Maria Klodt

Technical University of Munich, Fraunhofer Institute for Intelligent Analysis and Information Systems

Papers

Total Citations

183

H-Index

About

Maria Klodt is a leading researcher in computer vision and plant phenotyping, whose work bridges real-time visual attention systems and agricultural technology. Her most influential contribution is "A Real-Time Visual Attention System Using Integral Images" (2019, 119 citations), which addresses the computational bottleneck of scale-invariant feature extraction for applications like robotics and image preprocessing. She further advanced field phenotyping with "Field phenotyping of grapevine growth using dense stereo reconstruction" (2015, 50 citations), introducing sensor-based, non-invasive methods to overcome the "phenotypic bottleneck" in high-throughput plant research. Klodt also pioneered affordance-inspired robotics in "GPU-accelerated affordance cueing based on visual attention" (2007, 14 citations), integrating attention cues into perception layers for more intuitive robot-environment interaction. Her work has practical impacts on precision agriculture and autonomous systems, demonstrating how efficient visual processing can transform both biological research and robotics. With over 180 total citations, Klodt’s research continues to influence fields from grapevine monitoring to real-time scene understanding, making her a key figure in applied computer vision.