edoc

3D unknown view tomography via rotation invariants

Zehni, Mona and Huang, Shuai and Dokmanić, Ivan and Zhao, Zhizhen. (2020) 3D unknown view tomography via rotation invariants. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 1449-1453.

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Official URL: https://edoc.unibas.ch/81807/

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Abstract

In this paper, we study the problem of reconstructing a 3D point source model from a set of 2D projections at unknown view angles. Our method obviates the need to recover the projection angles by extracting a set of rotation-invariant features from the noisy projection data. From the features, we reconstruct the density map through a constrained nonconvex optimization. We show that the features have geometric interpretations in the form of radial and pairwise distances of the model. We further perform an ablation study to examine the effect of various parameters on the quality of the estimated features from the projection data. Our results showcase the potential of the proposed method in reconstructing point source models in various noise regimes.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Data Analytics (Dokmanic)
UniBasel Contributors:Dokmanić, Ivan
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Publisher:IEEE
ISBN:978-1-5090-6632-2
e-ISBN:978-1-5090-6631-5
ISSN:1520-6149
e-ISSN:2379-190X
Note:Publication type according to Uni Basel Research Database: Conference paper
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Last Modified:16 Feb 2021 11:39
Deposited On:16 Feb 2021 11:39

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