Differences
This shows you the differences between two versions of the page.
Both sides previous revision Previous revision Next revision | Previous revision Last revision Both sides next revision | ||
eyes:logics:classification [2017/06/06 17:21] nfische [Usage] |
eyes:logics:classification [2017/06/06 17:32] nfische [Concept] |
||
---|---|---|---|
Line 16: | Line 16: | ||
| Eigen images location | Define, whether the Eigen images used for complexity reduction are generated on-the-fly internally (intern) or provided by an input from en external source (extern) | | | Eigen images location | Define, whether the Eigen images used for complexity reduction are generated on-the-fly internally (intern) or provided by an input from en external source (extern) | | ||
| -> Number of eigen images | How many Eigen images (and therefore dimensions) should be used as components during linear combination | | | -> Number of eigen images | How many Eigen images (and therefore dimensions) should be used as components during linear combination | | ||
- | | Split up method | Determine, whether large classes should be slip up into smaller classes due to their number of containing objects (size) or due to their high internal variance of the cross-correlation-coefficients (cccVariance) | | + | | Split up method | Determine, whether large classes should be split into smaller classes i) to obtain classes containing a similar number of images/volumes (Cluster size) or ii) to minimize internal variance within each class, as measured by the cross-correlation-coefficients (cccVariance) | |
| Number of classes | Number of resulting classes/clusters | | | Number of classes | Number of resulting classes/clusters | | ||
| Remove duplicated images | Duplicate images identified by the classification are removed | | | Remove duplicated images | Duplicate images identified by the classification are removed | | ||
Line 35: | Line 35: | ||
===== Concept ===== | ===== Concept ===== | ||
- | Once clusters/classes of images are found, they can be averaged (see [[eyes:logics:SumByClassNumber]]) to improve the signal-to-noise-ration substantially.((van Heel, M. (1984). Multivariate statistical classification of noisy images (randomly oriented biological macromolecules). Ultramicroscopy, 13(1-2), 165–83. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/6382731)) | + | Once clusters/classes of images are found, they can be averaged (see [[eyes:logics:SumByClassNumber]]) to improve the signal-to-noise-ratio substantially.((van Heel, M. (1984). Multivariate statistical classification of noisy images (randomly oriented biological macromolecules). Ultramicroscopy, 13(1-2), 165–83. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/6382731)) |