2015Anden Covariance: Difference between revisions
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== Abstract == | == Abstract == | ||
Extension of the previous method of Katsevich, Katsevich, and Singer for estimating the 3D covariance matrix of a heterogeneous population of volumes. The method is based on conjugate gradient for solving a sparse linear system, and allows for incorporation of contrast transfer function and non-uniform distribution of viewing angles, making it more suitable for real-world data. Performance is evaluated on a synthetic dataset and on an experimental dataset obtained by imaging a 70S ribosome complex. | |||
== Keywords == | == Keywords == | ||
Revision as of 19:47, 18 March 2015
Citation
J. Andén, E. Katsevich, A. Singer, ``Covariance estimation using conjugate gradient for 3D classification in Cryo-EM”, 12th IEEE International Symposium on Biomedical Imaging (ISBI 2015) arXiv
Abstract
Extension of the previous method of Katsevich, Katsevich, and Singer for estimating the 3D covariance matrix of a heterogeneous population of volumes. The method is based on conjugate gradient for solving a sparse linear system, and allows for incorporation of contrast transfer function and non-uniform distribution of viewing angles, making it more suitable for real-world data. Performance is evaluated on a synthetic dataset and on an experimental dataset obtained by imaging a 70S ribosome complex.