Mastering Computer Vision with TensorFlow 2.x

Linear filtering—convolution with kernels

Convolution in computer vision is a linear algebra operation of two arrays (one of them is an image and the other one is a small array) to produce a filtered image array whose shape is different than the original image array. Convolution is cumulative and associative. It can be represented mathematically as follows:

The preceding formula is explained as follows:

• F(x,y) is the original image.
• G(x,y) is the filtered image.
• U is the image kernel.

Depending on the kernel type, U, the output image will be different. The Python code for the conversion is as follows:

import numpy as npimport cv2import matplotlib.pyplot ...