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This strategy increases the speed of calculating rotation invariant normalized cross-correlation by storing the precalculated correlation windows in the form of feature arrays.
The point sets {Pp i } and {Po i } were imposed the same centroid for calculating rotation: P p ¯ = 1 N ∑ i = 1 N P p i P ^ p i = P p i - P p ¯ P o ¯ = 1 N ∑ i = 1 N P o i P ^ o i = P o i - P o ¯ (4).
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Calculated rotation values were compared with known rotation values.
Calculated rotation values were close to actual rotation values throughout the arc of rotation.
Pearson's correlation coefficient was used to determine the correlation between the calculated rotation and the actual rotation.
Larson and Freymueller (1997) analyzed GPS data for 38 sites and were able to calculate rotation vectors for eight plates.
Calculated rotation values were close to actual rotation values throughout the arc of rotation from 60° of external rotation to 60° of internal rotation.
From 25° of external rotation to 45° of internal rotation, the calculated rotation values were within 5° of the actual rotation.
In fact, a little computer on the CJ1′s center console calculates rotation speed to the knot, based on temperature, altitude and weight aboard.
There was good correlation between actual rotation and calculated rotation values of all observers, and the intraclass correlation coefficient was 0.997.
Note that there are also direct solutions to calculate rotations and translations for 3-D point correspondences, for example the method by Horn [27].
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