Computer vision is really struggling is understanding the context of images and the relation between the objects they see. We humans can quickly tell without a second thought that the picture at the beginning of the book is that of a family picnic, because we have an understanding of abstract concepts it represents. We know what a family is. We know that a stretch of grass is a pleasant place to be. We know that people usually eat at tables, and an outdoor event sitting on the ground around a tablecloth is probably a leisure event, especially when all the people in the picture are happy. All of that and countless other little experiences we've had in our lives quickly goes through our minds when we see the picture. Likewise, if I tell you about something unusual, like a "winter picnic" or a "volcano picnic" you can quickly put together a mental image of what such an exotic event would look like. For a computer vision algorithm, pictures are still arrays of color pixels that can be statistically mapped to a certain descriptions. Unless you specifically train a neural network on pictures of family picnics, it won't be able to make the connection between the different objects it sees in a photo. Even when trained, the network will only have a statistical model that will probably label any picture that has a lot of grass, several people and tablecloths as a "family picnic." It won't know what a picnic is contextually. Accordingly, it might mistakenly classify a picture of a poor family with sad looks and sooty faces eating in the outdoors as a happy family picnic. And it probably won't be able to tell the following picture is a drawing of an animal picnic. Computer vision is a field of study which enables computers to replicate the human visual system. It's a subset of artificial intelligence which collects information from digital images or videos and processes them to define the attributes. The entire process involves image acquiring, screening, analysing, identifying and extracting information. This extensive processing helps computers to understand any visual content and act on it accordingly. Computer vision projects translate digital visual content into explicit descriptions to gather multi-dimensional data. This data is then turned into computer-readable language to aid the decision-making process. The main objective of this branch of artificial intelligence is to teach machines to collect information from pixels.