Simplification of Interest Point detection and usage

Currently, for each pair of tiles, the overlapping are is computed, and inside an interest point detection takes place.

Algorithmic description:

• While running xuvstitch, for every tested pair of stacks (stackX, stackY), do:
  • Compute start and end points of overlapping area: range R
  • Let stackX be the stack with higher average brightness in range R
  • Compute voxel gradient magnitude in range R of stackX: gradmagX_R
  • downscale gradmagX_R by the size of the moving window: scaled_gradmagX_R
  • find the top N values and their positions in scaled_gradmagX_R: interest_points

Algorithmic description:

• Prior to starting xuvstitch, for every stack (stackX), do:
  • Compute voxel gradient magnitude of stackX: gradmagX
  • downscale gradmagX by the size of the moving window: scaled_gradmagX

The proposed concept will require more memory than the current concept. However, repeated computation of the 'average brightness', the 'gradient magnitudes' and the downscaling is omitted.