Hands-On Image Processing with Python

Some notes about the Laplacian

Let's take a look at the following notes:

•  is a scalar (unlike the gradient, which is a vector)
• A single kernel (mask) is used to compute the Laplacian (unlike the gradient where we usually have two kernels, the partial derivatives in the x and y directions, respectively)
• Being a scalar, it does not have any direction and hence we lose the orientation information
•  is the sum of the second-order partial derivatives (the gradient represents a vector consisting of the first-order partial derivatives), but the higher ...