dave_user
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By looking at the acquired images, it is interesting to note that most defect features are distributed in the solder region in the component and in particular two defects belonging to two different classes can be easily distinguished by looking at the two soldering regions. This means that both can be potentially used for training a ML model for a classification problem, while all the other parts in the image can be discarded without losing information and accuracy.
{| style="background:transparent; color:black" border="0" align="center" cellpadding="10px" cellspacing="0px" height="550" valign="bottom"|- align="center"||[[File:Q2-1-C87 missing defect sample.png|thumb|350x350px|Original ''missing'' sample, ''full'' typology]]||[[File:Q2-1-C87 upper side solder region.png|thumb|350x350px|''upper'' side ROI solder region]]||[[File:Q2-1-C87 lower side solder region.png|thumb|350x350px|''lower'' side ROI solder regiont]]|}
==Data augmentation with image synthesis==
