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eyes:logics:optimizereferences [2017/06/08 13:36]
jschlie1
eyes:logics:optimizereferences [2017/06/12 18:36] (current)
jschlie1
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 ====== OptimizeReferences ====== ====== 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 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 ===== ===== Usage =====
-The quality of the output heavily depends on the quality of the first class average chosen - preferrably, it should show enough details of the molecule or complex and be surronded ​by a black rim. The optimal values for radius parameter are around 0.8-0.9. ​FIXME+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 ===== +===== Parameters ​=====
-FIXME +
- +
-===== Process ​=====+
 |< 100% 30% >| |< 100% 30% >|
 ^ Parameters ​                ^ Description ​    ^ ^ Parameters ​                ^ Description ​    ^
 | Correlation Function ​      | The correlation function that is optimized during the image alignment | | Correlation Function ​      | The correlation function that is optimized during the image alignment |
-| Fraction ​                  | The maximal fraction of the image that will be shifted relative to reference image during the alignment |+| Fraction ​                  | The maximal fraction of the image that will be shifted relative to the reference image during the alignment |
 | Interpolation ​             | Defines the interpolation function used to get the value between two neighbouring pixels | | Interpolation ​             | Defines the interpolation function used to get the value between two neighbouring pixels |
-| Radius ​                    | The value of Circular Mask Fraction |+| 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 | | Sampling ​                  | The maximal fraction of the image shifted during the alignment relative to the reference image |
- 
-|< 100% 30% >| 
-^ Input   ^ Description ^ 
-| Input    | A set of class averages ​ | 
- 
-|< 100% 30% >| 
-^ Output ​  ^ Description ^ 
-| Output ​ | A set of aligned class averages | 
- 
-|< 100% 30% >| 
-^ Written Header Values ​  ^ Description ^ 
-| resolutionAt0.143 | Estimated resolution for FSC cut off at 0.143 | 
-| resolutionAt0.3 | Estimated resolution for FSC cut off at 0.3 | 
-| resolutionAt0.5 | Estimated resolution for FSC cut off at 0.5 | 
-| resolutionAt0.9 | Estimated resolution for FSC cut off at 0.9 | 
-| resolutionAt3Sigma | Estimated resolution for FSC cut off at 3 Sigma | 
-| resolution | Estimated resolution at the user chosen cut off value | 
-| maxResolution | Maximum resolution in the local map | 
-| meanResolution | Mean resolution in the local map | 
-| minResolution | Minimum resolution in the local map | 
-| resolutionVariance | Variance of the resolution in the local map | 
-| abscissaIsLogarithmical , abscissaMin,​ abscissaMax | For internal 1D viewer use only, to be hidden | 
-FIXME 
  
 ===== Concept ===== ===== Concept =====
-A Paper about FSC theory ​and applications can be found [[http://www.sciencedirect.com/science/article/​pii/​S1047847705001292|here]] FIXME+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. ​