检索结果-内蒙古大学图书馆

Gabor Wavelets in Face Recognition and Its applications 1

International Conference on Intelligent Emerging Methods of Artificial Intelligence and Cloud Computing, IEMAICLOUD 2021

作者： Ojha, Manoj Department of Electronics and Communication Engineering Sanskriti University Uttar Pradesh Mathura India

ISBN: (数字)9783030929053

ISBN: (纸本)9783030929046

Face recognition has become an advanced area in the field of recognition and has various applications especially in biometrics, forensic investigations, smart advertising, national database for identity cards etc. Various methods and algorithms are available for face recognition using image processing, machine vision and deep learning. In image processing, wavelets transform is one of the effective methods as they are capable of extracting both spatial and frequency information of the given signal and is a substitute of Fourier transforms in many applications of signal processing used in machine vision, speech analysis etc. Among all the wavelet transforms the most efficient is Gabor wavelet transform that is widely used in research done in the field of image processingas it has certain important mathematical properties. Gaborwavelet transform is also used as they yield distortion tolerance space for various recognitions including face recognition and pattern recognition. This paper addresses the applications of Gabor wavelets in face recognition along with face reconstruction, iris recognition, and corner detection etc. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Exploring the applications of machine vision and Nondestructive Inspection Methods for Quality Control of Metal Implants in the Health Sector

引用

Journal of Commercial Biotechnology 2022年第5期27卷 126-132页

作者： Sun, Ming Institute of Mechanical and Electrical Engineering Heze University HeZe274000 China

The quality control of metal implants is of utmost importance in the health sector to ensure patient safety and optimal performance. In this study, we present a novel machine vision-based nondestructive inspection (NDE) method tailored for welding defects in metal materials used for medical implants. The proposed technique aims to measure the depth of welding defects and analyze the images to identify suitable points for laser cutting or drilling, thereby minimizing the need for destructive testing. To implement this approach, we employ a low-cost 3D printed scanner, integrated with an SLR camera and specialized software, to examine welds in metal materials comprehensively. By identifying potential defects before any damage occurs, this inspection method facilitates accurate assessment of weld strength, enabling the early detection and mitigation of potential issues. The advantages of the proposed inspection technique extend beyond defect identification. It also assists in pinpointing areas that may require further post-processing or additional procedures, ultimately enhancing the overall quality of metal implants. With improved quality control, the likelihood of implant failure and patient complications is significantly reduced. Through the integration of machine vision and nondestructive inspection, this study lays the foundation for advanced quality control methodologies in the health sector. By harnessing cutting-edge technologies, such as 3D printing and image analysis, the proposed approach demonstrates great potential for ensuring the safety and reliability of metal implants, contributing to improved patient outcomes and healthcare standards. © 2022 Authors. All rights reserved.

关键词： Welding

来源：评论

学校读者我要写书评

暂无评论

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

Research on Water Conservancy Project Monitoring and Intelli...

引用

2024 International Conference on Telecommunications and Power Electronics, TELEPE 2024

作者： Zhu, Jin Yang, Feng Zhejiang Province Water Conservancy and Hydropower Technology Consultation Center Zhejiang Hangzhou310009 China Zhejiang Zhongshui Engineering Technology Co. Ltd. Hangzhou310020 China

ISBN: (纸本)9798350369212

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. © 2024 IEEE.

关键词： Environmental monitoring

来源：评论

学校读者我要写书评

暂无评论

V-DETR: Pure Transformer for End-to-End Object Detection 16th

V-DETR: Pure Transformer for End-to-End Object Detection

引用

16th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Dung Nguyen Van-Dung Hoang Van-Tuong-Lan Le Hue Univ Hue Univ Sci Hue City 530000 Vietnam HCMC Univ Technol & Educ Fac Informat Technol Ho Chi Minh City 720000 Vietnam Hue Univ Dept Acad & Students Affairs Hue City 530000 Vietnam

ISBN: (纸本)9789819749843;9789819749850

In the field of computer vision, the task of object detection is one of the important tasks, with many challenges and practical applications. Its task is to classify and determine the location of objects in images or videos. Many machine learning methods, especially deep learning, have been developed to perform this task. This article introduces a model combining DETR (DEtection TRansfomer) and ViT (vision Transformer) as a method to recognize objects in images/videos that only use components of the Transformer model. The DETR model achieves good results in object detection using the Transformer architecture and without the need for complex intermediate steps. The ViT model, a Transformer-based architecture, has brought about a breakthrough in image classification. Combining both architectures opens exciting prospects in computer vision. The input image automatically extracted features using the ViT model previously trained on the imageNet21K dataset, then the features will be fed into the Transformer model to find the classification and bounding box of the objects. Experimental results on test data sets showthat this combined model has better ability in object recognition than DETR and ViT alone. This brings important prospects for the application of the Transformer model not only in the field of natural language processing but also in the field of image classification and object detection. The results of our proposed model have quite high mAP@0.5= 0.444 accuracy, slightly better than the original DETR model. The code is available at https://***/nguyendung622/vitdetr.

关键词： Computer vision Object Detection Classification Convolutional Neural Networks Deep Learning DETR ViT

来源：评论

学校读者我要写书评

暂无评论

MUWS 2024: The 3rd International Workshop on Multimodal Human Understanding for the Web and Social Media 24

MUWS 2024: The 3rd International Workshop on Multimodal Huma...

引用

4th Annual International Conference on Multimedia Retrieval (ICMR)

作者： Kastner, Marc A. Cheema, Gullal S. Hakimov, Sherzod Garcia, Noa Kyoto Univ Kyoto Japan Leibniz Univ Hannover L3S Res Ctr Hannover Germany Univ Potsdam Potsdam Germany Osaka Univ Osaka Japan

ISBN: (纸本)9798400706028

Multimodal human understanding and analysis are emerging research areas that cut through several disciplines like Computer vision (CV), Natural Language processing (NLP), Speech processing, Human-Computer Interaction (HCI), and Multimedia. Several multimodal learning techniques have recently shown the benefit of combining multiple modalities in image-text, audio-visual and video representation learning and various downstream multimodal tasks. At the core, these methods focus on modelling the modalities and their complex interactions by using large amounts of data, different loss functions and deep neural network architectures. However, for many Web and Social media applications, there is the need to model the human, including the understanding of human behaviour and perception. For this, it becomes important to consider interdisciplinary approaches, including social sciences and psychology. The core is understanding various cross-modal relations, quantifying bias such as social biases, and the applicability of models to real-world problems. Interdisciplinary theories such as semiotics or gestalt psychology can provide additional insights on perceptual understanding through signs and symbols across multiple modalities. In general, these theories provide a compelling view of multimodality and perception that can further expand computational research and multimedia applications on the Web and Social media. The theme of the MUWS workshop, multimodal human understanding, includes various interdisciplinary challenges related to social bias analyses, multimodal representation learning, detection of human impressions or sentiment, hate speech, sarcasm in multimodal data, multimodal rhetoric and semantics, and related topics. The MUWS workshop is an interactive event and includes keynotes by relevant experts, a poster session, research presentations and discussion.

关键词： multimodality machine learning image-text relations social media web human understanding semiotics

来源：评论

学校读者我要写书评

暂无评论

image processing Technique for Tracking and Counting of Vehicles Using ROI

Image Processing Technique for Tracking and Counting of Vehi...

引用

2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kumar, Ankit Chandel, Ritika Singh, Monika Apex Institute of Technology Chandigarh University Mohali India

ISBN: (纸本)9798350357769

This research paper presents a novel approach for vehicle tracking and counting utilizing the advanced object detection model YOLOv8 in the field of image processing. The accurate monitoring of vehicular traffic is crucial for various applications, including traffic management, urban planning, and safety analysis. Existing methods often struggle with real-time tracking and counting of vehicles due to challenges such as occlusions, varying lighting conditions, and complex traffic scenarios. In our proposed methodology, we leverage the power of YOLOv8, a deep learning-based object detection model, uses real-time detection to identify and monitor vehicles, ensuring high accuracy and robustness in various traffic environments. The proposed approach provides a valuable tool for traffic management authorities and urban planners, offering precise data for traffic flow analysis and infrastructure development. © 2023 IEEE.

关键词： Deep Learning image processing machine vision Object Detection ROI (Region of Interest) Vehicle Detection YOLO (You Only Look Once)

来源：评论

学校读者我要写书评

暂无评论

Design of inception with deep convolutional neural network based fall detection and classification model

引用

MULTIMEDIA TOOLS AND applications 2023年第8期83卷 23799-23817页

作者： Bhavani, K. Durga Ukrit, M. Ferni SRM Inst Sci & Technol Dept Computat Intelligence Chennai 603203 India

Falling is the most serious health problem for elderly population resulting in serious injuries, if not treated quickly. As the world population gets increased, the number of serious falls and succeeding financial burdens rise accordingly. It is important to detect falls timely to initiate appropriate medical responses to decrease considerable physical, social, and financial losses. Earlier detection of fall events can helps to provide timely medical services and reduce severe injuries. Different techniques have been developed for fall detection process among elderly population. Recently, the development of Internet of Things (IoT) and artificial intelligence (AI) technologies involving deep learning (DL) and machine learning (ML) approaches can be employed in the field of healthcare for automating the diagnosis procedure of diseased and abnormal cases. This study designs a new Inception with deep convolutional neural network-based fall detection and classification (INDCNN-FDC) model. The presented INDCNN-FDC model intends to categorize the events into two class labels namely fall and not fall. To accomplish this, the INDCNN-FDC model carries out two stages of data pre-processing: Gaussian filter (GF) based image sharpening and Guided Filter (GIF) based image smoothing. In addition, the presented INDCNN-FDC model applies deep transfer learning-based Inception v3 model for generating a helpful group of feature vectors. Finally, DCNN approach receives the feature vectors as input and performs fall detection process. The experimental validation of the INDCNN-FDC approach is performed on benchmark dataset. The comparative study reported the supremacy of the INDCNN-FDC model over state-of-the-art.

关键词： Fall detection Computer vision Transfer learning Convolution neural network Inception v3

来源：评论

学校读者我要写书评

暂无评论

A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9677-9750页

作者： Musa, Nehemiah Gital, Abdulsalam Ya’u Aljojo, Nahla Chiroma, Haruna Adewole, Kayode S. Mojeed, Hammed A. Faruk, Nasir Abdulkarim, Abubakar Emmanuel, Ifada Folawiyo, Yusuf Y. Ogunmodede, James A. Oloyede, Abdukareem A. Olawoyin, Lukman A. Sikiru, Ismaeel A. Katb, Ibrahim Department of Mathematical Sciences Abubakar Tafawa Balewa University Bauchi Nigeria University of Jeddah Jeddah Saudi Arabia Computer Science and Engineering University of Hafr Al-Batin Hafr Saudi Arabia Computer Science and Engineering University of Hafr Al-Batin Hafr Al-Batin Saudi Arabia Department of Computer Science University of Ilorin Ilorin Nigeria Department of Physics Sule Lamido University Kafin Hausa Nigeria Department of Electrical Engineering Ahmadu Bello University Zaria Zaria Nigeria Department of Medicine University of Ilorin Ilorin Nigeria

The success of deep learning over the traditional machine learning techniques in handling artificial intelligence application tasks such as image processing, computer vision, object detection, speech recognition, medical imaging and so on, has made deep learning the buzz word that dominates Artificial Intelligence applications. From the last decade, the applications of deep learning in physiological signals such as electrocardiogram (ECG) have attracted a good number of research. However, previous surveys have not been able to provide a systematic comprehensive review including biometric ECG based systems of the applications of deep learning in ECG with respect to domain of applications. To address this gap, we conducted a systematic literature review on the applications of deep learning in ECG including biometric ECG based systems. The study analyzed systematically, 150 primary studies with evidence of the application of deep learning in ECG. The study shows that the applications of deep learning in ECG have been applied in different domains. We presented a new taxonomy of the domains of application of the deep learning in ECG. The paper also presented discussions on biometric ECG based systems and meta-data analysis of the studies based on the domain, area, task, deep learning models, dataset sources and preprocessing methods. Challenges and potential research opportunities were highlighted to enable novel research. We believe that this study will be useful to both new researchers and expert researchers who are seeking to add knowledge to the already existing body of knowledge in ECG signal processing using deep learning algorithm. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

SAR image Fusion Classification Based on Improved D-S Evidence Theory 8

SAR Image Fusion Classification Based on Improved D-S Eviden...

引用

2024 8th International Conference on machine vision and Information Technology, CMVIT 2024

作者： Liang, Jialu Fang, Hui Zhang, Xinggan School of Electronic Science and Engineering Nanjing University Nanjing China

applications for classifying Synthetic Aperture Radar (SAR) images are critical to environmental monitoring, urban planning, and land resource surveying. Fusion approaches work well for increasing SAR image categorization accuracy. However, effective processing of uncertainty information is frequently overlooked by conventional fusion classification systems. This paper presented a fusion classification approach for SAR images based on improved Dempster-Shafer (D-S) evidence theory, taking into account the uncertainty and possible conflicts in the classifiers' output. First, after adaptively identifying the optimal hyperparameters of models, the SAR data is identified using Gradient Boosting machine (GBM), Multilayer Perceptron (MLP), and Random Forest (RF). Different weights are then allocated in accordance with the variations in classifier performance. Ultimately, enhanced D-S evidence theory is used to integrate the output of these classifiers. In addition, uncertainty in classification results is also modeled and visualized. Experiments with Flevoland and Oberpfaffenhofen datasets show that the accuracy of this method is 85.21% and 91.88% respectively, outperforming both the soft voting ensemble classifier (SVE) and D-S evidence theory fusion method. This approach enhances the comprehension and capacity to manage uncertainty in the model output in addition to increasing the accuracy and reliability of the classification of SAR data. © Published under licence by IOP Publishing Ltd.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Automated waste-sorting and recycling classification using artificial neural network and features fusion: a digital-enabled circular economy vision for smart cities

引用

MULTIMEDIA TOOLS AND applications 2023年第25期82卷 39617-39632页

作者： Mohammed, Mazin Abed Abdulhasan, Mahmood Jamal Kumar, Nallapaneni Manoj Abdulkareem, Karrar Hameed Mostafa, Salama A. Maashi, Mashael S. Khalid, Layth Salman Abdulaali, Hayder Saadoon Chopra, Shauhrat S. Univ Anbar Coll Comp Sci & Informat Technol 11 Ramadi Anbar Iraq Al Ayen Univ Sci Res Ctr Environm Res Grp Thi Qar Iraq City Univ Hong Kong Sch Energy & Environm Kowloon Hong Kong Peoples R China HICCER Hariterde Int Council Circular Econ Res Ctr Digital Circular Econ Palakkad 678631 Kerala India Al Muthanna Univ Coll Agr Samawah 66001 Iraq Univ Warith Al Anbiyaa Coll Engn Karbala Iraq Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Batu Pahat 86400 Johor Malaysia King Saud Univ Coll Comp & Informat Sci Software Engn Dept Riyadh 11451 Saudi Arabia Univ Tun Hussein Onn Malaysia Fac Civil & Environm Engn Batu Pahat 86400 Johor Malaysia Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Architecture Bangi Selangor Malaysia

Waste generation in smart cities is a critical issue, and the interim steps towards its management were not that effective. But at present, the challenge of meeting recycling requirements due to the practical difficulty involved in waste sorting decelerates smart city CE vision. In this paper, a digital model that automatically sorts the generated waste and classifies the type of waste as per the recycling requirements based on an artificial neural network (ANN) and features fusion techniques is proposed. In the proposed model, various features extracted using image processing are combined to develop a sophisticated classifier. Based on the different features, different models are built, and each model produces a single decision. Besides, the kind of class is determined using machine learning. The model is validated by extracting relevant information from the dataset containing 2400 images of possible waste types recycled across three categories. Based on the analysis, it is observed that the proposed model achieved an accuracy of 91.7%, proving its ability to sort and classify the waste as per the recycling requirements automatically. Overall, this analysis suggests that a digital-enabled CE vision could improve the waste sorting services and recycling decisions across the value chain in smart cities.

关键词： Waste management in smart cities Waste images Automated sorting approach Trash recycling classification AI for waste management Circular economy in smart cities

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：