Experienced Doctoral Researcher at the university of Bonn with a demonstrated history of working in the research industry. Skilled in Computer Vision and Deep Learning. Strong research professional with a Master’s Degree focused in Artificial Intelligence from MUT.
Doctoral researcher at the University of Bonn, faculty of Computer Science III under supervision of Prof. Dr. J. Gall.
Designing and Developing Deep Neural Networks Architectures for Computer Vision Tasks.
Machine learning and Deep learning researcher in IUST HPC lab with the supervision of Prof. Dr. M. Fathy.
Supervisor: Prof. Dr. J. Gall
Supervisors: Prof. Dr. Mahmood Fathy, Dr. Mojtaba Hosseini
Advisor: Dr. Mohammad Sabokrou
Thesis: Activity Recognition in Video based on Convolutional Neural Networks
Ranked first with highest GPA among all Computer Engineering students (AI) since 2014
Supervisor: Dr. K. Kiani
Thesis: Designing and Implementing a Cloud-based Accounting System
Ranked First among all of the university computer engineering students since the year 2010
Feb 2017 - IEEE Transactions on Image Processing
This paper proposes a fast and reliable method for anomaly detection and localization in video data showing crowded scenes. Time-efficient anomaly localization is an ongoing challenge and subject of this paper. We propose a cubic patch-based method, characterized by a cascade of classifiers, which makes use of an advanced feature learning approach. Our cascade of classifiers has two main stages. First, a light but deep 3D auto-encoder is used for early identification of “many” normal cubic patches.
Sep 2016 - ACCV
This paper presents a novel method to involve both spatial and temporal features for semantic video segmentation. Current work on convolutional neural networks(CNNs) has shown that CNNs provide advanced spatial features supporting a very good performance of solutions for both image and video analysis, especially for the semantic segmentation task.
Nov 2015 - 9th Iranian Conference on Machine Vision and Image Processing (MVIP)
In this paper, we propose a method for user Finger Vein Authentication (FVA) as a biometric system. Using the discriminative features for classifying theses finger veins is one of the main tips that make difference in related works, thus we propose to learn a set of representative features, based on auto-encoders.