Image fitting with 2D-normal distribution
Investigate performance of image fitting with 2D-normal distribution.
Possible benefits are:
- calculation of errors on image parameters (e.g., on centroid position or image orientation)
- less dependent on camera pixelisation and gaps between the sensitive area in the camera
Drawbacks:
- much slower than standard image calculation
- loss of events due to unsuccessful fits?
Development steps:
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integrate time gradient into image fitting for sufficiently large images with time gradient measured -
use image parameter errors for stereo reconstruction -
geometrical method using image errors as weight -
dispBDT: -
add errors to improve prediction of disp -
add errors to improve prediction of dispError
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calculate in all cases the expected error on the stereo position from error propagation
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