What is image recognition?
There are many different kinds of artificial intelligence, one of the most important being Computer Vision (CV), which allows computers to, in simple terms, gain a sense of sight through the use of software. More technically, it refers to the ability of computers to collect, analyze, and process visual data, which comes from visual inputs, such as cameras, heat sensors, LIDAR, and so on.
After a computer (or mobile device) has been fed with data, it needs to process it. To enable it to do so, developers create advanced software that applies algorithms to images to make the data they contain understandable to computers. Using such technological paradigms as Deep Learning and Machine Learning, they create programs that analyze the visual input (such as photographs) and allow the computer to identify and compare the objects it sees.
Image recognition is a rapidly developing branch of software engineering. It has a bright future ahead of it, and it has already been applied in many cases – read on to learn about some of them.
Image recognition is extremely useful in the fashion industry. Imagine walking through the street and seeing an outfit you like. Without image recognition, there is little you can do with that information except maybe try to remember the way it looked and search for similar items on your phone or when you next go shopping. However, with mobile image recognition, the system can instantly provide a user with the brand of the particular piece of clothing, along with links to a website where you can order it immediately. This is a great benefit to fashion enthusiasts, online retailers, and clothing brands.
When it comes to medicine, two things are extremely important: prevention and accurate diagnostics. Image recognition can help with both. Computer vision can aid in such areas of preventative care as early cancer detection – a Machine Learning algorithm can help you track suspicious spots on your skin, ensuring that you get in touch with a dermatologist if they appear malignant.
Computer vision algorithms can also aid physicians in providing correct diagnostics. Even the most experienced oncologists can’t read MRI or X-ray imagery with 100% accuracy, but their success rates improve significantly when they are assisted by a piece of software trained on millions of similar images.
Smart cities are all about improving the way we live together in urban areas. Image recognition has a number of applications in this segment. One of them is automatic license plate tracking to make things like paying for parking spaces or toll roads easier. There are also security applications. For instance, street cameras equipped with image recognition software could notify police about the need to intervene without the necessity of a human operator watching the stream. Computer vision could also help municipal governments track things like overflowing trash bins and potholes. The possibilities are endless.
Image recognition is the backbone of the holy grail of innovation in the automotive industry – self-driving cars. A car equipped with the right array of sensors and cameras could very soon be capable of driving itself in any conditions – rain or shine, traffic jam or empty roads, dark of night or light of day, you name it. The work on this is very advanced, with efforts from such companies as Volvo and Tesla being probably the most famous. But image recognition in the automotive industry is not just about self-driving cars – it’s also about collision prevention, automatic braking, and parking assistance. Even if it’s still a while before you can doze off behind the wheel during your commute from the suburbs, you can already take advantage of this technology in a number of modern car models.
Image recognition has many applications in social media. One of the most popular is automatically tagging pictures with the names of the people present in them; another variation of this is identifying faces on photographs and prompting users to tag them themselves. Computer vision can also offer much more in the social realm: using modern technology, you can build applications that mine user-published images for data (for example, the products they use or the mood they’re in) and use it for business intelligence.
Image recognition can also be applied to business operations. To name one use, documents can be analysed automatically – paper invoices and other print-outs can be automatically scanned, converted into plain text, and stored in a database for easy searching and retrieval. The technology used to convert images to text is called OCR (optical character recognition) and it saves thousands of hours compared to manual data entry.
Another use of image recognition in business is related to e-commerce. Computer vision combined with AR can be utilized to suggest similar products to users who submit a photograph of an item they’re interested in purchasing.
Potential applications of Image Recognition
Image recognition can be used for document tagging, a process that uses software to automatically enrich scanned paper documents with metadata, making them easy to search and cross-reference. This is a boon if your company has a large archive of legacy data, such as financial documentation or contracts, that are is still stored on paper. Investing in document tagging can transform a paper-based archive from a bureaucratic burden into a useful source of business intelligence. Plus, there’s the benefit of redundancy and cheaper storage.
An image is worth a thousand words, especially when it comes to shopping. Potential customers often know what their desired purchase looks like, but don’t know what it’s called or what brand makes it. Image recognition can be harnessed to provide recommendations of visually similar products based on user-submitted photographs, making it easier for potential buyers to find an item that caught their eye.
Image recognition can be employed to report accidents automatically. The technology for accident reporting can be implemented as part of many different devices: CCTV in an office building or on a factory floor, stoplight cameras, or cameras built into modern cars. This enables first responders to react to incidents more quickly and arrive at the scene with more information. Another use for this is insurance adjusting – having an accurate recording of an accident makes it much easier to settle claims.
Image recognition can be used to recognize faces in still images and video. This has multiple practical applications: crowd counting (although this is quite a complex problem), sentiment analysis, or tracking down criminals whose photographs are stored in a governmental database. A well-implemented face recognition algorithm can help your business accurately count the number of guests who attended an event or assess the mood of visitors in a physical store. The possibilities are endless.
Image recognition can also be employed to automatically detect tumors in medical imagery. Data from MRIs and X-rays is normally interpreted by humans, but even experienced and highly-trained physicians sometimes miss a tumor. Assisted by image recognition technology, doctors can spot tumors more easily before it is too late and help patients who might have otherwise been untreated. In addition, lower-resolution imaging techniques (such as low-dose computer tomography or computer tomography without contrast) can be enhanced with image recognition to achieve the same results as with traditional methods, but with the patient receiving a lower dose of radiation.