TR2021-098
Learning-Based THz Multi-Layer Imaging for High-Capacity Positioning
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- , "Learning-Based THz Multi-Layer Imaging for High-Capacity Positioning", International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), DOI: 10.1109/IRMMW-THz50926.2021.9566940, November 2021.BibTeX TR2021-098 PDF
- @inproceedings{Wang2021nov,
- author = {{Wang, Pu and Koike-Akino, Toshiaki and Ma, Rui and Orlik, Philip V. and Yamashita, Genki and Tsujita, Wataru and Nakajima, M.}},
- title = {Learning-Based THz Multi-Layer Imaging for High-Capacity Positioning},
- booktitle = {International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz)},
- year = 2021,
- month = nov,
- publisher = {IEEE},
- doi = {10.1109/IRMMW-THz50926.2021.9566940},
- issn = {2162-2035},
- isbn = {978-1-7281-9424-0},
- url = {https://www.merl.com/publications/TR2021-098}
- }
- , "Learning-Based THz Multi-Layer Imaging for High-Capacity Positioning", International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), DOI: 10.1109/IRMMW-THz50926.2021.9566940, November 2021.
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MERL Contacts:
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Research Areas:
Computational Sensing, Machine Learning, Optimization, Signal Processing
Abstract:
This paper demonstrates a learning-based THz multi-layer pixel identification for contactless three-dimensional (3-D) positioning and encoders. More specifically, we propose a one-dimensional convolution-based residual network to deal with practical issues including 1) depth variation, 2) shadow effect, and 3) content recognition at the back surface of each layer. Experimental validation on a three-layer sample with contents on all surfaces is also provided.