Computer vision applications have become quite ubiquitous in our lives. The applications are varied, ranging from apps that play Virtual Reality (VR) or Augmented Reality (AR) games to applications for scanning documents using smartphone cameras. On our smartphones, we have QR code scanning and face detection, and now we even have facial recognition techniques. Online, we can now search using images and find similar looking images. Photo sharing applications can identify people and make an album based on the friends or family found in the photos. Due to improvements in image stabilization techniques, even with shaky hands, we can create stable videos.
With the recent advancements in deep learning techniques, applications like image classification, object detection, tracking, and so on have become more accurate and this has led to the development of more complex autonomous systems, such as drones, self-driving cars, humanoids, and so on. Using deep learning, images can be transformed into more complex details; for example, images can be converted into Van Gogh style paintings.
Such progress in several domains makes a non-expert wonder, how computer vision is capable of inferring this information from images. The motivation lies in human perception and the way we can perform complex analyzes of the environment around us. We can estimate the closeness of, structure and shape of objects, and estimate the textures of a surface too. Even under different lights, we can identify objects and even recognize something if we have seen it before.
Considering these advancements and motivations, one of the basic questions that arises is what is computer vision? In this chapter, we will begin by answering this question and then provide a broader overview of the various sub-domains and applications within computer vision. Later in the chapter, we will start with basic image operations.
