A brief introduction to computer vision

If we were to ask you to name the objects in the picture below, you would probably come up with words like, “car”, “van”, “streetlight”, “sign” without having to think too hard. This is a very simple task that anyone with basic intellect can do.

Behind the scenes, however, a very complicated neural process takes place. Human vision is an intricate system that involves our eyes but also accounts for our mind, our perception, our understanding of concepts, and our personal experiences through the billions of interactions we’ve made with the physical world during our lives.

While modern digital equipment can capture images like this with a level of detail that exceeds our own visual capabilities, making sense of what’s in those images is something that computers don’t naturally excel at. To a standard computer, the image above is nothing more than an amalgamation of pixels with differing color intensities. That’s it.

But there’s a field of computer science that has been working hard for decades to enable machines to read and interpret images in the same way we do—computer vision.

Seeing through the eyes of a machine

While this may sound straightforward, it’s anything but.

Computer vision is one of the most challenging fields in computer science, not only because of the dynamic complexity of our physical world but also because human eyesight is based on billions of years of human evolution.

If we’re able to perfect it, though, its value to our world will be immeasurable. This is perhaps best illustrated by the fact that the global computer vision market size is expected to grow at a compound annual growth rate of 45.64% between 2021 and 2028, culminating in a total value of US$144.46 billion as research efforts intensify.

What is computer vision?

Computer vision is a field of artificial intelligence (AI) that focuses on replicating parts of the human visual system and enabling machines such as computers, mobile devices, and cameras to “see” their surroundings and derive useful information from them.

The goal of computer vision is to imitate the human eye so that computers, with the help of hardware, algorithms, and data, can be trained to interpret images and perform a range of ever-increasingly complex tasks and functions in response. And thanks to recent advances in AI and deep learning, both research into and the physical application of computer vision is making astounding progress.

What are examples of computer vision applications?

The value of computer vision can be attributed to the problems it could solve. It’s the main technology that enables the digital world to interact with our physical world.

A classic example of a computer vision application is the self-driving car. While some models such as the Tesla Model X have self-driving capabilities, advances in computer vision could lead to fully autonomous “level 5” vehicles that require zero human interaction.

Computer vision also plays an important role in facial recognition, which enables computers to match images of people’s faces to their identities. It’s also the technology that powers augmented and virtual reality, and it has been critical in the development of modern health technology. Computer vision algorithms can help to automate tasks such as detecting fractures in X-rays and cancerous moles in images of skin.

There are several more nuanced applications, too. Smart home security, pest detection in farming, tracking objects through a sequence of images, image segmentation, retail inventory management, engagement monitoring in education, and more are all examples of areas where computer vision can add substantial value.

Common computer vision tasks

Applications like self-driving cars and computer-powered medical diagnostics are powered by a range of key computer vision tasks. These include:

Object detection

Object detection is the ability of a computer to detect, recognize, identify, and correctly classify the position of objects in an image. The position of an object is typically captured using bounding boxes or other shapes such as polygons or ellipsoids.

Image classification

Image classification in computer vision is the classification of an image as a whole as opposed to its individual objects or components. An example of an image class might be “field” or “forest”.

Semantic segmentation

This is an advanced AI image annotation method that classifies defined regions within an object or area. Returning to the above image, you can see that all people in the image are surrounded by a yellow bounding box. This is semantic segmentation; the objects (people) all belong to the same class.

What’s next for computer vision?

It has taken the best part of 80 years for computer vision to get to where it is now. Recent breakthroughs in AI and deep learning are helping to further progress in the field at an astonishing rate.

Yet, there are still plenty of unknowns. Not only in terms of how we can unlock the full potential of computer vision but also in terms of our partial biological understanding of human vision.

While it’s hard to say what the future of computer vision will look like, what we do know is that our utilization of it as a technology is bound to grow. If the examples above—autonomous vehicles, AI-powered medical technology, and agricultural technology — are anything to go by, the future looks bright.

About Tasq.ai

At Tasq, we are helping AI companies develop better machine learning models and computer vision applications by providing them with a best-in-class data labeling solution. This allows them to take their computer vision products to the next level in terms of accuracy and quality.

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