DIGITAL IMAGE PROCESSING
Lecture 3 Scale-space, Colors Tammy Riklin Raviv Electrical and Computer Engineering Ben-Gurion University of the Negev Last Class: Filtering Partial Derivatives
First order partial derivatives:
@I(x, y) = I(x +1,y) I(x, y) Left Derivative @x
@I(x, y) = I(x, y) I(x 1,y) Right Derivative @x
@I(x, y) I(x +1,y) I(x 1,y) = Central Derivative @x 2 Partial Derivatives
First order partial derivatives:
@I(x, y) = I(x, y + 1) I(x, y) @y
@I(x, y) = I(x, y) I(x, y 1) @y
@I(x, y) I(x, y + 1) I(x, y 1) = @y 2 Partial Derivatives Partial Derivatives Partial Derivatives Gradients
Gradients: @I @I I = , r @x @y ⇣ ⌘
@I 2 @I 2 I = + |r | s @x @y ⇣ ⌘ ⇣ ⌘ Gradients Gradients
Can you explain the differences? Gradients
You get long function but here is the important part: Derivatives & the Laplacian
• Second order derivatives @2I @2I 2I = + r @x2 @y2 @2I = I(x +1,y)+I(x 1,y) 2I(x, y) @x2
@2I = I(x, y + 1) + I(x, y 1) 2I(x, y) @y2
2I = I(x +1,y)+I(x 1,y)+I(x, y + 1) + I(x, y 1) 4I(x, y) r Divergence
Let x, y be a 2D Cartesian coordinates
Let i, j be corresponding basis of unit vectors
The divergence of a continuously differential vector field F = Ui+ Vj is defined as the (signed) scalar-valued function:
@ @ @U @V divF = F =( , ) (U, V )= + r · @x @y · @x @y Back to Laplacian
The Laplacian of a scalar function or functional expression is the divergence of the gradient of that function or expression: