Youssef EL ASSFOURI

– Led camera-based sub-pixel metrology campaigns for aircraft flight tests, delivering high-precision structural deformation and kinematic measurements used for aircraft design validation and certification, while coordinating up to 3 engineers. – Developed custom Python 2D/3D computer vision pipelines for in-flight structural and kinematic analysis, including pose estimation, 3D reconstruction, detection, identification, tracking, registration, and sub-pixel keypoint extraction, using data from 3D scanners, LiDAR, event-based, infrared, and RGB cameras. – Collaborated with cross-functional teams in structures, flight test, and R&D to define vision-system requirements and deliver tailored measurement solutions. – Contributed to an internal Airbus computer vision Python library by developing an optimized circular coded target decoding module. – Calibrated mono, stereo, and infrared camera systems for high-accuracy measurement applications. – Evaluated and implemented computer vision tools, SLAM approaches, and edge AI solutions to improve workflow efficiency and measurement performance.

– Designed and implemented an event detection algorithm for low Signal-to-Noise Ratio (SNR) recordings of aircraft background noise, enabling automated identification of acoustic anomalies. – Documented results and delivered technical reports to stakeholders.

– Developed a real-time Python video game controlled by hand gestures, achieving 30.7 FPS hand detection without GPU acceleration. – Implemented a hand tracking pipeline using a pre-trained Convolutional Neural Network (CNN) based on SSD architecture via Google MediaPipe. – Designed the computer vision and deep learning inference pipeline for real-time gesture classification and game control.