The role of AI in computer vision

Overview

You’re unlocking your smartphone. No need to provide the secret code, look at the screen, and bingo, your smartphone unlocks! You marvel at the ability of the technology that can recognize your face and unlock the phone for you. It won’t open the phone if someone else looks at the screen just you! How does it know that it’s you? You’ve just used AI-driven computer vision technology. It can recognize visuals or images around it based on its learnings from the data it consumes. It’s not that it can recognize human beings or other objects as we do, and it never will, but it can recognize patterns and shapes of the images based on the data. AI has been playing the central role in powering computer vision and there have already been multiple use cases. We use it in our day-to-day lives, perhaps unknowingly, and it’s being used in almost all industries.

What is computer?vision?

Computer vision is a technology that enables computers to recognize or identify objects visually. For example, it can recognize and distinguish between a car and a human being. How does it do what it does? The technology runs on a massive amount of data from which it learns. It is enabled to consume and analyze data, its types, patterns, and characteristics, for example, and over time, identify an object. It’s an extremely complex and layered technology. For example, your smartphone doesn’t know that it’s you, but it stores your facial data and its analysis and patterns that are distinct from anyone else. So, when you look at the screen, it matches the visuals with the data it has stored, and upon a match, it unlocks the screen.

Role of AI in powering computer?vision

AI and machine learning technologies play a crucial role in powering computer vision technology. AI enables computer vision to understand, recognize, and analyze all types of visual data. AI models, logic, and models can fast consume, absorb, and learn from the huge amount of labelled and unlabeled visual data. It enables computer vision-enabled computers to recognize the various diverse features, patterns, and relationships in videos, graphics, and even infographics.

Machine learning, which is part of a type of AI, has been at the forefront of enabling computer vision. Machine learning uses humongous volumes of training datasets from which it can learn about patterns. The algorithms or logic in machine learning can detect the features, characteristics, or objects within the most complex images. For example, it can identify the nose in a human face and identify it as one. Not only that, it can distinguish between various parts of an image. It will not confuse the nose and the eyes.

Machine learning can perform image segmentation on the most complex of images and detect anomalies. Image segmentation enables computers to categorize an image into its logical parts. For example, it can divide a car into its characteristics, such as windows, windscreen, wheels, and steering. Image segmentation enables it to identify the various logical parts. Not only that, image segmentation goes deeper down to analyze and identify various characteristics of each part. Obviously, the whole process is extremely complex and layered and a lot is there at stake. The data recognition and analysis must be spot on else there could be erroneous conclusions. For example, think of the disastrous consequences when an autonomous car is moving on the road and it confuses a pedestrian wearing a striped shirt with the zebra crossing.

Use cases

There are multiple use cases of AI-powered computer vision. Computer vision is being used in various industries and reports have been coming in, though early, that organizations have been reaping a lot of benefits. Some examples are described below.

Medical imaging

AI algorithms can help medical professionals analyze various imaging documents such as X-rays and Magnetic Resonance Images and detect anomalies and problems and help with better diagnoses. For example, computer visions are trained with large datasets to identify problems in mammograms that can detect breast cancer.

Autonomous vehicles

Computer vision in autonomous vehicles enables them to interpret their surroundings as they move. Autonomous vehicles are not controlled by human drivers. Hence, accurate identification of objects and surroundings is critical, else, it can result in a disaster.

Agriculture

AI-powered computer vision is being utilized to assess the quality of the crops, assess the conditions of the soil, and detect the various diseases that can afflict the plants. This technology can be of great help to the farmers that can use it to optimize the yields of the crop and reduce the wastage of resources.

Retail

Retail giants all over the world can use AI-powered computer vision to optimize their supply chain efficiency and improve overall productivity. It can also be used to improve customer experiences and reduce churn. Retail giants use the technology to identify and replenish empty shelves and recommend suitable products to customers based on their browsing or purchasing behavior or preferences.

Surveillance

Authorities have been using AI-powered computer vision to monitor public spaces, such as railway stations, museums, stadiums, and airports to fast identify suspicious behavior or movements of shady people or flag possible threats. As technology evolves, it can prove more potent in preventing crime.

Conclusion

It’s obvious that computer vision has a fantastic future and it has been rapidly evolving. But there are concerns around it that are being voiced. First, as in the case of AI, there is a concern about the privacy and confidentiality of individuals. Computer vision is a data guzzler and people are questioning how the data is being used. Second, with the emergence of deep fake technology, hackers or people with malicious intentions can create deep fakes and confuse computer vision into flagging them. For example, deep fake technology can be used to create panic in public spaces. Lastly, specifically in the surveillance use case, concerns are being raised that individuals’ face recognition can result in their data being abused. People with bad intentions can abuse the facial data captured and inflict atrocities on the people.