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Computer vision is undoubtedly one of the most exciting and powerful artificial intelligence (AI) technologies. Many of us interact with computer vision without even knowing. As a technology, computer vision, also known as machine vision, is fast gaining popularity.   

So, what is it? And why is it gaining so much traction these days?  

This article gives a comprehensive overview of computer vision, how it works, and some of its use cases.  

Let’s dive!  

What is Computer Vision?  

Computer vision is a field of artificial intelligence that trains and enables machines to make sense of the world using visual inputs like images and videos. It also uses deep learning algorithms to identify and classify objects based on visual information. Moreover, computer vision enables computers to take action or react to what they are fed visually.   

As a branch of AI, computer vision is quite similar to human vision in that it mimics how humans “see” objects and react to them. However, human sight surpasses AI-based computer vision by virtue of being able to put sensory input into context. Humans can tell how far away an object is, whether it’s stationary or moving, and how it differs from another object.  

But that does not mean that computer vision is limited or incapable of meeting, and even surpassing, human vision. Computer vision aims to imitate the human sight function by doing all the things that human vision allows you to do, e.g., tell how far an object is. Once you train a machine to analyze production defects in a product, the AI-powered system becomes capable of vetting hundreds of products in a short time.   

Thus, computer vision exceeds the abilities of human vision under given circumstances. No wonder, then, that sectors ranging from automobile manufacturing to services like hotels are using computer vision to improve business outcomes.   

Machine vision is not a new phenomenon. It has its roots in the late 1950s and early ‘60s when neurophysiologists conducted an experiment where they showed multiple images to a cat, aiming to match the response in its brain. They discovered that image processing begins with identifying hard edges and simple shapes.  

Computer vision has been growing rapidly since the 1990s when the internet took off. It was in the early 2000s that the first real-time facial recognition applications emerged. And the work has never stopped since.  

Let’s see how machine vision works.  

How Does Computer Vision Work?  

Computer vision requires vast volumes of data to work. It analyzes data repeatedly until it is able to notice the difference between various visual inputs and recognize them.   

Let’s take an example to explain this further. Suppose you want to use machine vision to determine a dog’s breed. This would require you to feed the computer tons of images showing various dog breeds. You would also need to provide the computer with all the colors of a breed so it can correctly identify the breed. A tedious task! So, how do you achieve this?  

Machine vision engineers use two primary technologies to do this: deep learning (a subset of machine learning) and Convolutional Neural Networks (CNN).  

Deep learning uses algorithms that allow a computer to train itself to analyze the context of visual data. When you feed adequate amounts of data to your computer, it uses algorithmic models to “see” the data and learn how to identify and discern visual inputs. Thus, you don’t need to program a computer to recognize a picture – it learns how to do it on its own.  

A convolutional neural network (CNN) deconstructs images into pixels with labels/tags to help a deep learning model “see.” It uses these tags to carry out convolutions (mathematical operations) and makes guesses as to what it is “seeing.” The network then runs continuous convolutions in iterations to check the precision of its guesses. This process continues until the guesswork starts becoming more precise and accurate.   

CNN’s are employed to understand single images, whereas recurrent neural networks (RNNs) are used to interpret video content.  

Computer Vision Applications  

Incredible amounts of money are spent researching how computer vision can help various industries bolster their products or business processes. But research is only one aspect of it. The true power of machine vision is demonstrated by its real-world applications – from healthcare and crime detection to automotive manufacturing.   

A critical factor that can enable the spread of computer vision applications is the vast volumes of visual data available today. This data comes from various sources, like CCTVs, security cameras, smartphones, traffic cameras, and other visually-equipped devices. However, this data is hardly being used to enhance deep learning models for computer visions.   

Nevertheless, we have some very good examples of industries and products where machine vision plays a vital role.   

Let’s look at these.  

Application of Computer Vision

1. Facial Recognition  

Computer vision plays a crucial role in enabling facial recognition in software applications. Smartphones and other machines use facial recognition to identify people. It works through machine vision algorithms that identify facial patterns and then run them through its database that stores facial profiles.  

Facial recognition, although subject to criticism, is used in many products and for various purposes. Your smartphone uses it to identify you, social media networks use it to identify users, and security agencies use it to identify criminals.   

2. Self-driving Cars  

Machine vision is a critical component of self-driving cars as it enables them to sense their surroundings. The cameras on these cars capture images and videos from various angles and feed them into computer vision software. The software then processes this visual input to identify road margins, read and interpret signboards and traffic signals, detect pedestrians and other objects, etc.  

Thus, self-driving cars use computer vision to steer clear of obstacles like other cars and pedestrians. It also ensures that there are no accidents and that the passengers arrive safely at their destination.  

3. Healthcare  

The use of AI and its associated technologies is rapidly growing, disrupting many industries worldwide. Healthcare is one such sector where AI, in general (by automating repetitive tasks) and machine vision, in particular, are playing a decisive role in advancing healthcare outcomes.  

A simple use case of computer vision in healthcare is to detect cancerous skin lesions through skin images or X-rays/MRIs. Moreover, machine vision is being deployed by leading tech solution firms to create devices that can look after the elderly and sick via computer vision. These devices or robots are used to monitor and detect healthcare changes in the elderly and suggest appropriate measures.  

4. Mixed and Augmented Reality  

Computer vision is also vital in mixed and augmented reality (AR) technologies. These technologies use machine vision to identify real-time objects and then ascertain their location on a device’s display screen. Similarly, AR tools use machine vision to determine the depth and dimensions of real-time things.  

That’s a Wrap!  

While it is true that the use of computer vision is growing by leaps and bounds, it still needs to match human vision in many ways. It is incredibly hard to build machines that can truly mimic human behavior, vision, and cognition. Not to mention the difficulty we face in attempting to understand ourselves and how our various bodily functions operate.  

Nevertheless, the field of machine vision is set to become monumental in the years to come, particularly in the healthcare and automobile sectors. Companies are pumping in loads of money to figure out how this technology can help them achieve their strategic goals.  

Are you also wondering how AI and computer vision can help your organization? If yes, contact us at info@xavor.com – we deliver innovative AI solutions to our clients, helping them achieve their goals. 

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Muhammad Abbas

Muhammad Abbas is an anthropologist at heart and a Marketing Specialist by profession. He has worked with leading marketing agencies over the years. While he considers himself a history buff, his interests also extend to topics like politics, economics, social justice, climate change, and tech.