of image restoration, research and application of diffusion models are reviewed, and this article provides a summary of the development history, methodology principles, and application cases associated with these topics. This article provides an introduction to the fundamental ideas and principles underlying diffuse models, as well as a summary of the current state of research and an outlook on potential future trends and prospects pertaining to image repair within the domain of image processing. © 2023 The Authors.

关键词： image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Comparative Evaluation of Deep Learning Models for image Captioning of Satellite images 4th

Comparative Evaluation of Deep Learning Models for Image Cap...

引用

4th International Conference on Innovations in Computational Intelligence and Computer vision, ICICV 2024

作者： Shree, Aishwary Sharma, Rohit Singla, Jai Gopal Dhaka, Vijaypal Singh Verma, Sourabh Singh Malik, Arnav Manipal University Jaipur Jaipur303007 India Space Applications Centre ISRO Ahmedabad380015 India Jayshree Periwal Global School Jaipur302026 India

ISBN: (纸本)9789819769940

In our investigation, we present a thorough comparative examination of contemporary models aimed at generating descriptive narratives based on satellite image analysis, leveraging a fusion of methodologies such as computer vision (CV), natural language processing (NLP), and machine learning (ML). Employing an array of cutting-edge neural network architectures including VGG-19, ResNet-50, DenseNet-201, EfficientNet B7, UNet, and Inception V3, we meticulously analyze intricate features within satellite images to extract essential patterns and information. These features are subsequently integrated into a Long Short-Term Memory (LSTM) network, a robust variant of Recurrent Neural Network (RNN), thereby enhancing our capacity to efficiently interpret and analyze satellite image data. Through exhaustive experimentation on established benchmark datasets such as the Sydney Dataset and RSIC Dataset, we conduct a comprehensive comparative analysis to assess the efficacy of diverse models. The notable advancements achieved by our model in the realm of satellite image captioning hold significant promise for practical applications in domains such as image interpretation. The observed milestones represent a substantive progression within the field, showcasing adaptable utility across diverse sectors. Particularly noteworthy is the superior performance attained through the pairing of EfficientNet B7 (ENet) with LSTM, yielding accuracy rates exceeding 90% across both the Sydney Dataset and RSIC Dataset. This underscores the effectiveness of synergizing ENet with LSTM architectures for satellite image captioning tasks, thereby emphasizing its potential for real-world deployment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Review and Enhancement of Discrete Cosine Transform (DCT) for Medical image Fusion 16th

Review and Enhancement of Discrete Cosine Transform (DCT) fo...

引用

16th International Conference on Information Technology and applications, ICITA 2022

作者： Alhatami, Emadalden Bhatti, Uzair Aslam Huang, MengXing Feng, SiLing School of Information and Communication Engineering Hainan University Haikou570100 China

ISBN: (纸本)9789811993305

An image fusion is a kind of single process which combines the necessary or efficient information from a set of different or similar input images into a single output image where the resulting image is more accurate, informative, and complete than any of the input images with a specific algorithm. image enhancement is a process used to improve the quality of an image and increases the application of these input data images, which is helpful in different fields of science such as medical imaging, microscopic imaging, remote sensing, computer vision, robotics, etc. In this paper, we describe the primary function of image fusion to improve an image’s good quality by evaluating the sharpness. Then we attempt to give an overview of multi-modal medical image fusion methods, emphasizing how we can use the DCT method for medical image fusion. This fused image provides more accurate information about the real world, which is helpful for human vision and machine perception or any further image processing tasks in the future. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image fusion

来源：评论

学校读者我要写书评

暂无评论

Application of Automated Quality Control in Smart Factories - A Deep Learning-based Approach 17

Application of Automated Quality Control in Smart Factories ...

引用

17th Annual IEEE International Systems Conference (SysCon)

作者： Mandapaka, Subbalakshmi Diaz, Catalina Irisson, Hasbanny Akundi, Aditya Lopez, Viviana Timmer, Douglas Univ Texas Rio Grande Valley Dept Comp Sci Edinburg TX 78539 USA Univ Texas Rio Grande Valley Informat & Engn Syst Dept Edinburg TX USA Univ Texas Rio Grande Valley Mfg & Ind Engn Edinburg TX USA

ISBN: (纸本)9781665439947

Industry 4.0 is the ongoing automation of conventional manufacturing and industrial applications using smart technology. Quality control (QC) is a set of procedures to ensure that a manufactured product adheres to a defined set of quality criteria or meets the requirements of the customer. Many applications within the manufacturing domain employ image-processing or machine learning systems but deep learning-based applications are rare. The goal of this project is to leverage deep learning methods for the automation of quality control. A visual QC automation application is proposed that utilizes a camera placed over a product assembly line containing 3-D printed product samples in a smart factory prototype setup for data collection. After model training, the model will perform object detection and recognition for analyzing complex free-form products and perform product dimension and surface analysis to identify the products that meet the quality control guidelines.

关键词： deep learning industry 4.0 smart factory machine vision system quality control automation product defect detection CNN object detection object recognition image classification

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：