Computer vision is already a reality in our daily lives: understand the concept and its applications.
In 2023, more than ever, the world’s attention has turned to Artificial Intelligence (AI) and its numerous possibilities, comprised of various technologies that relate to them. One of them corresponds to Computer Vision, also known as Computational Vision.
Although many may not be aware, they are already using this technology, which is becoming increasingly advanced and powerful every day. It is present in social networks, in mobile systems, and even in automobiles. The trend, in fact, is for it to expand even more every day.
That’s why anyone with an interest in technology, especially in AI, cannot afford to overlook delving into this concept. The opportunity for that is right here: continue reading and understand everything about the subject!
What is Computer Vision?
Computer Vision, or Visão Computacional, is a field that belongs to AI. Its goal is to empower computers and systems to interpret images, enabling them to understand and take actions based on them.
Therefore, it operates in a way that allows machines to see, understand images (photos, videos, drawings, etc.), extract relevant information, and make decisions based on these visual perceptions.
Since machines do not have corneas, pupils, retinas, and optic nerves, which are responsible for decoding images and interpreting them by the human brain, they require their own structure. This, in turn, is composed of data, algorithms, and cameras.
It is also important to note that human vision is not solely based on the structures capable of capturing images, such as those that make up our eyes. It also relies on memories, context, and references that accumulate from an early age. Machines do not have access to this. Therefore, Computer Vision requires training to enable object recognition, differentiation between different things, and even an understanding of concepts like distance and positioning.
Despite these challenges, this technology that teaches machines to see is already quite advanced and, as it seems, will continue to grow. Understand how it works, and then learn about some applications where computer vision is present.
How does Computer Vision work?
This AI-based technology employs a variety of strategies to teach machines to see and understand what they see. These strategies include algorithms, deep learning, and convolutional neural networks (an algorithm that captures an image, assigns importance to it, and distinguishes it from other images).
These techniques make it possible to train machines. The training involves the repeated analysis of a vast amount of data that enables them to understand what is in front of them, how it differs from other objects, and the ideal state of something.
For example, consider a machine responsible for analyzing product quality. First, it needs to understand what the product is it’s looking at. This includes distinguishing it from other items in the same environment, as well as tools that may be related to it.
Likewise, it should recognize whether the product is in good condition or not. This can often depend on minor flaws that might go unnoticed by the human eye.
Therefore, training machines based on Computer Vision is only possible when millions of images and videos with labeled information are made available to them.
This is followed by complex processes involving edge detection, segmentation, and pattern recognition. In the end, all of these elements come together to enable the machine to recognize and identify objects, patterns, movements, and even emotions expressed in images.
What are the applications of Computer Vision?
This technology is widely applicable. It is already present in several areas and machines of different complexities, but it should soon reach new levels, which is due to its constant development.
See some of the scenarios in which Computer Vision is already present and how it is useful in each of them:
- Medicine: applies Computer Vision to medical diagnostic tasks based on images such as X-rays and MRIs.
- Automotive: widely uses the technology, especially in autonomous vehicles that need to understand paths and the obstacles within them. Based on this information, important decisions are made for driving and navigation.
- Security: utilizes Computer Vision for object or person identification in video surveillance systems.
- Entertainment: relies on machine vision for the development of virtual and augmented reality applications, leading to more immersive interactions.
Examples of using Computer Vision
As mentioned above, there are numerous areas where Computer Vision can be applied and prove useful. In fact, you probably already use this technology without realizing it. Here are some of the tools where it is already present and how it is used:
Facial recognition on mobile devices: Do you use facial recognition for unlocking and accessing your phone and the apps on it? Well, this is nothing more than the use of Computer Vision for user authentication and security.
Augmented reality filters on social media: The filters and effects used to edit photos on social networks like Instagram and TikTok, whether to add animal ears, slim the face, or change the user’s appearance, also rely on Computer Vision.
Email spam filters: Many email services use Computer Vision algorithms to identify unwanted messages, based on their visual content.
Smart home virtual assistants: Virtual assistants like Google Home use computer vision to interpret gestures and visual commands made to them, whether to turn off the lights in a room or turn on the television.
Parking assistants in cars: Many cars now have assisted parking systems. These systems use vision sensors to detect obstacles around the vehicle to assist the driver in parking safely.
QR code reader apps: Another common use of Computer Vision in daily life is through mobile apps (like Google Lens) that read QR codes printed on products, menus, or even on a computer or TV screen.
License plate recognition at toll booths: Automatic toll systems also use technology to teach machines to recognize vehicle license plates and automatically collect tolls.
Visual translation apps: Apps like Google Translate can translate text in images taken with a mobile phone camera. This is another common use of Computer Vision in our daily lives.
Next steps for Computer Vision
Just like other technologies, Computer Vision is in constant development and improvement. That’s why it should become increasingly prevalent, going beyond the applications we see today.
It will be able to take on more complex tasks and make important decisions. Moreover, the broader technological advancements enable this AI tool to surpass the limitations and boundaries of human vision.
For example, one expectation involves the creation of 3D sensory images. With this capability, it’s as if machines can fully comprehend the space around them. They can digitally recreate it with high precision, including distances and scales.
The very concept of deep learning, used in the development of Computer Vision, is likely to benefit from its expansion. After all, it’s yet another machine learning resource that feeds upon itself.
Similarly, applications based on SLAM (Simultaneous Localization and Mapping) are expected to become increasingly autonomous and secure. These are essentially autonomous devices that map their surroundings, locate themselves within it, and move through it.
The improvement of agriculture, such as crop monitoring and pest control using Computer Vision, is something we should see more of in the future. The same applies to assistive and accessibility technologies for people with disabilities, as well as medical use for precise diagnostics and surgeries.
Conclusion
Computer Vision is a field that opens up a wide range of possibilities. This AI-based technology is not only already present in various everyday devices, but it is expected to appear in many other innovations.
That’s why it’s a technology that’s here to stay and has enormous potential to transform our lives in various ways, whether by making access to information easier or by enhancing our safety in different aspects, such as in traffic and at home.
In the coming years, we should encounter a host of new applications, some of which may even be unimaginable at this moment. Therefore, Computer Vision is the face of the future, and paying close attention to it now is a way to stay ahead of the curve.
