Building Computer Vision Projects with OpenCV 4 and C++
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Feature extraction

As we discussed earlier, the human visual system tends to extract the salient features from a given scene to remember it for retrieval later. To mimic this, people started designing various feature extractors that can extract these salient points from a given image. Popular algorithms include Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Features From Accelerated Segment Test (FAST).

An OpenCV module called features2d provides functions to detect and extract all these features. Another module called xfeatures2d provides a few more feature extractors, some of which are still in the experimental phase. You can play around with these if you get the chance.

There is also a module called bioinspired that provides algorithms for biologically-inspired computer vision models.