Features
– parts of an image that encode it in a compact form
Edges
edges in an image
surface normal, depth, surface color, illumination
information theory view: edges encode change, therefore edges efficiently encode an image
Edges appear as ridges in the 3D height map of an image
Edge Detection
Basic Idea
Look for a neighborhood with strong signs of change
Derivatives of F(x,y) to get Edges
Need an operation that when applied to an image returns its derivatives
model these “operatiors” as mask/kernel
then “threshold” this gradient function to select edge pixels
Gradient of an image = measure of change in Image function.
ΔF = [δF/δx,δF/δy]
measure of change in image function (F) in x and y
Gradient direction is
Θ = tan^-1[δF/δx,δF/δy]
