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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?

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?

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. similar in Python3. 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?