Jens Timo Neumann
Papers
1
Total Citations
18
H-Index
1
About
Jens Timo Neumann is a researcher at the forefront of applying deep learning to industrial non-destructive testing, with a primary focus on 3D X-ray imaging. His most cited work, "Automated Attribute Measurements of Buried Package Features in 3D X-ray Images using Deep Learning" (2021, 18 citations), demonstrates a pioneering approach to automating the detection and segmentation of buried structures—such as through-hole vias and other package features—in complex volumetric scans. By leveraging state-of-the-art deep learning models originally developed for robotics and medical imaging, Neumann has adapted these techniques to solve critical challenges in quality control and reverse engineering for the electronics and manufacturing industries. His contributions enable precise, automated attribute measurements that were previously manual and error-prone, significantly advancing the reliability of 3D X-ray inspection. With a growing citation record, Neumann’s work bridges the gap between cutting-edge AI and practical industrial applications, marking him as a key innovator in the field of automated defect detection and metrology.
Research Focus
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Top Papers
- 1