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Explore the fundamentals and advanced concepts of robust visual recognition in this keynote lecture delivered by renowned expert Jiri Matas at the AI Doctoral Academy's AICET2025 conference. Delve into cutting-edge techniques and methodologies that enable computer vision systems to maintain high performance under challenging conditions such as varying lighting, occlusion, noise, and adversarial attacks. Learn about the theoretical foundations of robust feature extraction, invariant descriptors, and resilient classification algorithms that form the backbone of reliable visual recognition systems. Discover how modern deep learning approaches can be enhanced with robustness principles to create more dependable AI systems for real-world applications. Examine case studies and practical examples that demonstrate the implementation of robust visual recognition in domains such as autonomous vehicles, medical imaging, surveillance systems, and industrial automation. Gain insights into current research challenges, emerging trends, and future directions in the field of robust computer vision from one of the leading researchers in the area.
