In order to understand Harris Corner Detection, let us consider a
grayscale image. Sweep a window w(x,y) (with displacements u in
the x-direction and v in the y-direction), I calculates the
variation of intensity w(x,y).
Where:
w(x,y)is the window position at (x,y)I(x,y)is the intensity at (x,y)I(x+u,y+v)is the intensity at the moved window(x+u,y+v).
Since we are looking for windows with corners, we are looking for windows with a large variation in intensity. Hence, we have to maximize the equation above, specifically the term:
Using Taylor expansion:
Expanding the equation and cancelling I(x,y) with -I(x,y):
The above equation can be expressed in a matrix form as:
So, our equation is now:
A score is calculated for each window, to determine if it can possibly contain a corner:
Where,
