How does image recognition work? Image recognition has previously been discussed on our blog. It’s a technology that’s been gaining traction for a while, and we wanted to see what other intriguing and even unconventional ways image recognition software is being used in different industries today.
In healthcare, image recognition applications
Do you ever wake up in the middle of the night, open your eyes, and see the same blanket of blackness you saw when you closed your eyes? For some folks, that is their entire existence.
Apart from the obvious limitations that being blind or visually handicapped imposes, these individuals are also cut off from one of the most important instruments of our generation: social media.
Thanks to the use of picture recognition, Facebook altered the way blind and visually impaired people use and engage with Facebook a few years ago. It may appear simple and uncomplicated at first, but scrolling through Facebook to keep up with friends’ activities can take hours rather than minutes for someone who lives in near or complete darkness.
Automated image recognition, picture classification, and automatic alternative text technologies were coupled by Facebook to provide not just an accurate description of the items in a photo, but also who is even if they’re not tagged in the picture
An accessibility team led by Matt King, Facebook’s first blind engineer, created the functionality for the visually impaired. Since he was in his early twenties, Matt has been legally blind.
This function is powered by the same algorithm that suggests who you should tag in your images. It’s a machine learning picture recognition system that analyses the pixels of a face in an image and creates a “template” that may be used to identify people in the future.
Here are some more examples of machine learning picture recognition. Radiology is perhaps the most well-known application of image recognition technology. Last year, IBM calculated that photos make up at least 90% of all patient data. This has had a significant impact on radiologists, who must examine an increasing number of images every day.
While many medical images are terrible news for human radiologists, deep learning algorithms, which are at the heart of many automated image recognition technology, are thrilled. Data is required for deep learning algorithms to learn. They become better the more they have.
Deep learning algorithms and image classification apps are now surpassing human radiologists in many circumstances, and they are becoming a component of healthcare.
Enlitic, an Australian startup formed by Jeremy Howard, the former president of Kaggle, is another example of machine learning picture recognition. Enlitic is a company that specializes in detecting cancers in CT images of the lungs and offering an early diagnosis. Enlitic’s software was 50 percent more accurate than a panel of radiologists in classifying a lung tumor as malignant in one of their internal tests, thanks to automated image recognition technology.
In the security industry, application of image recognition is used
Face recognition and image identification machine learning are very useful tools in the security sector, particularly when it comes to protecting private property from attackers.
Security systems for the home are nothing new. Many houses have motion detectors installed and are connected to a security company that is open 24/7. The problem with these systems is that they are intrinsically inept. They commonly cause false alarms because they rely on motion or heat detectors, which cannot tell the difference between a homeowner who has forgotten the password, a domestic pet taking a walk in the home, and an actual burglar.
Home security systems can now fight such concerns by incorporating application of image recognition technology. They can distinguish between people and dogs and know and recall household members (independent of lighting or angles).
For example, Netatmo Welcome features a feature that only starts capturing video when it identifies unknown faces. Ulo, a cute owl-shaped personal security device, has a similar feature but goes one step further. When the device detects unfamiliar faces, it will begin streaming live video to the device of your choice.
Law enforcement is also embracing image identification machine learning. Face recognition technology is being used by the South Wales Police in the United Kingdom to help them screen larger events and crowds for offenders.
Instead of working in place of the cops, the system collaborates with them. If somebody with a 59 percent likeness to the suspect is flagged by the system, the match is related to a human officer for confirmation before any action is done. The deployment of an automated image recognition system has greatly reduced expenditures and boosted the police force’s overall efficiency.
In the vehicle sector, image recognition is important
Autonomous vehicles are making great progress, despite the fact that they are not yet generally available. For how successfully cars can traverse the world without a driver, image recognition deserves a lot of credit. Multiple video cameras use lidar and radar sensors to detect traffic signals, read road signs, and track other vehicles, as well as keep an eye out for pedestrians and other obstructions.
The advantages of self-driving cars are numerous and substantial. Autonomous vehicles have the potential to reduce the frequency of accidents, enhance emissions compliance, and relieve traffic congestion. The reason for this is that machines are far better at obeying rules and reacting to unexpected diversions than humans.
Google’s self-driving car initiative For almost a decade, Waymo has been testing and developing self-driving cars. They’ve even constructed a miniature hamlet in the Arizona desert to test their algorithm in various real circumstances.
Self-driving cars require such technological advancements because, unlike other businesses, the margin for error is minimal. Because human lives are at stake, every photo frame the algorithm processes must be precisely assessed in real-time as quickly as feasible.
In the retail industry, image recognition is important
You may never have to try on clothing before buying them again, thanks to image identification machine learning technology.
Several well-known brands, including Topshop and Timberland, have employed a gadget called a visual mirror to try on the whole spectrum of clothing from their collections. The visual mirror can be placed inside or outside of a store to entice customers to enter the store.
The mirror consists of a huge screen with numerous cameras that identify different body parts of the individual in front of it. The mirror will select the proper size for you, and you can turn around to view how the clothes fit on your body from every angle. You can also search for certain colors and styles, making your purchasing experience even more convenient.
Some visual mirrors allow you to take photos of the ensembles you’ve put together, transmit them to your phone, and make a complete inventory of all the items available in the store.
While the visual mirror aims to make shopping easier, a Japanese company developed AI Guardian, a security device that aims to minimize stealing.
The AI Guardian system scans entire bodies rather than just faces and detects “suspicious behavior” based on a training data set that outlines the characteristics of a shoplifter.
After this technology was introduced at a Japanese retailer, shoplifting decreased by 40%. Despite the fact that this technology is not yet widely used, the designers of AI Guardian and other similar security cameras believe it is only a matter of time before it is deployed and the accuracy of the results perfected.