Dr. Sana Alamgeer (She/Her)
PhD | Electronic and Automation Systems Engineering
About Me
I was born in Pakistan, and completed my high-level education there. Then, for my doctoral studies, I moved to Brazil. Currently, I am a research scholar at the University of Brasília, Brazil. I am currently interested in developing deep learning-based methods for visual quality assessment methods for 2D and 4D images and videos. I also work on deep learning-based visual attention models for 360-degree videos. More specifically, my research interests include deep learning, quality assessment, feature extraction, visual attention, and Light Field images.
Prior to the PhD, I obtained a master's degree in computer science from Bahaudding Zakariya University, Multan, Pakistan. I also obtained a master of philosophy degree in computer science from Air University, Multan Campus, Pakistan.
I have my YouTube channel "Coderific", where I publish videos on programming. Considering that every problem has multiple solutions, you will find one of those solutions in this channel. Through this platform, I aim to help research students doing small tasks so that their big projects do not stop.
Publications
Journals
A survey on visual quality assessment methods for light fields
Sana Alamgeer and Mylène C. Q. Farias
Signal Processing: Image Communication - ELSEVIER (2022)
Blind visual quality assessment of light field images based on distortion maps - [code]
Sana Alamgeer and Mylène C. Q. Farias
Frontiers Signal Processing – Image Processing (2022)
A two-stream CNN-based visual quality assessment method for light field images - [code]
Sana Alamgeer and Mylène C. Q. Farias.
Journal of Multimedia Tools and Applications (2022)
CNN-based no-reference video quality assessment method using a spatiotemporal saliency patch selection procedure - [code]
Sana Alamgeer, Muhammad Irshad, and Mylène C. Q. Farias.
Journal of Electronic Imaging - SPIE (2021)
Conferences
Using a Diverse Neural Network to Predict the Quality of Light Field Images - [code]
Sana Alamgeer, André H. M. Costa and Mylène C.Q. Farias
IEEE Workshop on Multimedia Signal Processing (2023)
Light Field Image Quality Assessment with Dense Atrous Convolutions - [code]
Sana Alamgeer and Mylène C. Q. Farias
IEEE International Conference on Image Processing - ICIP (2022)
360RAT: A Tool for Annotating Regions of Interest in 360-degree Videos
Lucas Althoff, Myllena Prado, Sana Alamgeer, Alessandro Rodrigues e Silva, Ravi Prakash, Marcelo M Carvalho, and Mylène C. Q. Farias
WebMedia '22: Proceedings of the Brazilian Symposium on Multimedia and the Web (2022)
Deep Learning-Based Light Field Image Quality Assessment Using Frequency Domain Inputs - [code]
Sana Alamgeer and Mylène C. Q. Farias
14th International Conference on Quality of Multimedia Experience - QoMEX (2022)
Long Short-Term Memory based Quality Assessment of Light Field Images - [code]
Sana Alamgeer and Mylène C. Q. Farias
IEEE International Conference on Multimedia and Expo Workshops - ICME (2022)
Sana Alamgeer, Muhammad Irshad, and Mylène C. Q. Farias
MMSys '22: Proceedings of the 13th ACM Multimedia Systems Conference (2022)
Muhammad Irshad, Camilo Sanchez-Ferreira, Sana Alamgeer, Carlos H. Llanos, and Mylène C. Q. Farias
Conference of IS&T International Symposium on Electronic Imaging (2021)
Perceptual quality assessment of enhanced images using a crowd-sourcing framework
Muhammad Irshad, Alessandro Silva, Sana Alamgeer, and Mylène C. Q. Farias
Conference of IS&T International Symposium on Electronic Imaging (2020)
Blind image quality assessment based on multiscale salient local binary patterns
Pedro Garcia Freitas, Sana Alamgeer, Welington Akamine, and Mylène C. Q. Farias
MMSys '18: Proceedings of the 9th ACM Multimedia Systems Conference (2018)
Projects
In my recent project, I designed a novel model aimed at accurately predicting regions of interest (ROIs) in 360◦ videos. The ROI is crucial for various applications such as predicting view-ports, optimizing video cuts for live streaming, and improving the overall viewing experience. The project involved training and testing a hybrid saliency model that was developed to identify the saliency regions representing the ROIs. The methodology comprised of preprocessing video frames, predicting ROIs using the hybrid saliency model, and post-processing to obtain the final output. The performance of the proposed model was then compared with the subjective annotations of the 360RAT dataset, showcasing my expertise in developing innovative solutions in the field of computer vision.
As an NLP enthusiast, I recently worked on a project where I analyzed pre-trained models on the Portuguese language to build an algorithm capable of predicting synonyms for every word. With a combination of two successful pre-trained models, I was able to achieve accurate predictions without the need to train a network from scratch. This project showcases my expertise in NLP, particularly in the Portuguese language, and highlights my ability to creatively leverage existing resources to deliver high-quality results.
Resources
Programming Tools & Libraries:
Python
Quality Assessment Databases:
Events
Upcoming Events:
Date: October 08 - 11, 2023
Location: Kuala Lumpur, Malaysia
Date: June 20 - 22, 2023
Location: Ghent, Belgium