Table 1 Surrogate data parameters: a random Euclidean transformation determined by parameters (r, s, u) is applied to a set of n objects U to produce a new set of objects V. To simulate noise, p objects are added at random locations to both U and V
From: A random-sampling approach to track cell divisions in time-lapse fluorescence microscopy
| Param | Description |
|---|---|
| \(\varepsilon\) | Minimum object separation |
| n | Number of points to generate in the point-set U |
| p | Number of “noise” points to add to the point-set U and the point-set V |
| r | Generate random (global) rotations in the range [\(-\pi /r\), \(\pi /r\)] |
| s | Generate random (global) translations in the range [0, s] |
| u | Random uniform shift of points in V on each axis in the range [0, u] |