Nicolas Cubaud On Building Trustworthy Computer Vision Systems
AI Standards Stack #09
Podcast
Background: In this episode, hosts Michael Mainelli and Adam Leon Smith welcome Nicolas Cubaud, founder and CEO of EyeSnap and co-founder of TotalYmage, editor of the preliminary European norm (prEN) 18281 on artificial intelligence, evaluation methods for accurate computer vision systems. With over 25 years of experience in computer vision and image processing, Nicolas discusses how iSnap turns images and videos into actionable business insights for industrial and regulated sectors, such as nuclear conformity assessments and workplace safety monitoring (e.g., detecting proper PPE in hazardous areas).
The conversation explores the development of prEN 18281 (evaluation methods) and its companion prEN 18288 (taxonomy of computer vision tasks), covering classification, detection, segmentation, tracking, pose estimation, image/video generation, and metrics like IoU. Nicolas explains how these standards translate high-level EU AI Act requirements, such as accuracy, robustness, and cybersecurity, into technically measurable, reproducible, and auditable processes tailored to specific intended purposes and operating conditions.
Topics include alignment between European harmonized standards and international (ISO/IEC) efforts, jurisdictional differences (e.g., biometrics and fundamental rights), and the role of standards in building customer trust. The episode offers practical advice for companies and entrepreneurs developing computer vision systems, emphasizing early engagement with standards as an opportunity for better products beyond mere compliance.
Guest:
Nicolas Cubaud is founder and CEO of EyeSnap and co-founder of TotalYmage. He serves as the editor of the European standard prEN 18281, Artificial Intelligence, Evaluation methods for accurate computer vision systems, developed under CEN-CENELEC JTC 21 in support of the EU AI Act. With over 25 years of experience in computer vision and image processing, Nicolas leads the European standardization project focused on evaluation methods and accuracy criteria for AI-powered computer vision systems.
