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Computer vision and image recognition are commonly used terms, but the former includes much more than just analyzing images. This is because, even for humans, “seeing” encompasses many other aspects of perception and analysis. Humans use about two-thirds of their brains for visual processing, so it’s not surprising that computers need to employ more than just image recognition to achieve the correct visual effects.
Of course, image recognition itself – the analysis of pixels and patterns in images by computers – is a component of the machine vision process, involving everything from object and character recognition to text and sentiment analysis. However, as Cornell Tech computer scientist Serge Belongie pointed out at the LDV Vision Summit, today’s image recognition still mainly focuses on recognizing basic objects, such as “bananas or bicycles in images.” Even toddlers can do this, but the potential of computer vision is superhuman: capable of seeing in the dark, through walls, observing from long distances, and processing all intake rapidly and in large volumes.
In its most comprehensive sense, computer vision has been applied in daily life and business to perform various functions, including warning drivers of animals on the road, detecting medical conditions in X-rays, identifying products and where to purchase them, and editing advertisements in images. We use computer vision to scan social media platforms to find relevant images that cannot be discovered through traditional searches. This technology is complex, and like all the tasks mentioned above, it requires not just image recognition but also semantic analysis and big data.
So, aside from image recognition, what other uses does computer vision have? Here are some examples and technologies.
Humans cannot “see” heat or gases. In many cases – especially in situations involving fire, wild predators, or gas leaks – these are the types of dangers people want to see before they feel or smell them. Advances in thermal imaging mean this capability has not only been built into portable cameras for industrial and consumer use but has also been integrated into smartphones, as shown by the Cat S60. Eventually, this feature will be integrated into every phone. However, natural disasters are not the only factors that thermal imaging can assist with. They can help maintain fairness in sports competitions, as demonstrated by the infrared thermal cameras used to detect mechanical doping in this year’s Tour de France.
Sensors that detect temperature, light, air quality, gases, and motion are just a few of the sensors used in computer vision to identify specific content. For example, some of today’s smartest buildings use sensors built into lighting and temperature systems to detect the movement of people, optimizing illumination and energy levels, becoming smarter over time. Additionally, home monitoring systems not only use motion sensors to allow built-in cameras to track your dog’s movements but can also combine them with temperature and air quality sensors for a comprehensive understanding of conditions when you are away from home. Meanwhile, in-store sensors and beacons combined with cameras track shoppers’ movements, cross-referencing their “big” behavioral data in the cloud. The ultimate goal is to help retailers optimize store layouts and pricing, while also providing real-time coupons to customers.
X-rays, ultrasounds, MRIs, and other medical tests reveal what is happening inside our bodies, which radiologists and doctors then examine. Applying image recognition to these images would allow for faster and ultimately more accurate detection of health abnormalities, leading to quicker diagnoses and ultimately saving lives.
Today’s semi-autonomous vehicles use sensors, lidar, radar, cameras, and image recognition to “see” what is in front of them. For example, Volvo’s new S90 features a “large animal detection” function that uses radar and cameras with image recognition to alert drivers, even stopping when a deer or moose crosses the road. While LIDAR may appear bulky and cumbersome, it will likely improve in the future, reducing the need for space-consuming 3D and other object detection devices.
Real-time location access via GPS and the cloud can significantly aid in identifying specific things – pedestrians, famous landmarks, or busy roads. This helps programs like Google Photos differentiate between the Eiffel Tower and the Tokyo Tower when tagging user photo collections. Geolocation can also help warn drivers about cyclists they might be about to ride into (and vice versa), adding a layer of safety if the car’s radar or large object detection cameras do not capture them first.
From historical traffic patterns and weather reports to public online behaviors, computer vision can help identify everything from photos taken on hot days to images of cars that might be running low on fuel through cloud-accessible information. For businesses, it can be used to track consumer shopping patterns or understand which advertisements to display.
Like human vision, computer vision is not just about simply looking at things. It requires connections to many other data collection technologies to provide accurate insights, resulting in safer cars, smarter homes, and optimized businesses. Two-thirds of the human brain is used to process visual information. This means computer vision is a crucial component of artificial intelligence (AI) and may even be the most important part.
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