I am trying to classify two kinds of images as follows:
(1)
(2)
classes can be called unscrewable/screwable. The screwable ones(1) are the ones with a slot on them, in addition to the hole, whereas the unscrewable ones only contain a hole.
There are two ways of solving this problem.
1. Train a network.
2. Use edge information.
I tried to use sobel derivates and Canny but one can't implement something that works for all, I mean, anything that is threshold-dependent is not so reliable, I try to avoid using such methods, therefore. What works for these two images, does not work for more and etc. Perhaps sobel derivates weren't the best choice for this type, I don't know. Hence, my question.
I was curious if anyone here try to achieve something similar. The difference between these two classes are really little, and I wonder if it makes sense to use purely traditional image processing techniques found in OpenCV or to deploy a neural network. I suspect haar classifier wouldn't be able to tackle this either.
What do you think?
↧