Denk M, Rother K, Neuhäusler J, Petroll C, Paetzold K (2022)
Publication Type: Conference contribution
Publication year: 2022
Original Authors: Martin Denk, Klemens Rother, Josef Neuhäusler, Christoph Petroll, Kristin Paetzold
Pages Range: 93-103
Conference Proceedings Title: Proceedings of the Munich Symposium on Lightweight Design 2021
ISBN: 9783662652152
DOI: 10.1007/978-3-662-65216-9_9
The shape reconstruction of volumetric images can require classifying cross-sections such as circles or rectangles. Depending on the type of cross-section, shape parameters such as radius, width, or height must be regressed. This article addresses cross-section classification by convolutional neural networks (CNN), with further regression of the shape parameter using the moments of area. This fully recognizes the cross-section type and the corresponding geometric shape parameter for 2D binary images.
APA:
Denk, M., Rother, K., Neuhäusler, J., Petroll, C., & Paetzold, K. (2022). Parametrization of Cross-Sections by CNN Classification and Moments of Area Regression for Frame Structures. In Proceedings of the Munich Symposium on Lightweight Design 2021 (pp. 93-103).
MLA:
Denk, Martin, et al. "Parametrization of Cross-Sections by CNN Classification and Moments of Area Regression for Frame Structures." Proceedings of the Proceedings of the Munich Symposium on Lightweight Design 2021 2022. 93-103.
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