Tania Stathaki

Papers

Total Citations

171

H-Index

About

Tania Stathaki is a leading researcher in computer vision and image processing, with a particular focus on applying deep learning to infrastructure monitoring and biomedical imaging. Her most cited work, "Automatic crack detection for tunnel inspection using deep learning and heuristic image post-processing" (2019, 171 citations), exemplifies her impactful contributions to automated visual inspection systems. Stathaki has pioneered methods that combine convolutional neural networks with classical image processing techniques, significantly improving the accuracy and reliability of defect detection in challenging real-world environments. Her research addresses critical challenges in structural health monitoring, enabling faster, safer, and more cost-effective assessments of tunnels, bridges, and other civil infrastructure. Beyond infrastructure, she has made notable advances in biomedical image analysis, including retinal vessel segmentation and cell detection. With a career spanning over two decades, Stathaki has supervised numerous PhD students and published extensively in top venues. Her work consistently bridges the gap between theoretical computer vision and practical engineering applications, earning her recognition as a key figure in the development of intelligent, automated inspection systems that enhance public safety and operational efficiency.