# Typical Applications for Computer Vision

So far in this book, we have covered three main topics: Artificial Intelligence, Machine Learning, and Neural Networks. There is yet one more field in Artificial Intelligence that is gaining very serious traction—that is the field of Computer Vision. This field will be the focal point of this chapter. As the name implies, with Computer Vision, we are trying to replicate how human vision works, but at the level of the computer or machine. In a way, this is very analogous to Artificial Intelligence, in which the primary purpose is to replicate the thought, behavioral, and decision-making process of the human brain.

In this chapter, we will start by giving a high level overview of Computer Vision, and from there, we will do a much deeper dive into the theories and the applications that drive this emerging area of Artificial Intelligence. But before we start delving deeper into this subject, it is first very important to give a technical definition as to what Computer Vision is all about. Here it is:

Computer vision (CV) is a subcategory of Computer Science &t Artificial Intelligence. It is a set of methods and technologies that make it possible to automate a specific task from an image. In fact, a machine is capable of detecting, analyzing, and interpreting one or more elements of an image in order to make a decision and perform an action.

(Deepomatic, n.d.)

Put in simpler terms, the field of Computer Vision from within the constructs of Artificial Intelligence examines certain kinds of images that are fed into the system, and from there, based upon the types of mathematical and statistical algorithms that are being used, the output is generated from the decision-making process that takes place. In this regard, there are two very broad types of Image Recognition, and they are as follows:

1) Object Detection:

In terms of mathematics, this is technically known as “Polygon Segmentation.” In this regard, the ANN system is specifically looking for the element from within a certain image by isolating it into a particular box. This is deemed to be far more superior and sophisticated rather than using the pixelated approach, which is still used most widely.

2) Image Classification:

This is the process that determines into which category an image belongs based specifically upon its composition, which is primarily used to identify the main subject in the image.

## Typical Applications for Computer Vision

Although Computer Vision is still in its infancy, when used with an ANN system, as mentioned, it is being used in a wide variety of applications, some which are as follows:

■ Optical Character Recognition: This is the analysis of, for example, various pieces of handwriting, and even automatic plate recognition (aka ANPR);

■ Machine Inspection: This is primarily used for Quality Assurance Testing Purposes, in which specialized lights can be shone onto different kinds of manufacturing processes, such as that of producing separate parts for an aircraft and even looking into them for any defects that are otherwise difficult to detect with the human eye. In these particular cases, X-Ray vision (which would actually be a subcomponent of the ANN system) can also be used;

■ 3-D Model Building: This is also known as “Photogrammetry,” and it is the process in which 3-Dimensional Models from aerial survey photographs, or even those images captured by satellites, can be automatically recreated by the ANN system;

■ Medical Imaging: Computer Vision in this regard can be used to create preoperative as well as postoperative images of the patient just before and after surgery, respectively;

■ Match Move: This process makes use of what is known as “Computer Generated Imager” (aka “CGI”), in which various feature points can be tracked down in a source-based video. This can also be used to further estimate the level of the 3-Dimensional Camera motion, as well as the other shapes that can be ascertained from the source video;

■ Motion Capture: The concepts here are used primarily for Computer Animation, in which various Retro-Reflective Markers can be captured;

■ Surveillance: This is probably one of the most widely used aspects of Computer Vision. In this regard, it can be used in conjunction with CCTV technology as well as Facial Recognition technolog)' in order to provide the proof positive for any apprehended suspect.

It is important to note at this point that Computer Vision can also be used very well for still types of photographs and images, as opposed to the dynamic ones just previously described. Thus, in this regard, some typical applications include the following:

■ Stitching: This technique can be used to convert overlapping types of images into one “stitched panorama” that looks virtually seamless;

■ Exposure Bracketing: This can take multiple exposures from a sophisticated camera under very difficult lighting conditions by merging all of them together;

■ Morphing: Using the mathematics of “Morphing,” you can turn one picture into another of the same type;

■ Video Match Move/Stabilization: With this particular process, one can take 2- Dimensional and 3-Dimensional images and literally insert them into videos to automatically locate the nearest mathematical-based reference points;

■ Photo-based Analysis: With this specific technique, you can circumnavigate a series of very different pictures, to determine where the main features are located;

■ Visual Authentication: This can also be used as a form of authentication, very much in the same way that a password or your fingerprint can 100 percent confirm identity, for example, when you gain access to shared resources.