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检索条件"任意字段=2nd Conference on Image Reconstruction from Incomplete Data"

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Enhanced Deep Learning Model for Pneumonia Segmentation in Chest X-ray images 2

Enhanced Deep Learning Model for Pneumonia Segmentation in C...

引用

2nd IEEE International conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Agnes Pearly, A. Karthik, B. Chennai India

ISBN: (纸本)9798331504960

Pneumonia from images of chest X-rays requires accurate segmentation for effective diagnosis and treatment. This study proposes an enhanced deep learning model, EASU-Net, designed to integrate the EfficientNet-B4 backbone, Atrous Spatial Pyramid Pooling (ASPP) and a U-Net decoder with attention mechanisms to refine segmentation accuracy. The methodology preprocesses ChestX-ray14 data, uses the EfficientNet-B4 backbone for capturing hierarchical features, ASPP module to focus on different scales and contexts in the X-ray images and the U-Net decoder, augmented with attention mechanisms to prioritize relevant features and suppress irrelevant areas improving the model's focus on pneumonia-affected regions. Comparative results show that EASU-Net outperforms traditional models like U-Net, Attention U-Net and DeepLabV3 by achieving a Dice Coefficient of 0.93, accuracy of 95%, and Intersection over Union (IoU) of 0.88. The results suggest that EASU-Net is a reliable segmentation tool, providing medical professionals with improved precision for diagnosing pneumonia from chest X-rays. © 2024 IEEE.

关键词： image segmentation

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A design method of power field reconstruction system based on empirical formula 2

A design method of power field reconstruction system based o...

引用

2nd International conference on Signal image Processing and Communication, ICSIPC 2022

作者： Sun, Yong Zhao, Jinqing Lu, Guangzhao Yang, Weiqing Wang, Di Zhang, Jingxiao Guo, Mutian Wei, Lan China Research and Development Academy of Machinery Equipment Beijing100089 China

ISBN: (纸本)9781510657694

This paper presents a design method of power field reconstruction system based on empirical formula, and realizes the software system based on this method. This method includes five parts: warhead structure parametric modeling subsystem, warhead power field calculation and simulation subsystem, warhead power field three-dimensional presentation subsystem, model setting and management subsystem, database management subsystem and so on. There is data exchange between various parts. Based on this method, the power field can be reconstructed. © 2022 SPIE.

关键词： Electronic data interchange

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Design of optical image segmentation system based on KMeans clustering algorithm in smart ranch 2

Design of optical image segmentation system based on KMeans ...

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2nd International conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Ni, Lei Jia, Congsheng Fang, Yuanmeng Tan, Kaiyu Jin, Guan Gong, Bo Zedan Chengdu Technological University Chengdu611700 China

ISBN: (纸本)9781510689053

With the advancement of intelligent information technology, effectively extracting useful optical image data from pastures has become particularly crucial for enhancing pasture management efficiency. This paper proposes an optical image segmentation system utilizing the KMeans clustering algorithm. The system first performs data analysis on the collected pasture optical images to achieve automatic segmentation and recognition of key elements such as livestock, forage, and land. Secondly, the KMeans algorithm segments the processed optical images based on color and texture features, classifying different regions accordingly. Finally, the clustering results are evaluated to effectively identify the distribution of livestock, the growth condition of forage, and potential issues in the land. Experimental results indicate that this system achieves an accuracy rate of 95% for foreground segmentation and 92% for background segmentation, enabling intelligent management and decision-making in pastures. © 2025 SPIE.

关键词： Decision making

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Improved Convolutional Neural Networks by Integrating High-frequency Information for image Classification 23

Improved Convolutional Neural Networks by Integrating High-f...

引用

2nd Asia conference on Algorithms, Computing and Machine Learning (CACML)

作者： Zhuang, Chengyuan Yuan, Xiaohui Guo, Xuan Wei, Zhenchun Xu, Juan Fan, Yuqi Univ North Texas Dept Comp Sci & Engn 3940 N Elm Denton TX 76207 USA Hefei Univ Technol Sch Comp & Informat 193 Tunxi Rd Hefei 230009 Anhui Peoples R China

ISBN: (纸本)9781450399449

Deep convolutional neural networks are powerful and popular tools as deep learning emerges in recent years for image classification in computer vision. However, it is difficult to learn convolutional filters from the examples. The innate frequency property of the data has not been well considered. To address this problem, we find high-frequency information import within deep networks and therefore propose our high-pass attention method (HPA) to help the learning process. HPA explicitly generates high-frequency information via a stage-wise high-pass filter to alleviate the burden of learning such information. Strengthened by channel attention on the concatenated features, our method demonstrates consistent improvements upon ResNet-18/ResNet-50 by 1.36%/1.60% and 1.47%/1.39% on the imageNet-1K dataset and the Food-101 dataset, respectively, as well as the effectiveness over a variety of modules.

关键词： classification deep convolutional neural networks high frequency attention

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Computer Vision Based Bangla Sign Language Recognition Using Transfer Learning 2

Computer Vision Based Bangla Sign Language Recognition Using...

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2nd International conference on data Science and Information System, ICDSIS 2024

作者： Sadik, Md Rezwane Sony, Rayhanul Islam Prova, Nuzhat Noor Islam Mahanandi, Y. Al Maruf, Abdullah Fahim, Sinhad Hossain Islam, Md.Shariful University Of South Dakota VermillionSD57069 United States Trine Univesity AngolaIN46703 United States Pace University Seidenberg School Of Csis One Pace Plaza New York CityNY10038 United States G Pulla Reddy Engineering College Computer Science And Engineering Kurnool India Bangladesh University Of Business & Technology Department Of Computer Science & Engineering Dhaka Mirpur Bangladesh Department Of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350381665

Human societies have relied on communication since ancient times, yet verbal communication poses significant challenges for deaf and hard-of-hearing individuals, necessitating reliance on sign language. Recent advancements, notably through deep learning models, have propelled research in this area. Acknowledging the need for further progress, particularly in minority languages like Bengali, our study aims to develop a method for image-based Bengali sign language detection. We constructed two independent convolutional neural network (CNN) models, InceptionV3 and Xception, leveraging data from diverse sources. Remarkably, the Inception V3 model achieved an accuracy of 97%, while the Xception model surpassed expectations with an accuracy of 99.50%. These results signify substantial progress, demonstrating the efficacy of deep learning architectures, especially the Xception model, in accurately interpreting Bengali sign language. Our study demonstrated how transfer learning, when combined with careful optimization, may yield remarkable outcomes in Bengali sign language recognition, which are further enhanced by data augmentation methods. © 2024 IEEE.

关键词： image classification

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A Novel Feature Attention Network for Single image Dehazing 2

A Novel Feature Attention Network for Single Image Dehazing

引用

2nd International conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2024

作者： Yang, Yue Lao, Fengdan Zhao, Chengtao Yi, Weichao Beijing Foreign Studies University Information Technology Centre of Beijing Foreign Studies University Beijing China China Agriculture University Network Technology Center Beijing China Beijing Institute of Technology Beijing Key Laboratory of Precision Photoelectric Measuring Instrument and Technology School of Optics and Photonics Beijing China

ISBN: (纸本)9798350386639

image dehazing presents a formidable challenge within the domain of fundamental visual processing tasks since haze severely degrades the image quality and hampers the practical application. Therefore, tackling this issue is deemed essential and imperative. A novel feature attention network is presented in a seamless manner, designated as FANet tailored specifically for enhancing single image dehazing, comprises three distinct stages, comprising encoder stage, share stage, as well as decoder stage. With the paramount importance of contextual information in the dehazing process, a new block has been introduced by us, referred to as the contextualized dilated attention residual block (CDARB), serving as the central element in feature extraction. The block utilizes varying scales of dilated convolutions to expand the receptive field and a feature attention module that dynamically adjusts feature responses for both channel and spatial data. Consequently, we introduce a multifaceted loss formulation designed to enhance the performance of our proposed network. Comprehensive experimental assessments have validated that FANet consistently matches or surpasses the efficacy of preceding top-tier methodologies, as demonstrated by a broad range of quantitative and qualitative metrics. © 2024 IEEE.

关键词： Deep learning

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Parametric Sparse Representation Based Technique For 2-D SAR Autofocusing 2

Parametric Sparse Representation Based Technique For 2-D SAR...

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2nd IEEE International conference on Signal, Information and data Processing, ICSIDP 2024

作者： Chen, Yichang Fu, Hao Jia, Hangchuan Sun, Yongjian Zhang, Yuanpeng Zhou, Le Airforce Early Warning Academy Dept. of Early Warning Technology Wuhan China Chinese People's Liberation Army 94028 Unit Xianyang China

ISBN: (纸本)9798331515669

Aiming at the azimuth defocusing problem of SAR image caused by the unstable velocity of the platform, an autofocusing method based on parametric orthogonal matching pursuit (P-OMP) algorithm is proposed in this paper. Firstly, the SAR echo data is processed by range dimension pulse compression and migration correction. Then the range-cell of the strong scattering point is selected according to the energy distribution. Based on P-OMP algorithm, the optimal focusing factor is estimated from the selected range-cell data. With the estimated focusing factor, a well-focused image can be obtained. Experimental results shown the effectiveness of the proposed method. © 2024 IEEE.

关键词： Radar imaging

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Deep learning approaches for analysing visual stimuli from human fMRI 2

Deep learning approaches for analysing visual stimuli from h...

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2nd International Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2022

作者： Karam, Andrew Boles, Kirollos Raouf, Mario Yousef, Mina Atef Khoriba, Ghada Helwan University Computer Science Department Cairo Egypt Ahram Canadian University Artificial Intelligence Department Cairo Egypt

ISBN: (纸本)9781665466776

Decoding brain activities corresponding to an external stimulus is an excellent challenge because of the complexity of brain activities data and the understood of the activity of the brain is not yet complete. This research paper focuses on the functional magnetic resonance imaging (fMRI) data of different people corresponding to external visual stimulus images. The images consist of three main types: Letters, artificial shapes, and natural images. The proposed model provides an analysis and classification of the three main types of images using Support Vector Machine and Convolutional Neural Networks by proving that the brain is affected differently by each type. In addition, the identification of 200 natural image categories was analyzed by calculating the similarity between the features for each category by using the features of visual images using CNN and fMRI using the regression model. Also, we used deep convolution generative adversarial networks (DCGANs) for image reconstruction. © 2022 IEEE.

关键词： Support vector machines Visualization Analytical models Functional magnetic resonance imaging Predictive models Brain modeling data models

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3D multi views reconstruction of flame surface based on deep learning 2

3D multi views reconstruction of flame surface based on deep...

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2023 2nd International Symposium on Intelligent Unmanned Systems and Artificial Intelligence, SIUSAI 2023

作者： Song, Lingao Liu, Tao Jiang, Dong Li, Huadong Zhao, Dongmei Zou, Qingfeng School of Computer Science and Technology Southwest University of Science and Technology Sichuan Mianyang621010 China AECC Sichuan Gas Turbine Establishment Sichuan Mianyang621703 China

A deep learning network based on MVSNet multi-view 3D reconstruction network (IM-MVSNet) is proposed in this paper for the reconstruction of the surface of laminar flame, which could suppress the influence of background noise on the reconstruction data when reconstructing the surface of laminar flame and improve the reconstruction accuracy of the flame surface at the same time. The network obtains high-quality segmented images by image segmentation of the reference frames and neighboring frames of the input sampled images to remove the background noise during the time of sampling, then reconstructs the multi-view images in 3D to build a 3D point cloud of the laminar flame surface, finally obtains the reconstructed laminar flame surface. The comparison of computational results using different reconstruction models shows that the 3D reconstruction network proposed in this paper could effectively reduce the point cloud noise of the reconstructed flame surface, improve the reconstruction accuracy of the flame surface, and provide a novel technical means for combustion research. © Published under licence by IOP Publishing Ltd.

关键词： image segmentation

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Application of Synthetic Aperture Radar image processing in magnetic susceptibility inversion 2

Application of Synthetic Aperture Radar image processing in ...

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2nd International conference on Consumer Electronics and Computer Engineering, ICCECE 2022

作者： Feng, Yuze Guo, Senmiao Yang, Changbao Ma, Boxuan College of Geoexploration Science and Technology Jilin University Changchun China

ISBN: (纸本)9781665408868

Few people use remote sensing computer image processing technology to explore the magnetic susceptibility of large areas. As an important parameter of geological and environmental evolution, magnetic susceptibility is widely used in loess, rivers, lakes, marine sedimentary facies, paleoenvironment reconstruction and so on. In the experiment, the microwave radar image is processed by computer image processing to obtain the backscattering coefficient of the image. The results are obtained by Dubois model to invert the dielectric constant of the ground objects in the Xingcheng study area, the results of the fitting equation are then used to invert the magnetic susceptibility of rocks in the studied area. The results show that the magnetic susceptibility of different lithology is different, the value is influenced by lithology and minerals, and metamorphic rock is a little larger than sedimentary rock only considering lithology. The experiment provides a possibility to obtain the regional geomagnetic magnetic susceptibility using microwave radar data. © 2022 IEEE.

关键词： Lithology

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