It’s
very tempting to use subtraction from a “perfect image” to find
defects. Unfortunately though, this usually results in a lot of false
rejects. The reasons are many and various, but Ruben Uribe has done
an excellent job of summarizing and explaining in “Change
Detection for Machine Vision Applications”.
Published
on the Quality Magazine website December 10th,
2013, (registration required, but definitely worthwhile,) this also
suggests some steps you can take to improve image subtraction
approaches. The conclusion however seems to be that none of them are
much good. Instead, Mr. Uribe suggests going with “light invariant”
approaches.
Anyone
for 3D?
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