In this chapter, we introduced the most basic concepts of computer vision science. We started the chapter by learning about computer vision as a term and its use cases, before taking a look at some of the industries that make extensive use of it. Then, we moved on to learn about images and their most crucial properties, namely pixels, resolution, channels, depth, and so on. We then discussed some of the most widely-used color spaces and learned how they affect the number of channels and other properties of an image. After that, we were presented with the common input and output devices used in computer vision and how computer vision algorithms and processes fit between the two. We ended the chapter with a very brief discussion on computer vision libraries and introduced our computer vision library of choice, which is OpenCV.

In the next chapter, we'll introduce the OpenCV framework and start with some hands-on computer vision lessons. We'll learn how OpenCV can be used to access input devices, perform computer vision algorithms, and access output devices to display or record results. The next chapter will be the first real hands-on chapter in the book and will set us up for later, more practical chapters.