For many years, mankind had the utopian goal of building intelligent machines that could think and behave like people. One of the most intriguing concepts was to enable computers to "see" and comprehend their surroundings. The historical fiction of yesterday is now contemporary actuality. Computer vision technology has made great strides toward inclusion in our daily lives as a result of developments in artificial intelligence and computational capacity. Computer vision is a very promising UX technology that is anticipated to have a market of $48.6 billion by the end of 2022. This article will examine the idea of computer vision, go over how it developed, and provide some fantastic instances of how we may use it in our daily lives.

Defining computer vision

The study of computer vision focuses on developing digital systems that can process, examine, and comprehend visual input (such as photos or videos) in a manner similar to that of humans. The idea behind computer vision is to program computers to analyze and comprehend images down to the pixel level. Technically, machines try to retrieve, manipulate, and analyze visual data using specialized software algorithms.

Here are a few common tasks that computer vision systems can be used for:

• Object classification. The system parses visual content and classifies the object on a photo/video to the defined category. For example, the system can find a dog among all objects in the image.
• Object identification. The system parses visual content and identifies a particular object on a photo/video. For example, the system can find a specific dog among the dogs in the image.
• Object tracking. The system processes video finds the object (or objects) that match search criteria and track its movement.

How does it work?

Computer vision technology frequently resembles how the human brain functions. But how does our brain deal with recognizing visual objects? According to a widely accepted theory, our brains use patterns to decode specific objects. Systems for computer vision are developed using this idea. The algorithms for computer vision we use today are based on pattern recognition. We use a ton of visual data to train computers, which then process photos, identify the things in them, and look for patterns. For instance, if we send a million photographs of flowers, the computer will examine them, find patterns that are shared by all flowers, and then produce a model "flower" as a result of its analysis. As a result, each time we submit them a photo, the computer will be able to recognize a certain image as a flower with accuracy.

In his paper Image Processing and Computer Vision, Golan Levin describes in technical terms the steps that computers take to comprehend images. In essence, computers perceive images as a collection of pixels, each with a unique set of color values. Here is a photo of Abraham Lincoln as an illustration. This image's brightness is encoded as a single 8-bit value that ranges from 0 (black) to 255. When you enter an image into software, it sees these numbers. The computer vision algorithm using this data will be in charge of conducting additional research and making decisions.

The evolution of computer vision

The initial studies with computer vision were conducted in the 1950s, and at that time, it was used to decipher handwritten and typewritten text. At the time, computer vision analysis processes were quite straightforward, but human operators had a lot of work to do because they had to supply data samples for manual examination. It was challenging to supply a lot of data when doing it manually, as you could imagine. Additionally, the processing capacity was inadequate, which increased the analysis's error margin.

There is no scarcity of computing capacity in the modern world. Robust algorithms and cloud computing can help us address even the most challenging issues. The tremendous amount of publicly accessible visual data that we produce each day is what is propelling computer vision technology ahead, not simply the new hardware combined with advanced algorithms (we shall cover them in the next part). More than three billion photographs are shared online every day, and according to Forbes, this data is used to train computer vision systems.