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- Keyword
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- Convolutional neural networks (1)
- Deep learning (1)
- Eye tracking (1)
- Gaze velocity (1)
- Image forgery detection; Image forensics; Image decomposition; regression deep learning neural network; Polar dy-adic wavelet transform; Genetic Three Step Search algorithm (GTSS); Modified Otsu Thresholding (MOT) algorithm. (1)
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Full-Text Articles in Other Engineering
Rdlnn-Based Image Forgery Detection And Forged Region Detection Using Mot, Akram Hatem Saber, Mohd Ayyub Khan, Basim Galeb Mejbel
Rdlnn-Based Image Forgery Detection And Forged Region Detection Using Mot, Akram Hatem Saber, Mohd Ayyub Khan, Basim Galeb Mejbel
Karbala International Journal of Modern Science
Image forgery detection TEMPhas become an emerging research area due to the increasing number of forged images circulating on the internet and other social media, which leads to legal and social issues. Image forgery detection includes the classification of an image as forged or authentic and as well as localizing the forgery wifin the image. In this paper, we propose a Regression Deep Learning Neural Network (RDLNN) based image forgery detection followed by Modified Otsu Thresholding (MOT) algorithm to detect the forged region. The proposed model comprises five steps that are preprocessing, image decomposition, feature extraction, classification and block matching. …
Deep Gaze Velocity Analysis During Mammographic Reading For Biometric Identification Of Radiologists, Hong-Jun Yoon, Folami Alamudun, Kathy Hudson, Garnetta Morin-Ducote, Georgia Tourassi
Deep Gaze Velocity Analysis During Mammographic Reading For Biometric Identification Of Radiologists, Hong-Jun Yoon, Folami Alamudun, Kathy Hudson, Garnetta Morin-Ducote, Georgia Tourassi
Journal of Human Performance in Extreme Environments
Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed that …