Linear Algebra and Image Processing II

In this module, you will be introduced to some fundamental concepts in linear algebra that form the foundation of many data science and AI models you will encounter as you progress through your study program. Rather than teach it as a core mathematical topic, the aim of this module is to provide you with exposure to key topics in linear algebra that are essential for data science. Today we will examine applications of linear algebra to image processing, in particular kernals and convolution filters.

Today's learning objectives

• Understand the relationship between linear algebra and image processing.
• Understand the notion of kernals in images.
• Understand the concept of convolution in images.

Filters

We all have been guilty of using an instagram filter at somepoint in our lives! But what are these filters, and how do they make us all look so good! Well, unsuprisingly it's got a bit of linear algebra behind it!

An image filter typically uses something called a kernel. And the process of applying a kernel to an image is known as convolution.

In simple terms, a convolution is a process of taking two matrices - an image matrix and a kernel matrix and producing a matrix, which is the modified image. Here's a video describing the process.

For today's that all we need to know about convolutions and image processing - it's a really neat trick involving a lot of linear algebra! We will re-visit these concepts in block C when we introduce deep learning and computer vision! Please continue working on your creative briefs for the rest of today