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 ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Read now
Unlock full access