Is there a deep learning methods that uses feature point (SURF, SIFT, ORB) with its scale factor and descriptor? OR why neural networks not interacts with standard computer vision algorithms?
If I see you question correctly, I do not see any restriction to feed raw features (extracted with CV methods) to any Deep Algorithm. In that way you are still possible to learn combinatorial information (correlations in other perspective) from those features. Also other than learning the features, you might use pre-training step to find good start point for your NN learning like in the case of Deep Belief Nets
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