As you saw in the previous section, we used a derivative mask to an calculate image derivative. Before going further into the chapter, let's formally define what these masks are. A lot of times in texts/research papers/books related to image processing, we use the terms mask, kernel, and filter interchangeably. What these essentially mean is a square matrix of numbers that is used to compute various properties or characteristics in an image. You have already seen an example of an image derivative. Some other common examples of such kernels/filters/masks are edge detection, image blurring, and more. As you read through this chapter, you will see various examples of kernels that will help you understand this better.