> For the complete documentation index, see [llms.txt](https://aadv1k.gitbook.io/computer-vision-guide/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://aadv1k.gitbook.io/computer-vision-guide/convolutions-and-filters.md). # Convolutions and Filters ## What is a Convolution? To better understand all the sections which follow, Let's understand some of the jargon present within this space; A **Convolution** is a function which takes in an image and an **Area** let's call this `N` `N` refers to what is known as a **Kernel size** in context of CV we perform modifications to the pixels across an area or the Kernel, think of it as a stencil -- you place it on a 2D list of pixels and do some calculation on whatever pixels are contained within this stencil, this would mean that a kernel of dimensions 3x3 would encapsulate the current pixel, and eight other neighboring pixels. Rinse and repeat for every pixel within the image and you've got yourself a Convolution. Here is a visualization of what a kernel is.

> **Note** I will use pixels and array of integers interchangeably, when I say pixel it always implies it encapsulates three integers for RGB (for most cases) The terms **filter** and **mask** can also be used interchangeably with convolution functions. You may see me do this in the modules that follow.