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eyes:logics:classification [2017/06/06 17:21] nfische [Usage] |
eyes:logics:classification [2017/06/06 17:26] nfische [Modes] |
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| | 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 | | ||