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Visibility Enhancement of Scene Images Degraded by Foggy Weather Condition: An Application to Video Surveillance

Computers, Materials & Continua 2021年第9期68卷 3465-3481页

作者： Ghulfam Zahra Muhammad Imran Abdulrahman M.Qahtani Abdulmajeed Alsufyani Omar Almutiry Awais Mahmood fayez eid alazemi Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and TechnologyIslamabad44000Pakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia College of Applied Computer Science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia Department of Computer Science and Information Systems College of Business StudiesPAAET12062Kuwait

:In recent years,video surveillance application played a significant role in our daily *** taken during foggy and haze weather conditions for video surveillance application lose their authenticity and hence reduces the *** reason behind visibility enhancement of foggy and haze images is to help numerous computer and machine vision applications such as satellite imagery,object detection,target killing,and *** remove fog and enhance visibility,a number of visibility enhancement algorithms and methods have been proposed in the ***,these techniques suffer from several limitations that place strong obstacles to the real world outdoor computer vision *** existing techniques do not perform well when images contain heavy fog,large white region and strong atmospheric *** research work proposed a new framework to defog and dehaze the image in order to enhance the visibility of foggy and haze *** proposed framework is based on a Conditional generative adversarial network(CGAN)with two networks;generator and discriminator,each having distinct *** generator network generates fog-free images from foggy images and discriminator network distinguishes between the restored image and the original fog-free *** are conducted on FRIDA dataset and haze *** assess the performance of the proposed method on fog dataset,we use PSNR and SSIM,and for Haze dataset use e,r−,andσas performance *** results shows that the proposed method achieved higher values of PSNR and SSIM which is 18.23,0.823 and lower values produced by the compared method which are 13.94,0.791 and so *** results demonstrated that the proposed framework Has removed fog and enhanced the visibility of foggy and hazy images.

关键词： Video surveillance degraded images image restoration transmission map visibility enhancement

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A Machine Learning Approach for Expression Detection in Healthcare Monitoring Systems

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Computers, Materials & Continua 2021年第5期67卷 2123-2139页

作者： Muhammad Kashif Ayyaz Hussain Asim Munir Abdul Basit Siddiqui AaqifAfzaal Abbasi Muhammad Aakif Arif Jamal Malik fayez eid alazemi Oh-Young Song Department of Computer Science&Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science Quaid-i-Azam UniversityIslamabad44000Pakistan Department of Computer Science Capital University of Science&TechnologyIslamabad44000Pakistan Department of Software Engineering Foundation UniversityIslamabad44000Pakistan Department of Computer Science Abdul Wali Khan UniversityMardan23200Pakistan Department of Computer Science and Information Systems College of Business StudiesPAAET12062Kuwait Department of Software Sejong UniversitySeoul05006Korea

Expression detection plays a vital role to determine the patient’s condition in healthcare *** helps the monitoring teams to respond swiftly in case of *** to the lack of suitable methods,results are often compromised in an unconstrained environment because of pose,scale,occlusion and illumination variations in the image of the face of the patient.A novel patch-based multiple local binary patterns(LBP)feature extraction technique is proposed for analyzing human behavior using facial expression *** consists of three-patch[TPLBP]and four-patch LBPs[FPLBP]based feature engineering *** representation is encoded from local patch statistics using these *** and FPLBP capture information that is encoded to find likenesses between adjacent patches of pixels by using short bit strings contrary to pixel-based *** images are transformed into the frequency domain using a discrete cosine transform(DCT).Most discriminant features extracted from coded DCT images are combined to generate a feature *** vector machine(SVM),k-nearest neighbor(KNN),and Naïve Bayes(NB)are used for the classification of facial expressions using selected *** experimentation is performed to analyze human behavior by considering standard extended Cohn Kanade(CK+)and Oulu–CASIA *** demonstrate that the proposed methodology outperforms the other techniques used for comparison.

关键词： Detection expressions gestures analytics pain patch-based local binary descriptor discrete cosine transform healthcare

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