Feature Detection and Description

• Understanding Features

  What are the main features in an image? How can finding those features be useful to us?

• Harris Corner Detection

  Okay, Corners are good features? But how do we find them?

• Shi-Tomasi Corner Detector & Good Features to Track

  We will look into Shi-Tomasi corner detection

• Introduction to SIFT (Scale-Invariant Feature Transform)

  Harris corner detector is not good enough when scale of image changes. Lowe developed a breakthrough method to find scale-invariant features and it is called SIFT

• FAST Algorithm for Corner Detection

  All the above feature detection methods are good in some way. But they are not fast enough to work in real-time applications like SLAM. There comes the FAST algorithm, which is really “FAST”.

• ORB (Oriented FAST and Rotated BRIEF)

  SURF is good in what it does, but what if you have to pay a few dollars every year to use it in your applications? Yeah, it is patented!!! To solve that problem, OpenCV devs came up with a new “FREE” alternative to SIFT & SURF, and that is ORB.

• Feature Matching

  We know a great deal about feature detectors and descriptors. It is time to learn how to match different descriptors. OpenCV provides two techniques, Brute-Force matcher and FLANN based matcher.

• Feature Matching + Homography to find Objects

  Now we know about feature matching. Let’s mix it up with 3d module to find objects in a complex image.