Learning OpenCV 3

Chapter 10. Filters and Convolution

Overview

At this point, we have all of the basics at our disposal. We understand the structure of the library as well as the basic data structures it uses to represent images. We understand the HighGUI interface and can actually run a program and display our results on the screen. Now that we understand these primitive methods required to manipulate image structures, we are ready to learn some more sophisticated operations.

We will now move on to higher-level methods that treat the images as images, and not just as arrays of colored (or grayscale) values. When we say “image processing” in this chapter, we mean just that: using higher-level operators that are defined on image structures in order to accomplish tasks whose meaning is naturally defined in the context of graphical, visual images.

Before We Begin

There are a couple of important concepts we will need throughout this chapter, so it is worth taking a moment to review them before we dig into the specific image-processing functions that make up the bulk of this chapter. First, we’ll need to understand filters (also called kernels) and how they are handled in OpenCV. Next, we’ll take a look at how boundary areas are handled when OpenCV needs to apply a filter, or another function of the area around a pixel, when that area spills off the edge of the image.

Filters, Kernels, and Convolution

Most of the functions we will discuss in this chapter are special cases of a general concept