Feature Detection and Description {#tutorial_py_table_of_contents_features}
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-   @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.
