Dear forum members.
I want to segment backscattered electron images from a metal surface. Due to limited computing power I split my 30k x 20k pixel images into (5x5=) 25 6k x 4k pixel images.
I need to segment small white precipitates
and classifiy them according to three different categories
round and white with black in center = titanium
round and white = carbide
elongated (at grain boundary) = delta
and in some areas I have some artifacts from sample preparation that I cannot get rid of completely:
If I got it right I should do:
pixel classification into background and precipitate
object classification into titanium, carbide or delta precipitate
whats the best workflow to exclude false positives like scratches/dirt/white areas in the background?
should I add an “artifact” label in pixel classification or rather sort things out in object classification?
How to best decide which level of sorting/false positives is okay in pixel classification and as good as I can get in object classification?
Is there a way to Export labels in object classification and import them to the object classification of another image (just like its possible to reuse labels in pixel classification)?