The History of Computer Vision
Computer vision is a compelling type of artificial intelligence that trains computers to "see" and interpret the visual world as we do.
Early experiments on computer vision started in the 1950s. After years of research, we have made significant progress towards creating machines that can identify objects and sort them into different categories. However, human vision and perception are quite complex. That creates multiple challenges along the way to replicate the fundamental function of the human brain.
Want to know more? Here is everything you need to know about computer vision from its creation to its use today.
What Is Computer Vision?
Computer vision is a multidisciplinary field as it combines different areas of studies such as engineering and computer science. It is broadly a subfield of AI, which involves the use of specialized methods and algorithms.
Perhaps unsurprisingly, computer vision is one of the hottest research areas in AI and computer science. It seeks to develop AI systems' ability to see and understand the physical world and make decisions from vision according to the situation. In other words, it attempts to imitate the capability of human vision.
The AI trains computers to extract useful information from digital images such as pictures and videos. Then, the computer reacts to what it sees. This information can be anything from three-dimensional models to text descriptions and more.
With the rapid advancement in hardware and algorithms today, devices can identify objects with 99% accuracy. That means they can react more quickly and accurately to visuals than humans.
How Computer Vision Works
It’s basically about recognizing an image. So, we will see how computer vision does that. It follows three basic steps to understand an image:
- Obtaining a digital image. The machine obtains an image or a large set of images through photographs, video, or 3D technology for further analysis.
- Processing the image. The system processes the image and divides it into multiple parts to analyze them individually. Thanks to deep learning models, most of this process is automated.
- Understanding the image. The last step is identifying an object(s) and then grouping them into separate categories.
History of Computer Vision
Here's a brief history of the creation and development of computer vision from the beginning to 2012.
In the 1950s, early experiments in computer vision took place using the first neural networks to sort simple images by detecting the edges of objects.
In the late 1960s, the universities that were exploring AI started computer vision. It was intended to imitate the human visual system as the first step to creating intelligent robots.
In 1966, AI Papert and Minsky launched a two-month summer project with 10 men. They aimed to create a computer that could identify and describe objects in images. However, for that to happen, the computer has to have the ability to identify individual pixels as belonging to one object.
At the time, the dominant branch of AI was symbolic AI. It meant that the programmers had to specify rules manually. Since one object can appear in a range of different scenarios, it was impossible to create that many manual rules. Eventually, the summer project failed with a limited outcome.
It was the first time in the 1970s that computer vision was used commercially. It was used to interpret typed or handwritten text using optical character recognition. A few years later, in 1979, the Japanese scientist Kunihiko Fukushima proposed the neocognitron, a multilayered artificial neural network. Although it failed to perform complex visual tasks, it laid the basis for further development in computer history.
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In the 1980s, the studies were more strictly based on quantitative aspects of computer vision. A postdoctoral computer science researcher named Yan LeCun introduced the convolutional neural network, or CNN, inspired by Fukushima's neocognitron. Unfortunately, CNN needed a lot of data and resources to work efficiently, which were not available at the time. Hence, it remained on the sidelines of computers.
By the 1990s, more images were available online for better exploration. Consequently, there was development in facial recognition systems. These larger sets of data made it possible for machines to recognize specific people in digital images. Moreover, towards the end of the 1990s, some of the previous research came into the spotlight.
In 2012, the Toronto based AI researchers developed AlexNet, which was a convolutional neural network. Due to the increased availability of data sets, ALexNets became successful, and this rekindled interest in CNN once more. It also proved to be revolutionary in deep learning, a part of machine learning methods based on the use of artificial neural networks.
Applications of Computer Vision Today
With the advancements in convolutional neural networks and deep learning, computer vision is becoming more capable of identifying patterns and reacting to images than the human visual system. Here are a few applications of computer vision today.
Just like your fingerprint, your face print is your unique code. Today, facial recognition is widely used in official work, police work, payments and airport security checkpoints.
Google Translate App
The Google Translate app uses computer vision to see images through optical character recognition and provides an accurate translation using augmented reality. You just have to point your camera at the words in the foreign language and the app will instantly tell you what it says in your preferred language.
Real-Time Sports Tracking
Computer vision makes it easy to track players' performance and ratings as well as brand sponsorships in sports broadcasts.
Since most medical data is based on images, medicine field offers plenty of space for computer vision in medicine. It provides new diagnostic methods to analyze x-rays and other scans to identify any problems in the body.
To Wrap It Up
Looking at the rapid advancement in artificial intelligence systems, it is not hard to believe that computer vision will have more beneficial applications in the near future. The machines employed with this technology can become as efficient as us humans.
Still, it is believed that quite a few challenges are left for the computer vision to overcome. Yet, by looking at the current advancements and achievements, it is on the right track.