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Published: 30/03/2023

What are the practical applications of modern computer vision technology?


Computer vision technology is present in the majority of people’s everyday lives. Digital cameras and scanners have accompanied us in our daily endeavors for years. Modern computer vision helps us to a much broader extent. From gaze estimation to onboard assistance applications for cars, along with AI, CV is finding its way to more significant progress.

What is computer vision? An introduction

Computer vision can be defined as a field of science that involves creating systems that can process, analyze and interpret visual data the same way human vision does. It’s based on teaching computers to process and understand an image at a pixel level. It can all work with the use of specially designed computer vision algorithms.

The most common tasks and computer vision applications include the following:

  • Object classification: the system can classify the object on a photo or video to a defined category that the algorithm was trained to do.
  • Object identification: after examining the visual information, the system can identify a specific object—for example, a particular cat from among other cats in the picture or video.
  • Object tracking: after analyzing the video and finding the defined object, the algorithms can track its movement.
What is computer vision

How does computer vision work?

Primarily, computer vision uses pattern recognition techniques to train itself to understand visual data. It mimics the human brain and its ability to recognise visual information. With human vision, the image is perceived by the eye and then transmitted to the brain to interpret it. It’s slightly different from human intelligence in computer vision.

Initially, we need a sensing device that acts as an eye, a receiver of the visual data. Then it gets transmitted to the interpreting device – a computer, smartphone or like device. Once the device interprets the image data, it provides visual images as an output. The process is similar to how human perception works with the extra step of providing the output.

Computer vision algorithms and systems we use nowadays rely on recognising patterns. Computers are trained on a massive amount of visual data to process the images, label the objects present in them and find ways in those objects. If we “feed” the device a million images of cats, it will identify them as cats and recognise the similar patterns connecting all of them. In turn, it will allow it to recognise a cat easily when it’s present in a picture that we show in the future.

In the provided article, we can find more details about the technical aspect of how computer vision works. Simply put, machines interpret images as series of pixels, each with its colour value. When we provide an image, the device we use sees it as that set of numbers. This data is provided as input to the computer vision algorithm for further image segmentation based on the pixel’s brightness, analysis, and decision-making.

The evolution of computer vision

Foundations and early history

We can find the foundations of computer vision technology in research conducted in the 1950s and 1960s. In one of the early experiments, the scientists researched cats, limiting their vision to one eye, to conclude that how cats (and humans) perceive objects in physical space is hierarchical. We start with simple features like an object’s edges, proceed with the shapes and then recognise more details and complicated features. And it all happens so fast that we don’t notice all those stages.

Computer vision, as such, is not a new technology. Its beginnings date back to the 1950s. At that time, it was used to interpret the typewritten and handwritten text. The analysis procedures were relatively simple. However, the process still required much human work, as the data samples had to be provided manually. The low computational power at the time resulted in a relatively high margin of error regarding the analysis.

The foundations of computer vision

Recent computer vision development

Currently, we don’t face limitations in the computing power characteristic of the early days, as we can provide the computing power required to perform complex operations. Cloud computing enables computers and powerful algorithms to solve complex problems. The rapid development of computer vision couldn’t occur without the vast amount of data and digital images generated daily and shared online. It allows us to teach computers and computer vision systems.

The rise of deep learning models and computer vision algorithms

We must focus on the importance of machine learning, deep learning and artificial intelligence in computer science, which are currently the driving force for developing many new technologies, including computer vision software.

Computer vision relies vastly on machine learning and algorithms that help to detect and classify virtual objects well. Specifically, we are talking about deep learning – a branch of machine learning that uses algorithms to gain insights from collected data. Machine learning is based heavily on artificial intelligence, a foundation for both technologies.

The algorithms used in deep learning methods for computer vision are called neural networks. These networks extract patterns from provided data samples, for example, image processing. These algorithms are inspired by how the human brain functions, specifically the connections between neurons in the cerebral cortex.

The base principle of a neural network is the mathematical representation of a biological neuron. Deep learning relies on the layers of artificial neural networks, meaning having several layers of interconnected perceptrons is possible. Raw data goes through the network to the output layer, a prediction about a particular object.

The most popular type of artificial neural network used for processing visual input is a convolutional neural network. They are regularised versions of multilayer perceptrons. These, in turn, usually mean fully connected networks, where each neuron in one layer is connected to all neurons in the next layer. Convolutional neural networks use relatively little pre-processing compared to other image classification algorithms.

The deep learning network learns to optimize the filters (or kernels) through automated learning, whereas, in traditional algorithms, these filters are hand-engineered. This is why it gives a significant advantage in processing vast quantities of data.

Machine learning in computer vision

Applications of computer vision we can expect to grow

We can now focus on some practical applications and advancements that computer vision enables. Applying computer vision technology can improve many aspects of everyday or professional life.

Improve employee efficiency

In the transport or production industry, computer vision can contribute to the efficiency and safety of employees. While monitoring, if the station and positions are crewed as necessary at all times, might be perceived as invasive, it can serve more than just monitoring the employees. Once it matures enough, computer vision can help investigate the workstation and, if necessary, alert the supervisors for further investigation.

For example, the system can be trained to detect an abnormality at the station, like an accident. Thus, it could automatically report it so the necessary intervention can occur quicker. In the long run, especially in jobs with higher risk, it can make a difference between life and death.

Computer vision and machine learning can also help to automate tasks that don’t necessarily require human operators (for example, on a production line) so that they can focus on those that can’t be done automatically.

Computer vision in organising content

Organizing content

Machine learning algorithms and computer vision can come in handy with organizing visual inputs. With object detection and object recognition functions, it can distinguish one image from the other and manage them accordingly. Similar to video feeds. Detection properties can also aid with tasks like image restoration or scene reconstruction.

With advancements like optical character recognition, texts typed in practically any font and typeface can be scanned and transferred into the device, saving a lot of work with re-typing it if necessary for archiving or other purposes.

Self-Driving Cars

Self-driving cars are currently one of the hottest topics in technology and the physical world. It was also when computer vision started gaining popularity and visibility. However, computer vision applications in cars aren’t exactly new. Augmented reality has made significant progress in less than a decade. It’s now using object detection to assist drivers in multiple ways, for example, driving in their lane consistently or parking correctly.

Recently, computer vision has become a vital part of the interfaces in self-driving cars. Its ability to detect obstacles, lanes, and other traffic elements is essential for creating completely autonomous cars. However, getting to that stage would require further work and improvements to ensure road safety.

Facial recognition

One of the most common and popular computer vision applications is facial recognition. Many newer smartphones use facial recognition as one of their security measures, but this technology has a broader scope of use. For example, edge devices like cameras adopt using computer vision and facial recognition for automated focus while taking portrait pictures.

Facial recognition is also applied in other industries, for example, airport security. With biometric passports, some airports use automated systems with cameras for border control purposes and people identification.


Computer vision in healthcare isn’t a new concept. Medical imaging devices like ultrasonography or computer tomography have improved diagnostics for many years and are one of computer vision’s most prominent real-world applications.

Healthcare is another important department where computer vision systems can find their place. The ability of the neural network AI systems and machine learning to detect objects may prove helpful in analyzing and interpreting medical images to aid with cancer detection and other diagnostic tasks by human doctors. It can contribute to speeding up the diagnosis and starting the necessary treatment.

See how we helped create a health wearable for dogs.

View the case study

Entertainment and sports

CV has been a part of the entertainment industry, primarily gaming. Early attempts at using computer vision for object and image detection could be observed in accessories used for Nintendo Entertainment System consoles, like a gun that could be used for shooting games.

More recent developments saw a spike in the use of this technology with the emergence of virtual reality systems and hardware. They use computer vision to ensure the player can comfortably observe and traverse the game environment.

Some programs and games adopt augmented reality systems to create an entirely new experience or extend those already implemented in the game. AR can make a fun way to combine the real world with virtual entertainment.

Using computer vision in sports is something we know pretty well at One of the applications we developed for Sports Computing uses a smartphone camera to register and analyze shots on goal. Analyzing the trajectory and speed can help the players to improve their accuracy.

Computer vision in entertainment

Gaze estimation – an important branch of computer vision technology

Gaze estimation is a relatively new branch of computer vision technology. It allows tracking the user’s eye movements and attention to a specific point on the surface (usually a screen). It provides for various research purposes, especially in marketing and the automotive industry. Our computer vision experts are currently working on a gaze estimation solution for our clients to help them better understand the customers’ experience.

Listen to Karol Duzinkiewicz talk about Gaze Estimation.

Watch the video

Here’s a word from our computer vision expert on the importance of the technology, as well as what are some aspects to watch for:

I think that a key field in which computer vision will play a key role in Ethical AI. Ethical AI is a subfield that adheres to specific guidelines regarding preventing discrimination and manipulation and maintaining the privacy and individual rights. It places significant importance on determining the right and wrong uses of AI. Ironically, we will need AI to counter “rouge AI, ” which is developed and/or utilized for unethical purposes. One such example is countering deep fakes, which are powerful tools, especially in politics and warfare, through the destabilzation of a nation with speeches that never happened. AI developed by ethical practitioners will be crucial for limiting the harmful effects of recent advancements in the field.

Michał Ostyk, Computer Vision Engineer,

The future of computer vision systems

The computer vision field is gaining momentum and importance. It can contribute to improving both everyday lives and businesses. Modern technologies like AI and machine learning are helping to make significant advancements. Enabling computers to process and interpret images can contribute to developing new technologies and devices that can automate tasks and improve human work.

The industries currently top in CV are autonomous vehicles and machines, medicine and healthcare, image processing and content organization and creation. However, due to the rapid development of emerging tech, we can expect entirely new solutions to occur.

Eye tracking and gaze estimation technologies are set to revolutionize the way we interact with our devices. With the advancements in machine learning, these technologies will enable us to control our devices with just our eyes. This will make it possible for us to do away with traditional input devices like keyboards and mice and instead use our eyes to navigate and control our digital devices. This will have a huge impact on the way we do business and interact with technology.
– Kevin Grace – Head of Marketing at iwoolfelt

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Katarzyna Świątek

Junior Content Specialist at


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