Alignment of Density Maps in Wasserstein Distance
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866909134322925568 |
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| author | Singer, Amit Yang, Ruiyi |
| author_facet | Singer, Amit Yang, Ruiyi |
| contents | In this paper we propose an algorithm for aligning three-dimensional objects when represented as density maps, motivated by applications in cryogenic electron microscopy. The algorithm is based on minimizing the 1-Wasserstein distance between the density maps after a rigid transformation. The induced loss function enjoys a more benign landscape than its Euclidean counterpart and Bayesian optimization is employed for computation. Numerical experiments show improved accuracy and efficiency over existing algorithms on the alignment of real protein molecules. In the context of aligning heterogeneous pairs, we illustrate a potential need for new distance functions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_12310 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Alignment of Density Maps in Wasserstein Distance Singer, Amit Yang, Ruiyi Image and Video Processing Quantitative Methods Machine Learning In this paper we propose an algorithm for aligning three-dimensional objects when represented as density maps, motivated by applications in cryogenic electron microscopy. The algorithm is based on minimizing the 1-Wasserstein distance between the density maps after a rigid transformation. The induced loss function enjoys a more benign landscape than its Euclidean counterpart and Bayesian optimization is employed for computation. Numerical experiments show improved accuracy and efficiency over existing algorithms on the alignment of real protein molecules. In the context of aligning heterogeneous pairs, we illustrate a potential need for new distance functions. |
| title | Alignment of Density Maps in Wasserstein Distance |
| topic | Image and Video Processing Quantitative Methods Machine Learning |
| url | https://arxiv.org/abs/2305.12310 |