Summary
Things to take away from this lecture
•What
is an edge and where does it come from
•Edge
detection by differentiation
•Image
gradients
–continuous
and discrete
–filters
(e.g., Sobel operator)
•Effects
of noise on gradients
•Derivative
theorem of convolution
•Derivative
of Gaussian (DoG) operator
•Laplacian
operator
–Laplacian
of Gaussian (LoG)
•Canny
edge detector (basic idea)
–Effects
of varying sigma parameter
•Approximating
an LoG by subtraction
•Hough
Transform