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Transformer-based heart organ segmentation using a novel axial attention and fusion mechanism 基于变压器的心脏器官分割,采用新颖的轴向关注和融合机制
Pub Date : 2023-04-22 DOI: 10.1080/13682199.2023.2198394
Addae Emmanuel Addo, Kashala Kabe Gedeon, Zhe Liu
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引用次数: 0
Automated Mango Leaf Infection Classification using Weighted and Deep Features with Optimized Recurrent Neural Network Concept 基于优化递归神经网络概念的加权深度特征芒果叶片感染自动分类
Pub Date : 2023-04-20 DOI: 10.1080/13682199.2023.2204036
A. Selvakumar, A. Balasundaram
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引用次数: 3
Phase image-guided adaptive rotation-invariant feature point detector 相位图像引导自适应旋转不变特征点检测器
Pub Date : 2023-04-19 DOI: 10.1080/13682199.2023.2202088
Ahmed S. Mashaly, T. Mahmoud
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引用次数: 0
Facial emotion detection using thermal and visual images based on deep learning techniques 基于深度学习技术的热图像和视觉图像面部情绪检测
Pub Date : 2023-04-18 DOI: 10.1080/13682199.2023.2199504
R. Rashmi, U. Snekhalatha, Anela L. Salvador, A. Raj
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引用次数: 0
Energy based denoising convolutional neural network for image enhancement 基于能量去噪的卷积神经网络图像增强
Pub Date : 2023-04-17 DOI: 10.1080/13682199.2023.2198350
V. Karthikeyan, E. Raja, D. Pradeep
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引用次数: 1
Multi-mode dictionaries for fast CS-based dynamic MRI reconstruction 基于cs的MRI快速动态重建多模式字典
Pub Date : 2023-04-12 DOI: 10.1080/13682199.2023.2198347
Minha Mubarak, Thomas James Thomas, Sheeba Rani J, Deepak Mishra
{"title":"Multi-mode dictionaries for fast CS-based dynamic MRI reconstruction","authors":"Minha Mubarak, Thomas James Thomas, Sheeba Rani J, Deepak Mishra","doi":"10.1080/13682199.2023.2198347","DOIUrl":"https://doi.org/10.1080/13682199.2023.2198347","url":null,"abstract":"","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78226060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Hybrid deep autoencoder network based adaptive cross guided bilateral filter for motion artifacts correction and denoising from MRI 基于混合深度自编码器网络的自适应交叉引导双边滤波器用于MRI运动伪影校正和去噪
Pub Date : 2023-04-11 DOI: 10.1080/13682199.2023.2196494
Shiju Samuel, R. S. Ochawar, M. Rukmini
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引用次数: 0
Super pixels transmission map-based object detection using deep neural network in UAV video 无人机视频中基于超像素传输图的深度神经网络目标检测
Pub Date : 2023-04-09 DOI: 10.1080/13682199.2023.2195121
J. Evangelin, Deva Sheela, P. Arockia, J. Rani, M. A. Paul
ABSTRACT Object detection has become a very prominent subject for research in recent times. This study's main goal is to suggest a technique for video saliency object detection. It seems to sense that using the depth information in photos to detect salient things. Since depth offers abundant information about scene structure, object forms, and other 3D cues. This information is very compatible to distinguish between objects in the foreground and background. As a result of the high object density, small object size, and cluttered background, aerial photos and movies provide results with low precision. In this paper, the proposed SPTM (Super Pixel Transmission Map)-YOLO model, the input RGB image has applied Dark Channel Prior (DCP) method for estimating the transmission map. From the transmission map only, the background probability is estimated with the help of SLIC (simple linear iterative clustering algorithm) superpixel segmentation. That foreground extracted image is further learned with YOLO architecture to detect the objects effectively. For object detection in aerial images, this proposed SPTM-YOLO approach outperforms classic YOLO by up to 6% accuracy. Accurate detection of things that are small in size, partially occluded, and out of view is possible.
摘要:目标检测是近年来研究的一个非常突出的课题。本研究的主要目的是提出一种视频显著性目标检测技术。利用照片中的深度信息来发现突出的东西似乎是有意义的。因为深度提供了关于场景结构、对象形式和其他3D线索的丰富信息。这个信息非常兼容,可以区分前景和背景中的物体。由于物体密度高,物体尺寸小,背景杂乱,航空照片和电影提供的结果精度较低。本文提出了SPTM (Super Pixel Transmission Map)-YOLO模型,输入RGB图像采用暗通道先验(Dark Channel Prior, DCP)方法估计传输图。仅从传输图出发,借助SLIC(简单线性迭代聚类算法)超像素分割估计背景概率。利用YOLO架构对提取的前景图像进行进一步学习,有效检测目标。对于航空图像中的目标检测,本文提出的SPTM-YOLO方法比经典的YOLO方法准确率高出6%。精确地探测小的、部分遮挡的、在视线之外的物体是可能的。
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引用次数: 0
Adaptive multi-predictor based reversible data hiding with superpixel irregular block sorting and optimization 基于超像素不规则块排序和优化的自适应多预测器可逆数据隐藏
Pub Date : 2023-04-08 DOI: 10.1080/13682199.2023.2195090
Hui Shi, Baoyue Hu, Yanli Li, Jianing Geng, Yonggong Ren
ABSTRACT Reversible data hiding (RDH) is a special class of steganography that is capable of recovering the original cover image upon the extraction of the secret data. The main goal of this paper is to develop different adaptive predictors based on superpixel irregular block sorting. Firstly, a superpixel irregular block and sorting strategy is proposed which is applied to histogram shifting for the first time. Then, a multi-directional edge classification method is proposed, which divides pixels into strong edge pixels, normal edge pixels, and weak edge pixels. Moreover, strong edge pixels and normal edge pixels are further divided into four directions. According to edge classification, the most appropriate adaptive multi-predictor is proposed. Finally, an optimization-based data hiding strategy is proposed. The proposed scheme focuses on constructing a sharp enough histogram. The investigational results demonstrate that the proposed scheme achieves large capacity, high image quality, and low complexity.
可逆数据隐藏(RDH)是一种特殊的隐写技术,能够在提取秘密数据后恢复原始封面图像。本文的主要目标是开发基于超像素不规则块排序的不同自适应预测器。首先,提出了一种超像素不规则分块和排序策略,并首次应用于直方图移位;然后,提出了一种多向边缘分类方法,将像素分为强边缘像素、正常边缘像素和弱边缘像素。并将强边缘像素和法向边缘像素进一步划分为四个方向。根据边缘分类,提出了最合适的自适应多预测器。最后提出了一种基于优化的数据隐藏策略。该方案的重点是构建一个足够清晰的直方图。实验结果表明,该方案具有容量大、图像质量高、复杂度低等优点。
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引用次数: 0
A secure and efficient authentication and multimedia data sharing approach in IoT-healthcare 物联网医疗中安全高效的身份验证和多媒体数据共享方法
Pub Date : 2023-04-02 DOI: 10.1080/13682199.2023.2180140
Sangeetha Yempally, S. K. Singh, Velliangiri Sarveshwaran
ABSTRACT Currently, healthcare services are encountering challenges, particularly in developing countries wherein remote areas encounter a lack of highly developed hospitals and doctors. IoT devices produce enormous security-sensitive data; therefore, device security is considered an important concept. The main aim of this work is to formulate a secure key generation process in the data-sharing approach by exploiting the Rider Horse Herd Optimization Algorithm (RHHO). Here, eight phases, like the initialization phase, registration phase, key generation phase, login phase, data protection phase, authentication phase, verification phase, and data decryption phase are exploited for secure and efficient authentication and multimedia data sharing. The proposed RHHO model is the integration of the Rider Optimization Algorithm (ROA) and Horse herd Optimization Algorithm (HOA). The proposed RHHO model achieved enhanced performance with a computation cost of 0.235, an accuracy of 0.935and memory usage of 2.425 MB.
目前,医疗保健服务正面临挑战,特别是在发展中国家,其中偏远地区遇到缺乏高度发达的医院和医生。物联网设备产生大量安全敏感数据;因此,设备安全是一个重要的概念。这项工作的主要目的是通过利用骑手马群优化算法(RHHO),在数据共享方法中制定一个安全的密钥生成过程。通过初始化阶段、注册阶段、密钥生成阶段、登录阶段、数据保护阶段、身份验证阶段、验证阶段、数据解密阶段等8个阶段,实现安全高效的身份验证和多媒体数据共享。提出的RHHO模型是骑手优化算法(ROA)和马群优化算法(HOA)的集成。所提出的RHHO模型的计算成本为0.235,精度为0.935,内存使用为2.425 MB,性能得到了提高。
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引用次数: 0
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The Imaging Science Journal
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