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Border Collie Shuffled Shepherd optimization-based image reconstruction using visual cryptography 基于视觉密码优化的边境牧羊犬洗牌牧羊人图像重建
Pub Date : 2023-10-25 DOI: 10.1080/13682199.2023.2266595
Sajitha A S, S. Sridevi Sathya Priya, Sanish V S
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引用次数: 0
Greyscale correction algorithm of aerial filter array multispectral image 航空滤波阵列多光谱图像的灰度校正算法
Pub Date : 2023-10-18 DOI: 10.1080/13682199.2023.2263278
Tong Shao Li, Wen Bang Sun, Xin Wei Bai, Di Wu, Zhen Hai Chen, Jia Yu Zhang
ABSTRACTFilter array multispectral cameras are influenced by imaging mechanism and process characteristics, spliced images have edge interference fringes and greyscale differences. Aiming at the problems of inconsistent greyscale of filter array multispectral camera, a new method of greyscale correction algorithm is proposed in this paper. First, the mechanism and adjustment principle of edge interference streaks and stripe greyscale difference are thoroughly analyzed; Second, the greyscale of interference area is adjusted by using greyscale of adjacent image non-interference area; Third, the greyscale of whole image is adjusted by using proportional relationship between the adjacent overlap area greyscale; Finally, sequence images are spliced to obtain single-band image with same greyscale. Theoretical analysis and experimental results show that this method can not only effectively solve the problem of inconsistent greyscale due to the influence of imaging mechanism and process characteristics, but also can maximally preserve spectral information characteristics in different wavelength bands.KEYWORDS: Filter arraymultispectral imagegreyscale correctionedge interference streakgreyscale differenceimage stitchingsingle bandgreyscale consistency Additional informationNotes on contributorsTong Shao LiTong Shao Li, born in 1998, male, Master's Degree. Research Interests: Digital Image Processing.Wen Bang SunWen Bang Sun, born in 1976, male, PhD Degree, associate professor. Research interests: digital image processing and information security theory.Xin Wei BaiXin Wei Bai, female. Research interests: digital image processing.Di WuDi Wu, female. Research interests: digital image processing.Zhen Hai ChenZhen Hai Chen, male. Research direction: digital image processing.Jia Yu ZhangJia Yu Zhang, male. Research interests: digital image processing.
摘要滤波阵列多光谱相机受成像机理和工艺特性的影响,拼接后的图像存在边缘干涉条纹和灰度差异。针对滤波阵列多光谱相机灰度不一致的问题,提出了一种新的灰度校正算法。首先,深入分析了边缘干涉条纹和条纹灰度差的产生机理和调节原理;其次,利用相邻图像非干涉区域的灰度调整干涉区域的灰度;第三,利用相邻重叠区域灰度的比例关系调整整幅图像的灰度;最后,对序列图像进行拼接,得到具有相同灰度的单波段图像。理论分析和实验结果表明,该方法既能有效解决成像机理和工艺特性影响下的灰度不一致问题,又能最大限度地保留不同波长波段的光谱信息特征。关键词:滤波阵列多光谱图像灰度校正边缘干涉条纹灰度差图像拼接单波段灰度一致性附加信息作者说明邵立同邵立,1998年生,男,硕士学位。主要研究方向:数字图像处理。孙文邦,1976年生,男,博士,副教授。主要研究方向:数字图像处理与信息安全理论。白新薇,女。主要研究方向:数字图像处理。迪·吴,女。主要研究方向:数字图像处理。陈振海陈振海,男。研究方向:数字图像处理。张佳玉,张佳玉,男。主要研究方向:数字图像处理。
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引用次数: 0
A low-light image enhancement method based on HSV space 一种基于HSV空间的微光图像增强方法
Pub Date : 2023-10-10 DOI: 10.1080/13682199.2023.2266308
Libing Zhou, Xiaojing Chen, Baisong Ye, Xueli Jiang, Sheng Zou, Liang Ji, Zhengqian Yu, Jianjian Wei, Yexin Zhao, Tianyu Wang
ABSTRACTTo enhance the visual performance of low-illumination images, many low-illumination images are analyzed. Based on this, a low-light image enhancement method based on HSV space and semi-implicit ROF model is proposed. First, the low-illumination image is decomposed into HSV space for saturation denoising and brightness enhancement. Then, the Bayesian rules are applied to fuse the saturation and value. The three components in HSV space are converted to the RGB space and obtain a rough enhanced image. Finally, the semi-implicit ROF model is introduced to denoise the global noise and obtain the enhanced image. Such a comprehensive method can improve the low illumination image more clearly. The experimental results show that the algorithm has a PSNR score of 26.48, 6.29, 0.8947, and 28.4124, and the PSNR score is the highest in the comparison algorithm. The experiments on the Low-Light image data set also show that the proposed method can effectively improve the visibility of low-light images, and can provide a simple and effective method for low-light image enhancement.KEYWORDS: Image enhancementDeep learningHSV color spaceBayesian ruleROFGaussian noiseStructure SimilarityPeak Signal Noise Ratio Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsLibing ZhouLibing Zhou received master's degree from Hefei University of Technology, Hefei, China. Now he works at Tiandi(Changzhou) Automation Co., Ltd. His current research interests include mine electromechanical system intelligent, intelligent detection and control.Xiaojing ChenXiaojing Chen is an associate research fellow. Now he works at Tiandi(Changzhou) Automation Co., Ltd. His current research interests include coal mine industrial control, Internet of Things and intelligent technology.Baisong YeBaisong Ye received PhD degree from the University of Science and Technology of China, Hefei, China, in 2013. Now he works at Tiandi(Changzhou) Automation Co., Ltd. His current research interests includecoal mine photoelectric detection system and intelligent application technology.Xueli JiangXueli Jiang received PhD degreefrom the University of Science and Technology of China, Hefei,China, in 2021. Now he works at Tiandi(Changzhou) Automation Co., Ltd. His current research focuses on motor control.Sheng ZouZhengqian Yu received master's degree from the Stevens Institute of Technology, New Jersey,USA, in 2020. Now he works at Tiandi(Changzhou) Automation Co., Ltd. His current research interests include positioning, object detection and obstacle perception.Liang JiJianjian Wei received master's degree from Xi'an University of Science and Technology, Xi'an,China, in 2022. Now he works at Tiandi(Changzhou) Automation Co., Ltd. His current research interests include positioning, object detection.Zhengqian YuSheng Zou received master's degree from University of South China, Hengyang,China, in 2019. Now he works at Tiandi(Changzhou)
摘要为了提高低照度图像的视觉性能,对大量低照度图像进行了分析。在此基础上,提出了一种基于HSV空间和半隐式ROF模型的弱光图像增强方法。首先,将低照度图像分解到HSV空间进行饱和度去噪和亮度增强;然后,应用贝叶斯规则对饱和度和值进行融合。将HSV空间中的三个分量转换为RGB空间,得到粗糙增强图像。最后,引入半隐式ROF模型对全局噪声进行去噪,得到增强图像。这种综合的方法可以提高低照度图像的清晰度。实验结果表明,该算法的PSNR得分分别为26.48、6.29、0.8947和28.4124,在比较算法中PSNR得分最高。在低光图像数据集上的实验也表明,该方法可以有效地提高低光图像的可见性,为低光图像增强提供了一种简单有效的方法。关键词:图像增强,深度学习,hsv颜色空间,贝叶斯规则,高斯噪声,结构相似性,峰值信噪比披露声明,作者未报告潜在的利益冲突。周立冰,硕士,毕业于中国合肥工业大学。现就职于天地(常州)自动化有限公司。主要研究方向为矿山机电系统智能化、智能检测与控制。陈晓晶,副研究员。现就职于天地(常州)自动化有限公司。目前主要研究方向为煤矿工业控制、物联网与智能技术。叶白松,2013年毕业于中国科学技术大学,获博士学位。现就职于天地(常州)自动化有限公司。主要研究方向为煤矿光电探测系统及智能应用技术。姜雪莉,博士,2021年毕业于中国科学技术大学合肥分校。现就职于天地(常州)自动化有限公司。他目前的研究重点是运动控制。余胜正谦,2020年毕业于美国新泽西州史蒂文斯理工学院,获硕士学位。现就职于天地(常州)自动化有限公司。他目前的研究兴趣包括定位、目标检测和障碍物感知。梁继健,魏建建,2022年毕业于中国西安科技大学,获硕士学位。现就职于天地(常州)自动化有限公司。他目前的研究兴趣包括定位,目标检测。邹正谦,2019年毕业于中国衡阳华南大学,获硕士学位。现就职于天地(常州)自动化有限公司。主要研究方向为煤矿视觉图像处理。纪卫亮,2019年毕业于中国矿业大学,获硕士学位。现就职于天地(常州)自动化有限公司。他目前的研究兴趣包括矿山智能、人工智能和深度学习。赵业新,2020年毕业于中国西安长安大学,获硕士学位。现就职于天地(常州)自动化有限公司。他目前的研究兴趣包括自动驾驶的决策控制。王天宇,2019年毕业于中国江苏大学镇江分校,获硕士学位。现就职于天地(常州)自动化有限公司。他目前的研究方向包括控制、自动化和结构设计。
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引用次数: 0
Adam bald eagle optimization-based Shepard CNN for classification and pixel change detection of brain tumour using post and pre-operative brain MRI images 基于Adam秃鹰优化的Shepard CNN在颅脑MRI术后和术前肿瘤分类和像素变化检测中的应用
Pub Date : 2023-10-05 DOI: 10.1080/13682199.2023.2262259
S Abirami, B Lanitha
ABSTRACTBrain tumour is a dangerous disease and it harms health. This research develops a productive model to categorize brain tumours exploiting an Adam Bald Eagle optimization-based Shepard Convolutional Neural Network (ABEO-ShCNN). Initially, the preprocessing is done in pre- and post-operative Magnetic resonance imaging (MRI). Then, U-Net++ is exploited to segment, which is tuned by the Bald Border Collie Firefly Optimization Algorithm (BBCFO). The BBCFO is the incorporation of Border Collie Optimization (BCO), the Firefly optimization Algorithm (FA) and Bald Eagle Search (BES). Thereafter, feature extraction is done and then categorization is conducted using ShCNN in which the training is conducted by ABEO. The ABEO is the integration of Adam and BES. The ABEO-ShCNN model has acquired better accuracy, Positive Predictive Value (PPV), True Negative Rate (TNR), True Positive Rate (TPR) and Negative Predictive Value (NPV) for pre-operative MRI, with values of 92.70%, 92.90%, 91.30%, 89.60% and 89.50%, respectively.KEYWORDS: Shepard convolutional neural networkbald eagle search algorithmborder collie optimizationmagnetic resonance imagingfirefly optimization algorithmU-Net++Shepard Convolutional Neural Networkfeature extraction Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsS AbiramiMrs. Abirami S obtained her Bachelor and Master's degrees in computer science and engineering from Anna University, Chennai in 2005 and 2013, respectively. She has worked in various reputed engineering institutions and software industries in and around India. Currently, she is working as an assistant professor in the department of computer science and engineering at Sri Krishna College of Engineering and Technology in Coimbatore, Tamilnadu, India. Her area of interest is Machine Learning and Deep learning.B LanithaDr. Lanitha B received her Bachelor and Master's degrees in computer science and engineering from Bharathiyar University and Karpagam University in 1989 and 1993, respectively. She earned her Ph.D. at Anna University in 2021. Currently, she is working as an associate professor at Karpagam Academy of Higher Education. She has worked in various reputed engineering institutions and software industries. She has published many papers in international journals and conferences.
脑肿瘤是一种危害健康的危险疾病。本研究利用基于Adam Bald Eagle优化的Shepard卷积神经网络(ABEO-ShCNN)开发了一种高效的脑肿瘤分类模型。最初,预处理是在术前和术后磁共振成像(MRI)中完成的。然后,利用U-Net++进行分段,并通过Bald Border Collie Firefly Optimization Algorithm (BBCFO)进行优化。BBCFO结合了Border Collie Optimization (BCO)、Firefly Optimization Algorithm (FA)和Bald Eagle Search (BES)。然后进行特征提取,然后使用ShCNN进行分类,其中ABEO进行训练。ABEO是Adam和BES的集成。ABEO-ShCNN模型在术前MRI上获得了较好的准确率、阳性预测值(Positive Predictive Value, PPV)、真阴性率(True Negative Rate, TNR)、真阳性率(True Positive Rate, TPR)和阴性预测值(Negative Predictive Value, NPV),分别为92.70%、92.90%、91.30%、89.60%和89.50%。关键词:谢泼德卷积神经网络白头鹰搜索算法边界牧羊犬优化磁共振成像萤火虫优化算法谢泼德卷积神经网络特征提取披露声明作者未报告潜在利益冲突。关于abiramrs贡献者的说明。Abirami S分别于2005年和2013年获得Anna University, Chennai的计算机科学与工程学士学位和硕士学位。她曾在印度及周边地区的多家知名工程机构和软件行业工作。目前,她在印度泰米尔纳德邦哥印拜陀的克里希纳工程技术学院计算机科学与工程系担任助理教授。她感兴趣的领域是机器学习和深度学习。B LanithaDr。Lanitha B分别于1989年和1993年获得Bharathiyar University和Karpagam University的计算机科学与工程学士和硕士学位。她于2021年在安娜大学获得博士学位。目前,她是Karpagam高等教育学院的副教授。她曾在多家知名工程机构和软件行业工作。她在国际期刊和会议上发表了多篇论文。
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引用次数: 0
Hybrid feature extraction and LLTSA-based dimension reduction for vein pattern recognition 基于lltsa的混合特征提取与降维静脉模式识别
Pub Date : 2023-10-03 DOI: 10.1080/13682199.2023.2257539
P. Gopinath, R. Shivakumar
ABSTRACTIn information and security, the personal identification of individuals becomes much more important. For improving security, several biometric recognition techniques are implemented. However, in finger vein recognition, it faces the critical problem of fake finger vein images, security and less accuracy. To conquer this problem, Hybrid Feature Extraction with Linear Local Tangent Space Alignment-based dimension reduction and Support Vector Machine classifier (HFE–LLTSA–SVM) is proposed. In this hybrid, FE is considered as the combination of histogram of oriented gradients (HOG), grey-level co-occurrence matrix (GLCM), stationary wavelet transform (SWT), and local binary pattern (LBP) for extracting the hybrid feature. LLTSA perform dimension reduction in the outputs of HFE from HOG, GLCM, and LBP. Furthermore, SVM is used for classification which gives authentication based on error-correcting code. Finally, the performance parameters were calculated and the proposed method achieved better accuracy of 99.75%, when compared with existing methods.KEYWORDS: Grey-level co-occurrence matrixhistogram of oriented gradientlocal binary patternstationary wavelet transformsupport vector machine Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsP. GopinathDr P. Gopinath is working as an Assistant Professor in the department of Electronics and Communication Engineering at Sengunthar Engineering College, Tiruchengode. He obtained his Ph. D in Digital Image Processing from Anna University Chennai in 2023. M.E. (Applied Electronics) from Anna University Chennai in 2011. B.E. (Electronics and Communication Engineering) from Anna University Chennai in 2008.He has a 13 years of teaching experience. His research interest includes Digital Image processing, Signal processing, Biometrics, Machine learning, and Artificial Intelligence. He has published more than 12 research article and 2 patent.R. ShivakumarDr R. Shivakumar is working as a Professor in Department of Electrical and Electronics Engineering at Sona College of Technology, Salem. He obtained his Ph. D in Electrical Engineering from Anna University Chennai in November 2012.M.E. (Power System Engg) -First class with Distinction in 1998 from Annamalai University, Chidambaram. B.E. (Electrical and Electronics Engineering) with I Class in 1997, from Shanmugha College of Engineering, Tanjore, Bharadhidasan University. His research interest includes Power System Stability and Control, Bio Inspired Optimization algorithms, Renewable energy conversion systems, and Digital Technology applications in Power Engineering. He has published more than 40 research article and 60 International and National Conference Papers. He won BEST RESEARCHER Award for academic contribution in Electrical and Electronics Engineering specialization under National Faculty Award 2021-2022 awarded by Novel Research Academy, Puducherry, India on 4.5.2022.
摘要在信息安全领域,个人身份识别变得越来越重要。为了提高安全性,采用了几种生物特征识别技术。然而,在手指静脉识别中,它面临着假手指静脉图像、安全性和准确性不高的关键问题。为了解决这一问题,提出了基于线性局部切线空间对齐的混合特征提取和支持向量机分类器(HFE-LLTSA-SVM)。在该混合特征中,FE被认为是结合定向梯度直方图(HOG)、灰度共生矩阵(GLCM)、平稳小波变换(SWT)和局部二值模式(LBP)来提取混合特征。LLTSA在HOG、GLCM和LBP的HFE输出中执行降维。在此基础上,采用支持向量机进行分类,基于纠错码进行认证。最后对性能参数进行了计算,与现有方法相比,该方法的准确率达到了99.75%。关键词:灰度共现矩阵定向梯度直方图局部二值模式平稳小波变换支持向量机披露声明作者未报告潜在利益冲突。附加信息:贡献者说明Gopinath博士是Tiruchengode Sengunthar工程学院电子与通信工程系的助理教授。他于2023年获得金奈安娜大学数字图像处理博士学位。2011年毕业于金奈安娜大学应用电子学硕士。2008年毕业于金奈安娜大学电子与通信工程学士学位。他有13年的教学经验。主要研究方向为数字图像处理、信号处理、生物识别、机器学习、人工智能等。发表学术论文12篇,专利2项。R. Shivakumar博士是塞勒姆Sona理工学院电气和电子工程系的教授。他于2012年11月获得Anna University Chennai电气工程博士学位。(电力系统工程)- 1998年毕业于印度奇丹巴拉姆的安纳玛莱大学,获一等优异成绩。1997年毕业于印度Bharadhidasan大学Shanmugha工程学院,获电气与电子工程学士学位。主要研究方向为电力系统稳定性与控制、生物优化算法、可再生能源转换系统、数字技术在电力工程中的应用。发表研究论文40余篇,国际国内会议论文60余篇。他于2022年5月4日获得了由印度普杜切里新颖研究学院颁发的2021-2022年国家教师奖的电气和电子工程专业学术贡献奖。
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引用次数: 0
Wavelet energy-based adaptive retinex algorithm for low light mobile video enhancement 基于小波能量的自适应retinex弱光移动视频增强算法
Pub Date : 2023-09-24 DOI: 10.1080/13682199.2023.2260663
G. R. Vishalakshi, A. Shobharani, M. C. Hanumantharaju
ABSTRACTOur paper presents an adaptive multiscale retinex algorithm and a new wavelet energy metric to improve low-light video captured on mobile devices. Initially, we extract RGB frames from the video and convert them to hue-saturation-value (HSV) format, preserving the hue channel to prevent common RGB colour shifting issues. Saturation channel enhancement is achieved through histogram equalization (HE), extending the dynamic range. The adaptive retinex algorithm enhances the value channel, quantified by our new wavelet energy metric. Combining the modified value and saturation channels improves the contrast of the reconstructed image. As a final step, we transform the HSV video back to RGB and restore naturalness using a modified colour restoration technique. The proposed approach has been tested on over 300 images and videos. It is evident from the experimental results presented that the proposed method lowers noise and halo artifacts more effectively than existing methods.KEYWORDS: Low light enhancementmobile videoHSV colour spaceadaptive multiscale retinexwavelet energy Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsG. R. VishalakshiVishalakshi G. R. completed a Bachelor of Engineering (B. E) in Electronics and Communication and a Masters of Technology (M. Tech) in Digital Electronics from Visvesvaraya Technological University (VTU), Belagavi, Karnataka, India, in the years 2005 and 2012, respectively and Pursuing Ph.D. in VTU under the guidance of A. Shobharani on video enhancement technique. Her research interest is in subjects like image and video processing, VLSI, and CNN.A. ShobharaniA. Shobha Rani received her B. E (Electronics and Communication Engineering) degree from Bangalore University in the year 2000, M. Tech (Digital Electronics and Communication) Degree from Visveswaraya Technological University (VTU), Belgaum in the year 2005, and Ph. D. (Networking) degree from Kuvempu University, Shimoga, in the year, 2014. She works as an Associate Professor in the Department of ECE at BMS Institute of Technology and Management, Bengaluru, India. She has 23 technical articles in well-reputed journals and conferences. Her research interests include designing and implementing efficient protocols for wireless networks such as Ad hoc, Sensor, and Mesh networks.M. C. HanumantharajuM. C. Hanumantharaju received his B. E (Electronics and Communication Engineering) degree from Bangalore University in the year 2001, M. Tech (Digital Communication and Network Engineering) degree from Visvesvaraya Technological University (VTU), Belagavi in the year 2004, and Ph. D (VLSI Signal and Image Processing) degree from VTU, Belagavi, in the year, 2014. He works as a Professor in the Department of ECE at BMS Institute of Technology and Management, Bengaluru, India. He has authored two books and 50 technical articles in refereed journals and proceedings such as IEEE, Intelligent
摘要:本文提出了一种自适应多尺度retinex算法和一种新的小波能量度量来改善移动设备上低光视频的捕获。最初,我们从视频中提取RGB帧并将其转换为色调饱和值(HSV)格式,保留色调通道以防止常见的RGB颜色移动问题。饱和通道增强是通过直方图均衡化(HE)实现的,扩展了动态范围。自适应retinex算法增强了价值通道,并通过我们的新小波能量度量进行量化。将修正值与饱和通道相结合,提高了重建图像的对比度。作为最后一步,我们将HSV视频转换回RGB,并使用修改的颜色恢复技术恢复自然性。该方法已经在300多张图片和视频上进行了测试。实验结果表明,该方法比现有方法更有效地降低了噪声和光晕伪影。关键词:弱光增强移动视频hsv色彩空间自适应多尺度视网膜小波能量披露声明作者未报告潜在利益冲突。其他资料:捐助者说明R. VishalakshiVishalakshi G. R.分别于2005年和2012年在印度卡纳塔克邦Belagavi的Visvesvaraya Technological University (VTU)获得电子与通信工程学士学位和数字电子学硕士学位,并在a . Shobharani的视频增强技术指导下在VTU攻读博士学位。她的研究兴趣包括图像和视频处理、VLSI和cnn。ShobharaniA。Shobha Rani于2000年在班加罗尔大学获得电子与通信工程学士学位,2005年在Belgaum Visveswaraya理工大学获得数字电子与通信硕士学位,并于2014年在Shimoga Kuvempu大学获得网络博士学位。她是印度班加罗尔BMS技术与管理学院ECE系的副教授。她在知名期刊和会议上发表了23篇技术文章。她的研究兴趣包括设计和实现无线网络的高效协议,如Ad hoc,传感器和Mesh网络。c . HanumantharajuM。C. Hanumantharaju于2001年获得班加罗尔大学(Bangalore University)电子与通信工程学士学位,2004年获得Visvesvaraya Technological University (VTU)数字通信与网络工程硕士学位,2014年获得VTU Belagavi超大集成电路信号与图像处理博士学位。他是印度班加罗尔BMS技术与管理学院ECE系教授。他撰写了两本书,并在IEEE,智能系统,粒子群优化等期刊和会议上发表了50篇技术文章。他目前是IEEE Transactions on Industrial Electronics, Computers and Electrical Engineering Journal, Journal of Microscopy and Ultrastructure等杂志的审稿人。他的研究兴趣包括信号和图像处理算法的硬件架构设计,计算机视觉,寄存器传输电平(RTL) Verilog编码,集成电路(ic)的合成和优化,以及FPGA/ASIC设计。
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引用次数: 0
Enhanced face age progression and regression model using hyper-parameter tuning-large scale GAN by hybrid heuristic improvement 采用混合启发式改进的超参数调谐大规模GAN增强面部年龄进展和回归模型
Pub Date : 2023-09-22 DOI: 10.1080/13682199.2023.2254134
Tejaswini Yadav, Rajneeshkaur Sachdeo
ABSTRACTThe main challenge is to automate the model for aged or de-aged face generation. However, there are certain limitations on accuracy for age estimation and identity preservation. To achieve this, a new face age progression and regression is proposed by Hyper-parameter Tuning-Large Scale Generative Adversarial Network (HT-Large Scale GAN) with Pollination Rate-based Sunflower Dolphin Swarm Optimization (PR-SDSO). The input images are collected and fed into the object detection model, where the viola Jones algorithm is utilized. Here, the pre-processing is done by median filtering and contrast enhancement. The face age progression and regression are accomplished by novel HT-Large Scale GAN, where the hyperparameters are optimized by a new algorithm of PR-SDSO. Throughout the result analysis, the proposed model ensures that it provides the appropriate synthesized images for both the progression and regression phases and acquires less error to improve the quality of the image.KEYWORDS: Face age progression and regressionobject detection modelviola-jones algorithmmedian filtering and contrast enhancement‌deep learningdolphin swarm algorithm‌pollination rate-based sunflower dolphin swarm optimizationhyper-parameter tuning-large scale generative adversarial networks Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsTejaswini YadavTejaswini Yadav received master degree from Pune University. She is currently research scholar in MIT-ADT University and her research area includes machine learning and artificial intelligence.Rajneeshkaur SachdeoRajneeshkaur Sachdeo received Ph.D. degree from SGBAU State University, Amravati. She is currently Dean of Engineering and Head of Computer Science and Engineering at MIT-ADT University, Pune. Her research area includes Data Security and privacy, natural language processing and linguistics, machine learning, data mining, and Wireless network. She is a member of ISTE, IACSIT and CSI.
摘要老化或去老化人脸生成模型的自动化是目前面临的主要挑战。然而,年龄估计和身份保持的准确性存在一定的局限性。为此,提出了一种基于传粉率的向日葵海豚群优化(PR-SDSO)的超参数调谐-大规模生成对抗网络(HT-Large Scale GAN)面部年龄进展和回归方法。收集输入图像并将其输入到目标检测模型中,该模型使用viola Jones算法。在这里,预处理是通过中值滤波和对比度增强完成的。采用新型的HT-Large Scale GAN实现了人脸年龄的增长和回归,其中超参数采用一种新的PR-SDSO算法优化。在整个结果分析中,所提出的模型保证了在前进和回归阶段都能提供合适的合成图像,并获得较小的误差以提高图像质量。关键词:人脸年龄进展与回归目标检测模型viola-jones算法中值滤波与对比度增强深度学习海豚群算法基于传粉率的向日葵海豚群优化超参数调谐大规模生成对抗网络披露声明作者未报告潜在的利益冲突。stejaswini YadavTejaswini Yadav获得浦那大学硕士学位。她目前是MIT-ADT大学的研究学者,她的研究领域包括机器学习和人工智能。Rajneeshkaur Sachdeo毕业于印度阿姆拉瓦蒂SGBAU州立大学,获博士学位。她目前是浦那MIT-ADT大学工程学院院长和计算机科学与工程系主任。她的研究领域包括数据安全和隐私、自然语言处理和语言学、机器学习、数据挖掘和无线网络。她是ISTE, IACSIT和CSI的成员。
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引用次数: 0
Optimization assisted autoregressive technique with deep convolution neural network-based entropy filter for image demosaicing 基于深度卷积神经网络熵滤波的图像去马赛克优化辅助自回归技术
Pub Date : 2023-09-20 DOI: 10.1080/13682199.2023.2248576
C. Anitha Mary, A. Boyed Wesley
ABSTRACTThis paper presents an image demosaicing based on an optimization-driven deep learning model, namely the Autoregressive Water Wave Optimization algorithm (Autoregressive-WWO). The proposed method is devised by assimilating the Wave Optimization algorithm (WWO), and the Conditional autoregressive value at risk (CAViaR) model. Here, the input images are subjected to Autoregressive WWO-based local polynomial approximation and intersection of confidence intervals (LPA-ICI) filter, and Deep Convolution neural network (Deep CNN) in a concurrent manner. The filter coefficients are obtained from the proposed Autoregressive WWO-based LPA-ICI filter and the residual image is obtained from Deep CNN. In order to create the demosaiced image, these two outputs are combined using an entropy measure. The proposed method offered superior performance with the highest Peak signal to noise ratio (PSNR) of 40.049dB, the highest Second derivative measure of enhancement (SDME) of 50.168dB, and highest Structural Index Similarity (SSIM) of 0.9056.KEYWORDS: Image demosaicingentropycolour filter arraydeep convolution neural networkfusion processLPA-ICI filterWWOCAViaR AcknowledgementsI would like to convey my sincere gratitude to the co-authors of this publication for their insightful advice and support throughout the conception and planning of this research project. All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementMultispectral Image Database: Stuff, ‘https://www.cs.columbia.edu/CAVE/databases/multispectral/stuff/’ Accessed on April 2021.
摘要提出了一种基于优化驱动深度学习模型的图像去马赛克算法,即自回归水波优化算法(Autoregressive Water Wave Optimization algorithm,简称Autoregressive- wwo)。该方法是将波浪优化算法(WWO)和条件自回归风险值(CAViaR)模型相结合而设计的。在这里,输入图像以并行的方式进行基于自回归的局部多项式近似和置信区间相交(LPA-ICI)滤波器和深度卷积神经网络(Deep CNN)。滤波器系数由提出的自回归基于ww的LPA-ICI滤波器获得,残差图像由Deep CNN获得。为了创建去马赛克图像,使用熵度量将这两个输出组合在一起。该方法的峰值信噪比(PSNR)最高为40.049dB,二阶导数增强(SDME)最高为50.168dB,结构指数相似度(SSIM)最高为0.9056。关键词:图像去拼接熵彩色滤波器阵列深度卷积神经网络融合过程lpa - ici滤波器wwocaviar致谢我想向这篇文章的合著者表示诚挚的感谢,感谢他们在整个研究项目的构思和规划过程中提供的富有洞察力的建议和支持。所有作者在构思设计、修改稿件、最终审定出版版本等方面都做出了实质性的贡献。此外,所有作者同意对工作的各个方面负责,以确保与工作任何部分的准确性或完整性有关的问题得到适当的调查和解决。披露声明作者未报告潜在的利益冲突。数据可用性声明多光谱图像数据库:Stuff, ' https://www.cs.columbia.edu/CAVE/databases/multispectral/stuff/ '于2021年4月访问。
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引用次数: 0
An implementation of intelligent YOLOv3-based anomaly detection model from crowded video scenarios with optimized ensemble pattern extraction 基于yolov3的拥挤视频场景智能异常检测模型的优化集成模式提取
Pub Date : 2023-09-13 DOI: 10.1080/13682199.2023.2255335
Poorni Ramakrishnan, P. Madhavan
The anomaly or abnormality detection in crowded scenes helps in identifying the violence and protecting the people from severe damage. Thus, there is a need to detect the anomalies with the classifier for learning information along with the usage of huge architectures. A new anomaly detection model is implemented in this model. The collected data is fed to optimal ensemble pattern extraction scheme through techniques like Local binary patterns (LBP), Local Gradient Pattern (LGP), and Local Tetra Pattern (LTrP). The weights are tuned by a new hybrid Spiral Search-based Black Widow Glowworm Swarm Optimization (SS-BWGSO) for getting the optimal ensemble patterns. Next, anomaly frame classification is carried out by optimized VGG16+ResNet technique, where the hyperparameters of VGG16 and ResNet are tuned by SS-BWGSO algorithm. Finally, anomaly detection is performed by the YOLOV3 classifier. Throughout the result analysis the higher performance of the designed technique is observed over the classical methods.
在拥挤的场景中发现异常或异常有助于识别暴力行为,保护人们免受严重伤害。因此,随着庞大体系结构的使用,需要使用分类器来检测异常以学习信息。在此模型中实现了一种新的异常检测模型。通过局部二值模式(LBP)、局部梯度模式(LGP)和局部利乐模式(LTrP)等技术,将采集到的数据馈送到最优的集成模式提取方案中。采用一种新的基于混合螺旋搜索的黑寡妇萤火虫群优化算法(SS-BWGSO)对权重进行调整,以获得最优的集合模式。其次,采用优化后的VGG16+ResNet技术进行异常帧分类,其中VGG16和ResNet的超参数采用SS-BWGSO算法进行调优。最后,由YOLOV3分类器进行异常检测。结果分析表明,所设计的方法比传统方法具有更高的性能。
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引用次数: 0
Multi-object 3D segmentation of brain structures using a geometric deformable model with a priori knowledge 基于先验知识的几何变形模型脑结构多目标三维分割
Pub Date : 2023-09-11 DOI: 10.1080/13682199.2023.2256504
Mohamed Baghdadi, Nacéra Benamrane, Mounir Boukadoum, Lakhdar Sais
Brain structure segmentation in 3D Magnetic Resonance Images is crucial for understanding neurodegenerative disorders. Manual segmentation is error-prone, necessitating robust automated techniques. In this paper, we introduce a novel and robust approach for the simultaneous segmentation of multiple brain structures in MRI images. Our method involves the concurrent evolution of 3D surfaces toward predefined anatomical targets, employing an efficient multi-object generalized fast marching method (MOGFMM) for simultaneous object detection. Additionally, we propose an effective evolution function that integrates prior knowledge from anatomical and probabilistic atlases, as well as spatial relationships among the segmented structures. Each deformable surface corresponds to a specific structure. To validate our approach, we conducted experiments on a dataset of real brain images (IBSR) and compared the results with several state-of-the-art methods. The obtained results were promising, demonstrating the effectiveness and superiority of our developed method.
三维磁共振图像中的脑结构分割对于理解神经退行性疾病至关重要。手动分割容易出错,需要强大的自动化技术。在本文中,我们介绍了一种新的鲁棒方法,用于同时分割MRI图像中的多个大脑结构。我们的方法涉及三维表面向预定义解剖目标的并发演化,采用高效的多目标广义快速推进方法(MOGFMM)进行同时目标检测。此外,我们提出了一个有效的进化函数,该函数集成了解剖学和概率地图集的先验知识,以及分割结构之间的空间关系。每个可变形表面对应一个特定的结构。为了验证我们的方法,我们在真实脑图像数据集(IBSR)上进行了实验,并将结果与几种最先进的方法进行了比较。所得结果令人满意,证明了该方法的有效性和优越性。
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引用次数: 0
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The Imaging Science Journal
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