Seed oversampling is a technique used in models that parametrise overall region where actual particles will be located, as membranes or pseudo crystals. The crop_points generated by the models after the geometric computation stage are not supposed to realistically be centered on a copy of the macromolecule of interest.
One possible procedure is to define a distribution of boxes denser than the expected distribution of physical particles. This way, after particle extraction you will end up end up with more subtomograms than actual particles, but you'll make sure that all the particles are actually included in some of the subtomograms. This might require some planning ahead, playing with the relationship between the expected physical distance between the particles, the sampling distance that you impose through the model, the physical size of the particle and the sidelength that you ask for the particle cropping.
Oversampling in an alignment project
When you crop your particles using seed oversampling, you should try to avoid carrying all the "extra" subtomograms along the iteration procedure.
The function that "filters" the refined table created by an iteration is dpktbl. exclusionPerVolume. You can use it to analize the results of an iteration manually.
Note that this function has not been optimized
function [newTable,o] = exclusionPerVolume(table,distanceThreshold,columnVolume,columnCC)