In today’s world human vision is essential, beautiful, and complex. The last decades, people dreamed of creating machines with the characteristics of human intelligence and those that can think and act like humans. One of the most fascinating ideas about computers the ability to “see” and interpret the world around them, And now the fiction of yesterday has become the fact of today.
The advancements of AI and computational power, computer vision technology has taken a massive leap toward integration in our daily lives. The computer vision market is expected to reach $55.8 billion by 2023, making it an extremely promising UX technology.
Computer vision is an interdisciplinary scientific field that deals with visualisation and how computers can gain comprehend the visual world in the form of images and videos. With the help of deep learning models, machines can accurately identify and classify objects and then react to what they “see”.
The 2020 McKinsey Global Survey on AI reveals that 50% of companies have now adopted Artificial Intelligence in at least one business function, with the greatest number of use cases targeted toward product or service development, and optimizing operations.
Computer vision is the field of computer science that focuses on creating digital systems that can process, analyze, and make sense of visual data (images or videos) in the same way that humans do. The concept of computer vision is based on teaching computers to process an image at a pixel level and understand it. Technically, machines attempt to retrieve visual information, handle it, and interpret results through special software algorithms.
Here are the common tasks that computer vision systems can be used for:
Object classification: The system check visual content and classifies the object on a photo/video to the defined category.
Object identification: The system check visual content and identifies a particular object on a photo/video.
Object tracking: The system processes/check video and finds the objects that match search criteria and track its movement.
Computer vision algorithms are based on pattern recognition. We train the computers on a massive amount of visual data, it process images, label objects on them, and find patterns in those objects. For example, if we send a million images of flowers, the computer will analyze all the image, identify patterns that are similar to all flowers and, at the end of they will create a model “flower.” As a result, computer will detect accurately whether a particular image is a flower every time we send them pictures.
Identify these features in images, computer vision algorithms have to consider small regions of pixels, called patches. The mathematical notations used, which is called a kernel or filter. It contains a value for a pixel-wise multiplication, the sum of which is saved into the center pixel.
Another algorithm called Viola-Jones Face Detection was used, which combined multiple kernels to detect features of the faces. Nowadsys, the newest and trending algorithms on the block are Convolutional Neural Networks (CNN).
Computer vision is already integrated in many areas of our life. Below are just a few notable examples of how we use this technology today:
Apple Photos is an excellent example. The app has an access to our photo collections, and it automatically add tags and notations to photos and allows us to browse a more structured collection of photographs. What makes Apple Photos great is that the app creates a curated view of your best moments of your photos.
In the For You section of Photos for iOS, you can see featured content that the app created so you can view your favorite moments.
Facial recognition technology is mostely used to match the photos of people’s faces to their identities. This technology is integrated into major products that we use in our daily life. For example, Facebook is using computer vision to identify the people in photos.
Nowadys Facial recognition is a crucial technology for biometric authentication. Many smartphone devices available on the market today allow users to unlock devices by showing their faces. The beauty of this technology is that it works faster.
Computer vision enable the cars to make sense on their surroundings. The smart vehicle will have a few cameras that capture videos from different angles and send videos as an input signal to the computer vision software. The system will processes the video in real-time and detects objects like road marking, objects near the car , traffic lights, etc in faster. Examples of applications of this technology is autopilot in Tesla cars.
Computer vision is a core element of augmented reality apps, in this technology AR apps detect physical objects (both surfaces and individual objects within a given physical space) in real-time and use this information to place virtual objects within real environment.
The Ikea Place app uses AR to help users understand whether the furniture they want to buy will fit into their interior.
As we increasingly look to Artificial Intelligence to help solve a range of real-world challenges, this means computer vision will, by necessity, have a significant role to play.
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