Physical Parameter Estimation from Images
Abstract
The goal of this project is to develop fast image-based measurement methods for optical properties, which would help to close feedback loops in adaptive manufacturing.
The introduction of novel production techniques for integrated optical components demands an increasing amount of quality control and inline feedback. Our focus in this project is the combination of fast optical measurement techniques and physics-based simulations to achieve fast and accurate feedback of physical parameters as close to the machine tool as possible.
The research on this topic is done in collaboration with the PhoenixD Cluster of Excellence. We work closely with expert researchers from other disciplines under the Task Group F2: Expert Systems for Quality Control.
Publications
N-SfC: Robust and Fast Shape Estimation from Caustic Images
in Proc. Vision, Modeling and Visualization (VMV), The Eurographics Association, pp. 33-41, September 2023.
N-SfC: Robust and Fast Shape Estimation from Caustic Images
arXiv preprint, December 2021.
url: https://arxiv.org/abs/2112.06705
Method for fast determination of the angle of ionizing radiation incidence from data measured by a Timepix3 detector
in Journal of Sensors and Sensor Systems, vol. 10, pp. 63-70, March 2021.
Shape from Caustics: Reconstruction of 3D-Printed Glass from Simulated Caustic Images
in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 2877-2886, January 2021.
Dreaming Neural Networks for Adaptive Polishing
in Proc. Int. Conf. European Society for Precision Engineering and Nanotechnology (euspen), euspen, pp. 263-266, June 2020.
Optical Quality Control for Adaptive Polishing Processes
in Proc. IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 90-94, March 2020.
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