In the field of computer vision, one of the most well-known tools is OpenCV. According to its own authors, the Artificial Intelligence market is projected to be worth as much as $390.9 billion by 2025. Computer image analysis is an integral part of this market—acting as the “eyes” of Artificial Intelligence. It is therefore worth exploring the possibilities it offers. In the following article, we will take a closer look at OpenCV, a standard tool for image processing. What is the OpenCV library and what is it used for?
Let’s start with a basic question about OpenCV: what exactly is this tool? The OpenCV library is a collection of open-source platforms. Originally created by Intel, it is now available for free to all users. OpenCV enables image recognition, object detection, and object tracking. The library was created in C++, but it can be used in other programming languages (C#, Python, Java, JS). It can be used for
business process automation, but it carries much greater potential than, for example, simple image processing via OCR (you can read more about
intelligent data reading in this article). Image recognition is just the beginning of further, more advanced analysis capabilities.
Possible applications of real-time image processing
A key feature that distinguishes OpenCV is real-time image processing. This technology is used, for example, for face recognition and tracking. This significantly simplifies human detection by security cameras. Of course, it can also recognize other objects, such as cars, which is naturally essential for creating autonomous vehicles. Another possibility offered by real-time image processing is human body posture analysis—this is particularly useful in the newly developing feature of trying on clothes in AR mode (
Augmented Reality). In medicine, it can be used to analyze X-rays or MRI scans to detect anomalies—often much more accurately than the human eye of a technician or doctor can. During the Covid pandemic, it proved useful for monitoring safe social distancing in public places. Intelligent analysis can also provide us with interesting insights into how people move within a given space. This is, of course, valuable information for security, but also for office space designers. In retail companies, in turn, it can contribute to better, more logical product placement, which paves the way for shortening production time. More applications of OpenCV can be found—it is worth mentioning, for example, combining images from several cameras or using the software to create human-machine interfaces. The most important fact for entrepreneurs will certainly be that these applications can realistically impact business.
How can OpenCV impact business?
For many companies, OpenCV can be an essential element of
intelligent process automation. It is important in this transformation to analyze the processes we are automating so that they scale appropriately as the business grows. It is worth noting that OpenCV is usually not used in isolation. Combined with IoT (
Internet of Things), the ability for artificial intelligence to “see” offers prospects for digitizing certain activities—for example, virtual counting of items (much faster than manual) or more efficient product indexing. Machine learning, in turn, allows many of these operations to take place without human supervision. We therefore minimize costs and labor time while simultaneously increasing quality. Before introducing automation, remember to select processes appropriately and optimize them beforehand. For more details on how to apply this in practice, we encourage you to read our article at
this link.
What are the advantages of using OpenCV in business?
Using computer vision provides a competitive advantage in the market—giants such as Google, Microsoft, Facebook, and SONY know this well. Pattern recognition by a machine provides opportunities for automation and improvements. Today, there is much talk about
Cognitive Robotic Process Automation (cRPA)—this is an intelligent combination of robotics, artificial intelligence, and machine learning to support human decision-making. Providing data from new sources (such as camera images) is new, valuable knowledge. Thanks to cRPA, humans are relieved not only of simple, routine tasks but also of more complex ones that would normally require the cognitive work of the human brain. Moreover, we can process more of this data faster using technology than with a simple pair of eyes. As a result, we can obtain a more accurate picture of reality and detect more subtle relationships. A computer, of course, does not look at an image the same way a human does. It reads it using values in three channels—values are captured in red, green, and blue. For this reason, mistakes are possible—e.g., when lighting changes. However, computer vision is still being perfected and carries much hope for the future that computer eyes will significantly surpass human eyes. For growing businesses, the choice then becomes obvious—it is worth investing in increasingly perfect technological tools rather than relying solely on biological ones.
