Difference between revisions of "Cross correlation matrix"
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# rotate missing wedges -> R<sub>i</sub>W<sub>i</sub>, R<sub>j</sub>W<sub>j</sub> | # rotate missing wedges -> R<sub>i</sub>W<sub>i</sub>, R<sub>j</sub>W<sub>j</sub> | ||
# compute Fourier coefficients common to bCoth rotated missing wedges | # compute Fourier coefficients common to bCoth rotated missing wedges | ||
| − | C<sub>ij</sub> = intersection(R<sub>i</sub>W<sub>i</sub>, R<sub>j</sub>W<sub>j</sub>) | + | #:C<sub>ij</sub> = intersection(R<sub>i</sub>W<sub>i</sub>, R<sub>j</sub>W<sub>j</sub>) |
# filter p<sub>i</sub> with C<sub>ij</sub> | # filter p<sub>i</sub> with C<sub>ij</sub> | ||
# compare the filtered particles inside the classification mask. | # compare the filtered particles inside the classification mask. | ||
Revision as of 11:43, 19 April 2016
The cross correlation matrix (often called ccmatrix in Dynamo jargon) of a set of N particles is an N X N matrix. Each entry (i,j) represents the similarity of particles i and j in the data set.
Contents
Definition of similarity
This similarity of particles i and j measured in terms of the normalized cross correlation of the the two aligned particles, filtered to their common fourier components, and restricted to a region in direct space (indicated by a classification mask). The pseudo code will run as:
- read particles i and j -> pi, pj
- align particles i, and j -> Aipi, Ajpj
- compute missing wedges for particles i and j -> Wi, Wj
- rotate missing wedges -> RiWi, RjWj
- compute Fourier coefficients common to bCoth rotated missing wedges
- Cij = intersection(RiWi, RjWj)
- filter pi with Cij
- compare the filtered particles inside the classification mask.
