Ali Farouk | Cairo University (original) (raw)
Related Authors
University of the Basque Country, Euskal Herriko Unibertsitatea
Uploads
Papers by Ali Farouk
Image and Vision Computing, 2019
Building reliable surveillance systems is critical for security and safety. A core component of a... more Building reliable surveillance systems is critical for security and safety. A core component of any surveillance system is the human detection model. With the recent advances in the hardware and embedded devices, it becomes possible to make a real-time human detection system with low cost. This paper surveys different systems and techniques that have been deployed on embedded devices such as Raspberry Pi. The characteristics of datasets, feature extraction techniques, and machine learning models are covered. A unified dataset is utilized to compare different systems with respect to accuracy and performance time. New enhancements are suggested, and future research directions are highlighted.
Computers, Materials & Continua
Image segmentation is crucial for various research areas. Many computer vision applications depen... more Image segmentation is crucial for various research areas. Many computer vision applications depend on segmenting images to understand the scene, such as autonomous driving, surveillance systems, robotics, and medical imaging. With the recent advances in deep learning (DL) and its confounding results in image segmentation, more attention has been drawn to its use in medical image segmentation. This article introduces a survey of the state-of-the-art deep convolution neural network (CNN) models and mechanisms utilized in image segmentation. First, segmentation models are categorized based on their model architecture and primary working principle. Then, CNN categories are described, and various models are discussed within each category. Compared with other existing surveys, several applications with multiple architectural adaptations are discussed within each category. A comparative summary is included to give the reader insights into utilized architectures in different applications and datasets. This study focuses on medical image segmentation applications, where the most widely used architectures are illustrated, and other promising models are suggested that have proven their success in different domains. Finally, the present work discusses current limitations and solutions along with future trends in the field.
Image and Vision Computing, 2019
Building reliable surveillance systems is critical for security and safety. A core component of a... more Building reliable surveillance systems is critical for security and safety. A core component of any surveillance system is the human detection model. With the recent advances in the hardware and embedded devices, it becomes possible to make a real-time human detection system with low cost. This paper surveys different systems and techniques that have been deployed on embedded devices such as Raspberry Pi. The characteristics of datasets, feature extraction techniques, and machine learning models are covered. A unified dataset is utilized to compare different systems with respect to accuracy and performance time. New enhancements are suggested, and future research directions are highlighted.
Computers, Materials & Continua
Image segmentation is crucial for various research areas. Many computer vision applications depen... more Image segmentation is crucial for various research areas. Many computer vision applications depend on segmenting images to understand the scene, such as autonomous driving, surveillance systems, robotics, and medical imaging. With the recent advances in deep learning (DL) and its confounding results in image segmentation, more attention has been drawn to its use in medical image segmentation. This article introduces a survey of the state-of-the-art deep convolution neural network (CNN) models and mechanisms utilized in image segmentation. First, segmentation models are categorized based on their model architecture and primary working principle. Then, CNN categories are described, and various models are discussed within each category. Compared with other existing surveys, several applications with multiple architectural adaptations are discussed within each category. A comparative summary is included to give the reader insights into utilized architectures in different applications and datasets. This study focuses on medical image segmentation applications, where the most widely used architectures are illustrated, and other promising models are suggested that have proven their success in different domains. Finally, the present work discusses current limitations and solutions along with future trends in the field.