Thomas Werner

Fraunhofer Institute for Intelligent Analysis and Information Systems

Papers

Total Citations

H-Index

About

Thomas Werner is a computer vision researcher whose work bridges the gap between automated infrastructure inspection and practical engineering needs. His primary research focuses on developing algorithms for the automatic analysis of sewer pipe systems, a critical yet underexplored application of computer vision. Werner's most notable contribution is a system that detects and classifies structural damages in sewer pipes using only low-quality, heavily compressed fisheye images captured by inspection robots. By unrolling these distorted monocular images, his approach enables robust damage recognition without requiring expensive or specialized hardware. This work, published in 2018 and garnering 24 citations, demonstrates how computer vision can transform routine municipal maintenance, reducing reliance on manual inspection and improving public infrastructure safety. Werner's research exemplifies the real-world impact of applying advanced vision techniques to challenging, non-ideal data—a hallmark of his career. His contributions are particularly valuable for students and researchers interested in domain-specific computer vision, where algorithmic innovation must contend with real-world constraints like image compression, poor lighting, and geometric distortion.