Work Experience
Assistant Reseracher, CEOSpace Tech 2023 -
AI, Deep Learning, Machine Vision, Python
Assistant Researcher for European Training Network (ETN) MENELAOS mission. Acquiring the 3D geometry of the scene is essential for many applications in the areas of navigation, robotics, scene understanding, etc. Among the existing approaches, those using passive devices are of increased interest since they allow the use of compact, standard, and low-cost imaging systems like DSLR cameras. There are many depth cues that can be used to extract the 3D geometry. In single-shot images, the depth is lying in the blur, shadows of objects, chromatic effects shape distortions caused by lens aberrations, etc. When multiple images are used, depth information comes from perspective change like in binocular systems or structures motion in video sequences. The physics of these effects is well known and more or less accurate mathematical models exist and are used by analytical image processing methods that are generally prone to heavy calculation.
The entrance of the new Deep Neural Networks (DNN) on the stage of signal processing has boosted the subject due to their capability to learn complex models that ingest multiple effects, not only single ones as analytical approaches are doing. The flexibility in learning and the fast processing, once the training is accomplished, make DNNs a very promising tool in building the 3D geometry of scenes from easy-to-acquire images.
Study of physical foundation for depth cues in images and evaluation of their potential in existing methods for depth mapping.
Elaboration of DNN-based solutions for depth inference from single-shot images by exploiting defocus and other depth cues.
Definition of benchmarks for DNN training, validation and testing.
Evaluation of the accuracy of depth maps obtained with the DNNs using indoor and outdoor image collection.
Visiting Researcher, Ingeniería INSITU (INSITU Engineering) 2022 - 2022
AI, Deep Learning, Machine Vision, Python
Research engineer, within the Ingeniería INSITU (INSITU Engineering) team. The aim of this research stay at the University of Vigo was to explore the LiDAR and TOF cameras and develop a real dataset for Depth from Defocus. The iDFD is open source dataset that can be used for 3D indoor applications. iDFD
Contribution to open-source projects for the team.
Devlopment of iDFD dataset.
Public presentation of the project, as well as contributing to research papers.
Visiting Researcher, CiTIUS (Centro Singular de Investigación en Tecnoloxías Intelixentes) 2021 - 2021
AI, Deep Learning, Machine Vision, Python
Research engineer within the CiTIUS, working on European Training Network (ETN) MENELAOS project. This collaboration was done between the CiTIUS team for the development of 2HDED:NET.
Development of 2HDED:NET.
Training and testing of 2HDED:NET on NYU-Depth v2 and Make3D datasets for DFD and Image deblurring.
Public presentation of the project, as well as contributing to research papers.
Machine learning internship, MIDL-NCAI-HEC COMSATS University, Islamabad (Pakistan) 2017 - 2020
AI, Deep Learning, Machine Vision, Python
For my Master’s thesis, I worked on a project in collaboration with the Medical Imaging and Diagnostic Lab (MIDL) affiliated with the National
Center of Artificial Intelligence (NCAI) under the Higher Education Commission (HEC) of Pakistan.
Aim to provide Computer Aided Diagnostics (CAD) system solutions using advanced Computer Vision and Deep Learning techniques
(e.g., Discriminative Models and Generative Models specifically Generative Adversarial Networks).
Master’s research thesis “Generative Adversarial Networks for Enhancing LowDose CT scans”.
Research Interest: Artificial Intelligence, Machine Learning, Deep
Learning, Computer Vision, Generative Adversarial Networks.
