Summary

In this chapter, we discussed a few important feature detection and extraction techniques to compute different types of feature descriptors from an image using Python's scikit-image and cv2 (python-opencv) libraries. We started with the basic concepts of local feature detectors and descriptors for an image, along with their desirable properties. Then we discussed the Harris Corner Detectors to detect corner interest points of an image and use them to match two images (with the same object captured from different viewpoints). Next, we discussed blob detection using LoG/DoG/DoH filters. Next, we discussed HOG, SIFT, ORB, BRIEF binary detectors/descriptors and how to match images with these features. Finally, we discussed Haar-like features and face detection with the Viola—Jones algorithm. By the end of this chapter, you should be able to compute different features/descriptors of an image with Python libraries. Also, you should be able to match images with different types of feature...