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?
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.
Computer vision, as every subset of AI, brings many values and facilitates many procedures. So what are its advantages?
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.
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.
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.
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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