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This logic determines the best alignment for all the input images against a set of given 2D references in an exhaustive manner and writes them in an AliInfoIO. Alignment is typically the prerequisite for all averaging procedures.
The user has to prepare a set of images for the alignment typically by filtering and normalizing the images and a set of 2D references coming either from a projection logic or a previous classification. The logic will transform all images in polar coordinates and than exhaustively compare all images with all references in all possible rotation and shift combination using the Cross correlation coefficient as means of comparision. The best fitting parameters for every combination of images and references are written in the output. They can be applied using the appendaliinfos logic.
Here, a very specific example should be given/described. In the future, this can be supported by screenshots etc.. For the moment, give an example easy enough for the user to understand, but specific enough to elaborate why a given parameter is a good set for this very situation.
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.
| 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 |
| Input | Description |
| FirstInput | Input Description 1 |
| SecondInput | Input Description 2 |
| ThirdInput | Input Description 3: Optional Input in Italic |
| Output | Description |
| FirstOutput | Output Description |
| 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? |
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.
| 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 |
| Input | Description |
| FirstInput | Input Description 1 |
| SecondImput | Input Description 2 |
| ThridImput | Input Description 1 |
| Output | Description |
| FirstOutput | Output Description |
| 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? |
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.