As a member of one scientific family, Mahmood Fathy mostly works in the field of Cognitive psychology, focusing on Distraction and, on occasion, Neuroscience. His work on Neuroscience is being expanded to include thematically relevant topics such as Distraction. Many of his studies on Artificial intelligence apply to Smart camera as well. He connects Computer vision with Computer graphics (images) in his research. He merges Computer graphics (images) with Computer vision in his study. His multidisciplinary approach integrates Computer network and Wireless sensor network in his work. With his scientific publications, his incorporates both Wireless sensor network and Computer network. His Image (mathematics) study frequently links to related topics such as Hough transform. Pattern recognition (psychology) and Artificial intelligence are commonly linked in his work.
Mahmood Fathy integrates several fields in his works, including Artificial intelligence and Machine learning. His Computer vision study frequently draws parallels with other fields, such as Image (mathematics). His Computer vision research extends to Image (mathematics), which is thematically connected. He incorporates Computer network and Distributed computing in his studies. He merges many fields, such as Distributed computing and Computer network, in his writings. Mahmood Fathy combines Telecommunications and Wireless in his research. Many of his studies involve connections with topics such as Wireless ad hoc network and Wireless. Mahmood Fathy integrates Wireless ad hoc network with Vehicular ad hoc network in his research. Mahmood Fathy combines topics linked to Telecommunications with his work on Vehicular ad hoc network.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Vehicular Ad Hoc Networks (VANETs): Challenges and Perspectives
Saleh Yousefi;Mahmoud Mousavi;Mahmood Fathy.
international conference on its telecommunications (2006)
A classified and comparative study of edge detection algorithms
M. Sharifi;M. Fathy;M.T. Mahmoudi.
international conference on information technology coding and computing (2002)
Adversarially Learned One-Class Classifier for Novelty Detection
Mohammad Sabokrou;Mohammad Khalooei;Mahmood Fathy;Ehsan Adeli.
computer vision and pattern recognition (2018)
Analytical Model for Connectivity in Vehicular Ad Hoc Networks
S. Yousefi;E. Altman;R. El-Azouzi;M. Fathy.
IEEE Transactions on Vehicular Technology (2008)
Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes
Mohammad Sabokrou;Mohsen Fayyaz;Mahmood Fathy;Zahra. Moayed.
Computer Vision and Image Understanding (2018)
Deep-Cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes
Mohammad Sabokrou;Mohsen Fayyaz;Mahmood Fathy;Reinhard Klette.
IEEE Transactions on Image Processing (2017)
An image detection technique based on morphological edge detection and background differencing for real-time traffic analysis
M. Fathy;M. Y. Siyal.
Pattern Recognition Letters (1995)
Real-time anomaly detection and localization in crowded scenes
Mohammad Sabokrou;Mahmood Fathy;Mojtaba Hoseini;Reinhard Klette.
computer vision and pattern recognition (2015)
An Iranian License Plate Recognition System Based on Color Features
Amir Hossein Ashtari;Mohd Jan Nordin;Mahmood Fathy.
IEEE Transactions on Intelligent Transportation Systems (2014)
Enhancing AODV routing protocol using mobility parameters in VANET
O. Abedi;M. Fathy;J. Taghiloo.
acs/ieee international conference on computer systems and applications (2008)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: