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--- eyes:logics:optimizereferences [2017/06/08 13:18]
ksingh2
+++ eyes:logics:optimizereferences [2017/06/12 15:20]
vzinch [Process]
@@ Line 1: / Line 1: @@
-====== Optimize References ======
+====== OptimizeReferences ======
-This logic aligns the class averages, so that they are in the same direction.
+This logic aligns class averages so that they share the same mutual orientation.
-After classification step different classes might contain the same view, but rotated in the image plane, so alignment is needed for the same views to look identical.
+After a classification step different classes might contain the same view, but rotated in the image plane (i.e. different α-angles), so an alignment is needed to optimize the class averages before using them as references for image alignment.
 ===== Usage =====
-Here, a general/generic description of HOW the logic is USED should be given. Try to be as general as possible, but also mention prerequisites, restrictions, advantages, requirements which are specific of this logic. Basically everything the user needs to know to successfully use this logic.
+The quality of the output heavily depends on the quality of the first class average chosen - preferably, it should show enough details of the molecule or complex and be surrounded by a black rim.
+The optimal values suggested for radius parameter are around 0.8-0.9.
-===== Example ====
-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.
-===== Modes/Processes =====
-==== thisIsTheNameOfMode1 ====
-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.
+===== Process =====
 |< 100% 30% >|
 ^ Parameters                 ^ Description     ^
-| Some changeable parameter  | Description of this parameter |
+| Correlation Function       | The correlation function that is optimized during the image alignment |
-| -> and its sub-parameter   | more description |
+| Fraction                   | The maximal fraction of the image that will be shifted relative to the reference image during the alignment |
-| Next main parameter        | and more more more |
+| Interpolation              | Defines the interpolation function used to get the value between two neighbouring pixels |
-| -> and its sub-parameter   | ... descriptions |
+| Radius                     | The value of Circular Mask Fraction (this fraction of image dimension will be used as diameter of the circle cut from the image for cf calculations)|
+| Sampling                   | The maximal fraction of the image shifted during the alignment relative to the reference image |
 |< 100% 30% >|
 ^ Input   ^ Description ^
-| FirstInput  | Input Description 1 |
+| Input    | A set of class averages  |
-| SecondInput | Input Description 2 |
-| //ThirdInput//  | Input Description 3: Optional Input in Italic |
 |< 100% 30% >|
 ^ Output   ^ Description ^
-| FirstOutput | Output Description |
+| Output  | A set of aligned class averages |
-|< 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 =====
-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.
+The logic takes a stack of class average images as input. It applies the circular mask of radius changed by parameter 'Radius' to every image, so that only the defined circle and not the surrounding noise will be used for correlation calculations. Then the logic aligns every image to the first one by shifting them in [[https://en.wikipedia.org/wiki/Polar_coordinate_system|polar coordinates]] (that corresponds to rotation of image in Cartesian coordinates) and maximizing the correlation function.