Patrick Follmann
Papers
1
Total Citations
19
H-Index
1
About
Patrick Follmann is a leading researcher in industrial machine vision, specializing in shape-based matching and deep-learning-driven object detection. His work bridges classic computer vision techniques with modern neural network approaches, addressing critical challenges in robot navigation, precision measurement, and automated grasping. Follmann’s most cited paper, "A comparison of shape-based matching with deep-learning-based object detection" (2019, 19 citations), provides a rigorous benchmark between traditional edge-based matching and contemporary deep learning methods for 2D pose estimation. This study has become a key reference for practitioners seeking to optimize real-world vision systems, offering clear guidance on when to deploy classical algorithms versus deep models. Beyond this comparative work, Follmann’s research advances the robustness and efficiency of object localization, directly impacting industrial automation and robotics. His contributions are recognized for their practical utility, helping engineers select appropriate techniques for high-stakes applications where accuracy and speed are paramount. Follmann’s work continues to shape the evolution of machine vision, blending foundational principles with cutting-edge innovation.
Research Focus
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Top Papers
- 1