Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision | ||
|
eyes:logics:pcatransform [2017/06/12 16:21] flambre [Example] |
eyes:logics:pcatransform [2017/06/12 19:11] (current) jschlie1 |
||
|---|---|---|---|
| Line 1: | Line 1: | ||
| - | ====== PCA Transform ====== | + | ====== PCATransform ====== |
| This logic uses the result of the PCA logic (the eigenvectors) to transform images into a new space spanned by these eigenvectors. I.e. Images can be represented as factors of eigenvectors, a so called linear combination. | This logic uses the result of the PCA logic (the eigenvectors) to transform images into a new space spanned by these eigenvectors. I.e. Images can be represented as factors of eigenvectors, a so called linear combination. | ||
| ===== Usage ===== | ===== Usage ===== | ||
| Line 6: | Line 6: | ||
| ===== Modes/Processes ===== | ===== Modes/Processes ===== | ||
| - | ==== thisIsTheNameOfMode1 ==== | + | ==== Pca forward transform ==== |
| - | Here, a short introduction for the given mode should be placed. Again, state WHAT and WHY this mode us useful in not more than 2 sentences. | + | Here, linear factors are calculated for a given set of INPUT images using the provided eigenvectors. |
| |< 100% 30% >| | |< 100% 30% >| | ||
| ^ Parameters ^ Description ^ | ^ Parameters ^ Description ^ | ||
| - | | Some changeable parameter | Description of this parameter | | + | | Desired Output | Lets you choose the kind of output. Linear factor vectors are just 1D plots of the linear factors per eigenvector as used in the back-transform. Images with linear factors as headerkeys just append the linear factors to the header. This is used as input for example the energy landscape calculation. | |
| - | | -> and its sub-parameter | more description | | + | |
| - | | Next main parameter | and more more more | | + | |
| - | | -> and its sub-parameter | ... descriptions | | + | |
| - | |< 100% 30% >| | ||
| - | ^ Input ^ Description ^ | ||
| - | | FirstInput | Input Description 1 | | ||
| - | | SecondInput | Input Description 2 | | ||
| - | | //ThirdInput// | Input Description 3: Optional Input in Italic | | ||
| - | |< 100% 30% >| | + | ==== Pca backward transform ==== |
| - | ^ Output ^ Description ^ | + | Here the linear factor vectors are used as INPUT. The eigenvectors times the linear factors gives you the original data (minus the amount of information you discarded by choosing the number of eigenvectors). |
| - | | FirstOutput | Output Description | | + | |
| - | |< 100% 30% >| | ||
| - | ^ New/Changed Header Values ^ Description ^ | ||
| - | | headerValue1 | what does it say? how is it changed? | | ||
| - | | headerValue2 | what does it say? how is it changed? | | ||
| - | | headerValue3 | what does it say? how is it changed? | | ||
| - | | headerValue4 | what does it say? how is it changed? | | ||
| - | |||
| - | ==== thisIsTheNameOfMode2 ==== | ||
| - | Here, a short introduction for the given mode should be placed. Again, state WHAT and WHY this mode us useful in not more than 2 sentences. | ||
| - | |||
| - | |< 100% 30% >| | ||
| - | ^ Parameters ^ Description ^ | ||
| - | | Some changeable parameter | Description of this parameter | | ||
| - | | -> and its sub-parameter | more description | | ||
| - | | Next main parameter | and more more more | | ||
| - | | -> and its sub-parameter | ... descriptions | | ||
| - | |||
| - | |< 100% 30% >| | ||
| - | ^ Input ^ Description ^ | ||
| - | | FirstInput | Input Description 1 | | ||
| - | | SecondImput | Input Description 2 | | ||
| - | | ThridImput | Input Description 1 | | ||
| - | |||
| - | |< 100% 30% >| | ||
| - | ^ Output ^ Description ^ | ||
| - | | FirstOutput | Output Description | | ||
| - | |||
| - | |< 100% 30% >| | ||
| - | ^ New/Changed Header Values ^ Description ^ | ||
| - | | headerValue1 | what does it say? how is it changed? | | ||
| - | | headerValue2 | what does it say? how is it changed? | | ||
| - | | headerValue3 | what does it say? how is it changed? | | ||
| - | | headerValue4 | what does it say? how is it changed? | | ||
| ===== Concept ===== | ===== Concept ===== | ||
| - | In this paragraph, the "HOW a logic works under the hood" and WHY someone should use it can be elaborated with higher detail. Describes a scenario in an image processing workflow where this logic can be used to solve the resulting problem. Also, wikipages, publications or anything else describing the theory behind an algorithm should be linked here, if applicable. | + | Based on the eigenvectors calculated by PCA, this logic calculates linear factors or vice versa calculates "raw" images or volumes by using linear factors and the eigenvectors. However, by simply choosing low numbers of eigenvectors filtering through [[https://en.wikipedia.org/wiki/Dimensionality_reduction|Dimensionality reduction]] can be done. |