检索结果-内蒙古大学图书馆

ECGConVT: A Hybrid CNN and vision Transformer Model for Enhanced 12-Lead ECG images Classification

IEEE ACCESS 2024年 12卷 193043-193056页

作者： Khalid, Mudassar Pluempitiwiriyawej, Charnchai Abdulkadhem, Abdulkadhem A. Afzal, Imran Truong, Tien Chulalongkorn Univ Dept Elect Engn Bangkok 10330 Thailand Al Mustaqbal Univ Coll Sci Dept Cyber Secur Babylon 51001 Hillah Iraq Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Univ Calif Berkeley Sch Econ & Cognit Sci Berkeley CA 94720 USA

Cardiovascular diseases, which are currently the major causes of death globally, can be largely ameliorated through early detection and categorization. Electrocardiogram (ECG) tests have emerged as widely employed, low-cost and non-invasive procedures for evaluating electrical activities of the heart and diagnosing cardiovascular ailments. In this research, by using deep learning techniques to detect specific cardiac disorders like cardiac myocardial infarction(MI), arrhythmia, past history of myocardial infarction(PMI) and normal ECG patterns on a dataset containing patients with heart disease. We propose ECGConVT framework that combines Convolutional Neural Network (CNN) module for extracting local features, and vision Transformer (ViT) module for capturing global features. The final classification is achieved by combining the two using Multilayer Perceptron (MLP) module. The experimental results indicate promise of ECGConVT in ECG image classification where it outperforms other approaches showing an average accuracy of 98.5%, F1-score: 98.7%, Recall: 98.8% and Precision: 98.5%. In order to meet the practical needs of clinical applications, we implemented a lightweight post-processing step to reduce the size of the model.

关键词： Electrocardiography Accuracy Heart Feature extraction Convolutional neural networks Arrhythmia Transformers Deep learning Cardiovascular diseases Convolution ECG images classification electrocardiogram machine learning vision transformer

来源：评论

学校读者我要写书评

暂无评论

COMPUTER vision ALGORITHM DESIGN IN image processing BASED ON PROJECTIVE GEOMETRY

COMPUTER VISION ALGORITHM DESIGN IN IMAGE PROCESSING BASED O...

引用

作者： Kang, Y.G. Di Zhao School of Electrical and Information Engineering Hunan Institute of Engineering Hunan Xiangtan411104 China

image processing with computer vision, particularly in the realm of projective geometry, offers remarkable potential for various applications. Through the lens of projective geometry, images can be transformed, augmented, and reconstructed with precision, facilitating tasks such as image rectification, 3D reconstruction, and object tracking. Landmark estimation in computer vision is a vital task with broad applications across various domains. This process involves identifying key points or landmarks within images, enabling tasks such as facial recognition, object tracking, and gesture recognition. This paper, proposed a novel approach for landmark estimation in computer vision using Projective Geometry Landmark Estimation (PGLM). The proposed model aims to estimate the landmark features by a projective geometry model. With the estimation of the geometry features landmarks related to the facial, object, and medical images are computed. The PGLM model uses the point features for the location of the landmark features. In order to compare PGLM’s performance to that of more conventional classification methods like Random Forest, K-Nearest Neighbors (KNN), and Support Vector machine (SVM), simulation analysis is carried out. From what we can see, PGLM routinely beats these alternatives when we compare their accuracy, precision, recall, and F1 score. The findings stated the effectiveness of PGLM as a promising approach for landmark estimation in image processing tasks, paving the way for further advancements in this domain. ©2024: The Royal Institution of Naval Architects.

关键词： Nearest neighbor search

来源：评论

学校读者我要写书评

暂无评论

State of Art IoT and Edge Embedded Systems for Real-Time machine vision applications

引用

IEEE ACCESS 2022年 10卷 58287-58301页

作者： Meribout, Mahmoud Baobaid, Asma Khaoua, Mohammed Ould Tiwari, Varun Kumar Pena, Juan Pablo Khalifa Univ Coll Engn Dept Elect Engn & Comp Sci Abu Dhabi U Arab Emirates Univ Blida 1 Dept Informat Blida Algeria

IoT and edge devices dedicated to run machine vision algorithms are usually few years lagging currently available state-of-the-art technologies for hardware accelerators. This is mainly due to the non-negligible time delay required to implement and assess related algorithms. Among possible hardware platforms which are potentially being explored to handle real-time machine vision tasks, multi-core CPU and Graphical processing Unit (GPU) platforms remain the most widely used ones over Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC)-based platforms. This is mainly due to the availability of powerful and user friendly software development tools, in addition to their lower cost, and obviously their high computation power with reasonable form factor and power consumption. Nevertheless, the trend now is towards a System-On-Chip (SOC) processors which combine ASIC/FPGA accelerators with GPU/multicore CPUs. This paper presents different state of the art IoT and edge machine vision technologies along with their performance and limitations. It can be a good reference for researchers involved in designing state of the art IoT embedded systems for machine vision applications.

关键词： machine vision Internet of Things Streaming media Task analysis image edge detection Field programmable gate arrays Real-time systems IoT edge machine vision systems multicore CPU GPU FPGA ASIC

来源：评论

学校读者我要写书评

暂无评论

CNN-based On-board Intelligent processing for Remote Sensing images Interpretation 2

CNN-based On-board Intelligent Processing for Remote Sensing...

引用

2nd International Conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Cui, Xiaojie Liu, Yuanyuan Chen, Xuehua Wang, Gang Beijing Institute of Remote Sensing Information Beijing China

ISBN: (纸本)9798350331417

Traditional remote sensing image processing is not able to provide timely information for near real-time applications due to the hysteresis of satellite-ground mutual communication and low processing efficiency. On-board intelligent processing is an important approach to improve the efficiency and intelligence of remote sensing satellites. This paper takes convolutional neural network (CNN) based on-board processing as the focus. Firstly, the basic workflow of CNN based on-board processing system is illustrated. Afterwards, the applications of lightweight CNN based on-board processing are thoroughly reviewed. The used CNN models are further analyzed to compare the advantages and disadvantages. Finally, current challenges are summarized and future works concerned with artificial intelligence are concluded. © 2023 IEEE.

关键词： change detection cloud detection earth observation satellites object detection on-board intelligent processing

来源：评论

学校读者我要写书评

暂无评论

Single image Dehazing Using CNN 2nd

Single Image Dehazing Using CNN

引用

2nd International Conference on Computational Intelligence in machine Learning, ICCIML 2022

作者： Bhadane, Samarth Bidwe, Ranjeet Vasant Zope, Bhushan Pune Institute of Computer Technology Maharashtra Pune India Maharashtra Lavale Pune India

ISBN: (纸本)9789819979530

Particulate matter in the atmosphere obscures the visibility of the atmosphere, causing a condition known as haze. Other natural phenomena like mist, fog, and dust also obscure the vision;this is because of scattering of light which attenuates the light intensity. All these instances are responsible for the degradation of image quality. Hazy images are problematic because these images cannot be used for computer vision and image processing applications like pattern and object recognition. Dehazing images improve the clarity and contrast of the images making them more suitable for computer vision and image processing. This paper presents a method of dehazing images using CNN. The proposed model is trained on D-HAZY (Ancuti et al. in 2016 IEEE international conference on image processing (ICIP), 2016) and SOTS (Li et al. in IEEE Trans image Process 28:492–505, 2019) datasets which contain a mix of natural and synthesized hazy images. To assess the model’s performance, we employ PSNR and SSIM metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

Optimizing zero-shot text-based segmentation of remote sensing imagery using SAM and Grounding DINO

ARTIFICIAL INTELLIGENCE IN GEOSCIENCES

引用

ARTIFICIAL INTELLIGENCE IN GEOSCIENCES 2025年第1期6卷

作者： Diab, Mohanad Kolokoussis, Polychronis Brovelli, Maria Antonia Politecn Milan Dept Civil & Environm Engn I-20133 Milan Italy Natl Tech Univ Athens Sch Rural Surveying & Geoinformat Engn Athens 15780 Greece

The use of AI technologies in remote sensing (RS) tasks has been the focus of many individuals in both the professional and academic domains. Having more accessible interfaces and tools that allow people of little or no experience to intuitively interact with RS data of multiple formats is a potential provided by this integration. However, the use of AI and AI agents to help automate RS-related tasks is still in its infancy stage, with some frameworks and interfaces built on top of well-known vision language models (VLM) such as GPT-4, segment anything model (SAM), and grounding DINO. These tools do promise and draw guidelines on the potentials and limitations of existing solutions concerning the use of said models. In this work, the state of the art AI foundation models (FM) are reviewed and used in a multi-modal manner to ingest RS imagery input and perform zero-shot object detection using natural language. The natural language input is then used to define the classes or labels the model should look for, then, both inputs are fed to the pipeline. The pipeline presented in this work makes up for the shortcomings of the general knowledge FMs by stacking pre-processing and post-processing applications on top of the FMs;these applications include tiling to produce uniform patches of the original image for faster detection, outlier rejection of redundant bounding boxes using statistical and machine learning methods. The pipeline was tested with UAV, aerial and satellite images taken over multiple areas. The accuracy for the semantic segmentation showed improvement from the original 64% to approximately 80%-99% by utilizing the pipeline and techniques proposed in this work. GitHub Repository: MohanadDiab/LangRS.

关键词： Foundation models Multi-modal models vision language models Semantic segmentation Segment anything model Earth observation Remote sensing

来源：评论

学校读者我要写书评

暂无评论

MoistNet: machine vision-based deep learning models for wood chip moisture content measurement

引用

EXPERT SYSTEMS WITH applications 2025年 259卷

作者： Rahman, Abdur Street, Jason Wooten, James Marufuzzaman, Mohammad Gude, Veera G. Buchanan, Randy Wang, Haifeng Mississippi State Univ Dept Ind & Syst Engn Mississippi State MS 39762 USA Mississippi State Univ Dept Sustainable Bioprod Mississippi State MS 39762 USA Mississippi State Univ Dept Agr & Biol Engn Mississippi State MS 39762 USA Purdue Univ Northwest Purdue Univ Northwest Water Inst PWI Hammond IN 46323 USA US Army Engineer Res & Dev Ctr 3909 Halls Ferry Rd Vicksburg MS 39180 USA

Quick and reliable measurement of wood chip moisture content is an everlasting problem for numerous forest-reliant industries such as biofuel, pulp and paper, and bio-refineries. Moisture content is a critical attribute of wood chips due to its direct relationship with the final product quality. Conventional techniques for determining moisture content, such as oven-drying, possess some drawbacks in terms of their time-consuming nature, potential sample damage, and lack of real-time feasibility. Furthermore, alternative techniques, including NIR spectroscopy, electrical capacitance, X-rays, and microwaves, have demonstrated potential;nevertheless, they are still constrained by issues related to portability, precision, and the expense of the required equipment. Hence, there is a need for a moisture content determination method that is instant, portable, non-destructive, inexpensive, and precise. This study explores the use of deep learning and machine vision to predict moisture content classes from RGB images of wood chips. A large-scale image dataset comprising 1,600 RGB images of wood chips has been collected and annotated with ground truth labels, utilizing the results of the oven-drying technique. Two high-performing neural networks, MoistNetLite and MoistNetMax, have been developed leveraging Neural Architecture Search (NAS) and hyperparameter optimization. The developed models are evaluated and compared with state-of-the-art deep learning models. Results demonstrate that MoistNetLite achieves 87% accuracy with minimal computational overhead, while MoistNetMax exhibits exceptional precision with a 91% accuracy in wood chip moisture content class prediction. With improved accuracy (9.6% improvement in accuracy by MoistNetMax compared to the best baseline model ResNet152V2) and faster prediction speed (MoistNetLite being twice as fast as MobileNet), our proposed MoistNet models hold great promise for the wood chip processing industry to be efficiently deployed on p

关键词： Wood chip Moisture content Deep learning machine vision Neural architecture search Hyperparameter optimization

来源：评论

学校读者我要写书评

暂无评论

Local Geometric Indexing of High Resolution Data for Facial Reconstruction From Sparse Markers

引用

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024年第8期30卷 5289-5298页

作者： Cong, Matthew Lan, Lana Fedkiw, Ronald Ind Light & Mag San Francisco CA 94129 USA Stanford Univ Dept Comp Sci Stanford CA 94305 USA

When considering sparse motion capture marker data, one typically struggles to balance its overfitting via a high dimensional blendshape system versus underfitting caused by smoothness constraints. With the current trend towards using more and more data, our aim is not to fit the motion capture markers with a parameterized (blendshape) model or to smoothly interpolate a surface through the marker positions, but rather to find an instance in the high resolution dataset that contains local geometry to fit each marker. Just as is true for typical machine learning applications, this approach benefits from a plethora of data, and thus we also consider augmenting the dataset via specially designed physical simulations that target the high resolution dataset such that the simulation output lies on the same so-called manifold as the data targeted.

关键词： Shape Faces Geometry Surface reconstruction Cameras Point cloud compression Deformation Computer graphics image processing and computer vision interpolation

来源：评论

学校读者我要写书评

暂无评论

CONTROLLABLE UNIVERSAL EDGE-PRESERVING image FILTERING 7

CONTROLLABLE UNIVERSAL EDGE-PRESERVING IMAGE FILTERING

引用

7th IEEE International Conference on Multimedia Information processing and Retrieval (MIPR)

作者： Liang, Shijun Fu, Dongdong Michigan State Univ Dept Biomed Engn E Lansing MI 48824 USA Dolby Labs Inc Sunnyvale CA USA

ISBN: (纸本)9798350351439;9798350351422

In this study, we investigate the Deep image Prior (DIP) in enhancing image smoothing, a crucial component in numerous computer vision and graphics applications. Although deep learning has demonstrated remarkable achievements in these domains, it often falls short in flexibility and controllability, in contrast to traditional methods, which are more adaptable and typically exhibit subpar performance. Notably, some end-to-end deep learning models offer control over edge preservation, yet their performance remains marginally suboptimal. To address this shortcoming, we introduce an innovative network architecture that diverges from the traditional U-Net model, featuring a Laplacian pyramid as the encoder and a deep decoder as the decoding component, integrated with a bilateral filter loss to improve DIP. This design aids the network in rapidly assimilating essential low-frequency information. Our approach excels in retaining texture details, significantly improving image smoothing and related tasks beyond the capabilities of standard DIP methods. Moreover, our technique outperforms the leading unsupervised method, pyramid texture filtering, in texture filtering tasks and other applications.

关键词： image smoothing machine learning deep learning Deep image prior

来源：评论

学校读者我要写书评

暂无评论

A deep journey into image enhancement: A survey of current and emerging trends

引用

INFORMATION FUSION 2023年第1期93卷 36-76页

作者： Lepcha, Dawa Chyophel Goyal, Bhawna Dogra, Ayush Sharma, Kanta Prasad Gupta, Deena Nath Chandigarh Univ Dept ECE Mohali 140413 Punjab India Ronin Inst Montclair NJ 07043 USA GLA Univ Inst Engn & Technol Mathura India C DAC Mumbai Mumbai India

image captured under poor-illumination conditions often display attributes of having poor contrasts, low brightness, a narrow gray range, colour distortions and considerable interference, which seriously affect the qualitative visual effects on human eyes and severely restrict the efficiency of several machine vision systems. In addition, underwater images often suffer from colour shift and contrast degradation because of an absorption and scattering of light while travelling in water. These unpleasant effects limits visibility, reduce contrast and even generate colour casts that limits the use of underwater images and videos in marine archaeology and biology. In medical imaging applications, medical images are important tools for detecting and diagnosing several medical conditions and ailments. However, the quality of medical images can often be degraded during image acquisition due to factors such as noise interference, artefacts, and poor illumination. This may lead to the misdiagnosis of medical conditions, which can further aggravate life threatening situations. image enhancement is one of the most important technologies in the field of image processing, and its purpose is to improve the quality of images for specific applications. In general, the basic principle of image enhancement is to improve the quality and visual interpretability of an image so that it is more suitable for the specific applications and the observers. Over the last few decades, numerous image enhancement techniques have been proposed in the literature This study covers a systematic survey on existing state-of-the-art image enhancement techniques into broad classification of their algorithms. In addition, this paper summarises the datasets utilised in the literature for performing the experiments. Furthermore, an attention has been drawn towards several evaluation parameters for quantitative evaluation and compared different state-of-the-art algorithms for performance analysis on benchmark

关键词： Review image enhancement Fuzzy theory Retinex theory Deep learning Convolutional neural networks (CNNs) Generative adversarial networks (GANs) applications Quality assessment criteria Survey

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：