Py Table Of Contents Features

Feature Detection and Description {#tutorial_py_table_of_contents_features}

• @subpage tutorial_py_features_meaning

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

• @subpage tutorial_py_features_harris

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

• @subpage tutorial_py_shi_tomasi

  We will look into Shi-Tomasi corner detection

• @subpage tutorial_py_sift_intro

  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

• @subpage tutorial_py_fast

  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".

• @subpage tutorial_py_orb

  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.

• @subpage tutorial_py_matcher

  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.

• @subpage tutorial_py_feature_homography

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