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An introduction to Computer Vision (CV)
The days of dreaming about computers that act just like humans do are over. Owing to the on-going development of artificial intelligence, we’re getting closer and closer to making computer vision a part of our daily lives. This technology is already present in many areas, and it continuously helps us in many mundane or complicated tasks by doing it faster and more efficiently.
So, what’s computer vision? How does it work? What are its applications, and how they impact various industries? What image processing got to do with computer vision?
This article is an introduction to computer vision (CV) – you’ll learn about its basics and the impact on the real world. You’ll get to know the industries in which computer vision works and the benefits it gives. Moreover, I’ll distinguish computer vision from its related field – image processing. How are they different?
What is Computer Vision?
Computer vision (CV) is a subset of AI and machine learning that enables machines to identify, interpret and label objects within images and videos. It detects and understands the visual world, using software algorithms. This process is very similar to how people see things. How is this possible? Computers recognize patterns in pixels and match them with the already validated images. It’s one of the most used solutions of computer vision.
Now, the systems are more accurate than ever. Thanks to a large amount of data that we share online every day, we can train CV and improve it even more.
The benefits
Computer vision, as every subset of AI, brings many values and facilitates many procedures. So what are its advantages?
- Process automation
There’s no longer the need for us to do the whole procedure of analysing the images manually. Now the computer will process and generate a hundred pages long data for every image that we put there.
- Saved time & money
While this process could take a few hours for a human, the machine will do it much faster. Owing to this, we save our time doing the mundane work, as we can do it something else instead. Also, it cuts down the costs of such operation since we no longer have to devote our time to do it.
- Efficiency
That means the computer can find something that we may overlook. With the help of appropriate algorithms, it’d be possible to spot something that originally might have been unnoticed by us. Moreover, the machine can “see” more because it doesn’t get tired. It can operate all day without breaks and work as efficiently as during the first hour of the day.
Image processing vs computer vision
Often these two terms are used interchangeably and yet they’re not the same thing. So, how are they different from each other?
Computer vision is about the machine’s ability to perceive objects on the image as humans do. Here, the focus is on taking the information from the input digital images and understand it.
At the same time, digital image processing is the use of a computer to process a particular image using algorithms. It allows the transition from the input image to the output image. In other words, it manipulates the data using computers by preparing them to do other tasks. There are many methods in image processing. For example, noise removal that smooths out the photo or thresholding that creates a binary image (which is an image with only black and white pixels).
All in all, even though there’s no computer vision without image processing, there are some vital differences between them that we should keep in mind.
Computer Vision applications
It’s present in the products of everyday use and various industries such as security, manufacturing, and healthcare. It facilitated the processes and made them efficient and less time-consuming. Below, you can read about a few examples of computer vision applications.
- Security (facial recognition)
This technology is used to control access to information, valuable objects and many more. Thanks to this, we have an additional layer of security to protect the data. - Manufacturing (robotics)
It’s used in many companies that specialize in production. Thanks to automating the process, manufacturing machines can assess a given product’s quality on a production line. - Healthcare (object recognition, image analysis)
Computer vision has also been an essential part of the healthcare industry. Its algorithms can help automate tasks such as detecting anomalies within digital images or finding symptoms in X-ray and MRI scans.
What we know
Our experience with computer vision focuses on medical imaging. We make software for the quality assurance of medical imaging devices. In other words, we do everything to improve the medical diagnostic process. There are many kinds of such medical devices – X-ray, MRI, CT and many more. Images from these devices facilitate detecting anomalies and other signs of illnesses.
Our job is to check the image’s quality. However, it’s challenging to do so when verifying images of various patients. Each person is different, which means you’ll never get the same two images. That’s the reason why we use imaging phantoms. They’re designed specifically to assess, inspect, and tune numerous imaging devices’ performance. Thanks to them, the images are standardized – each subsequent picture, regardless of the device, meets set norms. This helps us to check whether the device works as it should.
Final thoughts
I hope that you enjoyed this introduction to computer vision and that it gave you insight into the topic, its applications and image processing. As you can see, it’s a vast technology that automates many processes in various industries, making them more efficient. Moreover, this subset of artificial intelligence is growing every day.
Want to use AI to create your own digital product? Book a meeting with Leo or send us a message. We’ll be happy to talk to you.
Editor’s note: We’ve originally published this post in March 2021 and updated it for comprehensiveness.
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