305 citations · 450 across the 12 of their papers we have counts for
13 papers
Evaluating few shot and Contrastive learning Methods for Code Clone Detection
Mohamad Khajezade, Fatemeh Hendijani Fard, Mohamed S. Shehata
Context: Code Clone Detection (CCD) is a software engineering task that is used for plagiarism detection, code search, and code comprehension. Recently, deep learning-based models…
Deep Machine Learning Based Egyptian Vehicle License Plate Recognition Systems
Mohamed Shehata, Mohamed Taha Abou-Kreisha, Hany Elnashar
Automated Vehicle License Plate (VLP) detection and recognition have ended up being a significant research issue as of late. VLP localization and recognition are some of the most e…
Automated Human Cell Classification in Sparse Datasets using Few-Shot Learning
Reece Walsh, Mohamed H. Abdelpakey, Mohamed S. Shehata +1
Classifying and analyzing human cells is a lengthy procedure, often involving a trained professional. In an attempt to expedite this process, an active area of research involves au…
NullSpaceNet: Nullspace Convoluional Neural Network with Differentiable Loss Function
Mohamed H. Abdelpakey, Mohamed S. Shehata
We propose NullSpaceNet, a novel network that maps from the pixel level input to a joint-nullspace (as opposed to the traditional feature space), where the newly learned joint-null…
DomainSiam: Domain-Aware Siamese Network for Visual Object Tracking
Mohamed H. Abdelpakey, Mohamed S. Shehata
Visual object tracking is a fundamental task in the field of computer vision. Recently, Siamese trackers have achieved state-of-the-art performance on recent benchmarks. However, S…
Vehicles Detection Based on Background Modeling
Mohamed Shehata, Reda Abo-Al-Ez, Farid Zaghlool +1
Background image subtraction algorithm is a common approach which detects moving objects in a video sequence by finding the significant difference between the video frames and the…
DensSiam: End-to-End Densely-Siamese Network with Self-Attention Model for Object Tracking
Mohamed H. Abdelpakey, Mohamed S. Shehata, Mostafa M. Mohamed
Convolutional Siamese neural networks have been recently used to track objects using deep features. Siamese architecture can achieve real time speed, however it is still difficult…
Estimation and Tracking of AP-diameter of the Inferior Vena Cava in Ultrasound Images Using a Novel Active Circle Algorithm
Ebrahim Karami, Mohamed Shehata, Andrew Smith
Medical research suggests that the anterior-posterior (AP)-diameter of the inferior vena cava (IVC) and its associated temporal variation as imaged by bedside ultrasound is useful…
Adaptive Polar Active Contour for Segmentation and Tracking in Ultrasound Videos
Ebrahim Karami, Mohamed Shehata, Andrew Smith
Detection of relative changes in circulating blood volume is important to guide resuscitation and manage a variety of medical conditions including sepsis, trauma, dialysis and cong…
Tracking of the Internal Jugular Vein in Ultrasound Images Using Optical Flow
Ebrahim Karami, Mohamed Shehata, Andrew Smith
Detection of relative changes in circulating blood volume is important to guide resuscitation and manage variety of medical conditions including sepsis, trauma, dialysis and conges…
Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images
Ebrahim Karami, Siva Prasad, Mohamed Shehata
Fast and robust image matching is a very important task with various applications in computer vision and robotics. In this paper, we compare the performance of three different imag…
A Semi-Automated Technique for Internal Jugular Vein Segmentation in Ultrasound Images Using Active Contours
Ebrahim Karami, Mohamed Shehata, Peter McGuire +1
The assessment of the blood volume is crucial for the management of many acute and chronic diseases. Recent studies have shown that circulating blood volume correlates with the cro…
Image Identification Using SIFT Algorithm: Performance Analysis against Different Image Deformations
Ebrahim Karami, Mohamed Shehata, Andrew Smith
Image identification is one of the most challenging tasks in different areas of computer vision. Scale-invariant feature transform is an algorithm to detect and describe local feat…