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Computer vision

Computer vision processes visual inputs from thousands of videos and photographs using artificial intelligence (AI).

Computers can easily process images at a pixel level, differentiating between colors, but the next stage is recognizing individual objects and being able to classify images based on what they contain. Computer vision does just that, enabling organizations to detect virtually anything — from suspicious human activity to symptoms of poor crop health — in live or recorded visual data.

What is it?

A field within artificial intelligence and machine learning that teaches systems to respond to and learn from visual cues within visual data.

What’s in it for you?

Computer vision can offer object detection and classification at a higher accuracy than the human eye — and without the hours of manual work.

What are the trade-offs?

The technology is still evolving and isn’t always reliable.

How is it being used?

It has many uses such as disease diagnosis, autonomous vehicle development, facial recognition, CCTV and more.

What is it?


Computer vision is a subset of artificial intelligence that uses machine learning models to process images and video content. Generally, it’s used for object identification, classification and tracking. Some of today’s most common use cases for it include security, where it’s used to detect potential intruders and identify suspicious behavior, and farming, where computer vision can detect health issues in large herds of animals or fields of crops.

 

Computer vision has been in development for decades, but recent years have seen major advances in artificial intelligence and machine learning techniques, making it more sophisticated and viable than ever.

What’s in for you?


Computer vision can automate monotonous tasks, such as reviewing video feeds or sorting through images. And it can do so with a high degree of accuracy while removing the risk of human error. That means it can save you money.


There are hundreds of potential applications for computer vision, including facial recognition, connected security systems, content classification, and augmented reality — so there are a lot of opportunities for organizations to take advantage of the technology, and innovate with it.


And computer vision isn’t limited to the visible spectrum, so you can add extra capabilities, such as infrared vision with little extra work.

What are the trade offs?


Computer vision is great at noticing the things it’s been specifically trained to find. But outside of that, it’s limited in what it can do.



When a novel event happens, a computer vision model can’t identify it as a threat or anomaly, because it has no established frame of reference for it. In the same event, a human could draw on their knowledge and experience to reliably determine what’s going on.


These systems can struggle when dealing with suboptimal conditions — this is particularly pertinent when used for surveillance, where heavy rain or poor lighting can affect their accuracy.


For the time being, the solution is to continue feeding large amounts of data into these systems, to try and fill in these intelligence gaps.

How is it being used?


Many of today’s intrusion detection systems are powered by computer vision. By detecting movement, and scanning faces to ensure that they are cleared personnel, computer vision tools provide a reliable way to detect intrusion fast that isn’t impacted by human error.


In autonomous cars, computer vision is a key component of the vehicle’s ability to process the world around it as it drives, tracking people and objects to improve routing and prevent collisions.


And in the healthcare sector, computer vision is helping doctors identify potential issues in scans and X-rays, helping to accelerate diagnoses and ensure patients get the treatment they need. Some results suggest that computer vision can significantly outperform humans when it comes to detecting some diseases.


But the technology is not without controversies, especially when it used in surveillance — particularly in law enforcement, where bias in training models and errors in identification can undermine trust in the systems.

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