Projects
This work was originally conceived as my master thesis. It was later accepted and selected for an oral presentation at the NIPS 2016 Workshop on Adversarial Training.
What if you could take a picture of your face and have a neural network arbitrarily change how you look? This is exactly what the IcGAN does using the power of Generative Adversarial Networks. It doesn't apply to faces only, but to whatever image dataset you train with.
My teammates and I analyze different approaches to effectively monitor traffic. Considering the limitations of the real world (e.g. camera jittering, dynamic background), we use computer vision techniques to track vehicles and estimate their speed with an RGB camera.
We built a detector and classifier able to recognize 15 different types of traffic signs with 81.05% precision and 75.68% F1-Score. We experimented with hand-crafted features and neural networks.
Awarded as the best final degree project.
Given an aerial video of a terrain captured with a UAV equipped with a sensor, I learn and explore different techniques to accurately represent the map where the UAV has been.