In this chapter, we have covered the process of simple object detection, duplicate checking, and image matching through a two-step process of feature extraction and descriptor matching. We have practiced and extracted image features using FAST, a corner detection algorithm, and subsequently applied feature descriptors such as BRIEF, ORB, and BRISK to create and match keypoints.
Next, we proceeded with using the keypoints of the images in machine learning activities, such as clustering and classification.
Later on in this book, we will learn to achieve this by using neural networks.
