Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from visual cues such as digital images, live or recorded videos, special wavelength cameras and more. As the name implies, it grants computers the ability to see the world around them with the help of sensors and cameras. Today it is possible for systems to directly analyze the surroundings in real time and draw conclusions and write data based on what it sees.
Computer vision works by using deep learning to form complex algorithms that enable systems to analyze and draw data based on imagery. An image is made up of tiny dots known as pixels. Using cameras, a computer is made to process an image pixel by pixel. In a way, computer vision is an attempt at trying to mimic the human eye. Humans are able to identify and process images instantly by looking at them and this is what computer vision aims to achieve.
Just like we’re trained as kids to identify patterns or spot odd objects, the AI is trained to identify images. Millions of different images of the same object are fed as data until the system is able to identify a similarity or a pattern in those images. The data goes into the image recognition model of the system. Once it has this information, it will be able to identify the object even if a different image is used. The more data of the object the more accurately it will be able to identify it. Deep learning and computer vision algorithms make the process of turning an image into data faster.
Computer Vision Development: Complex?
There’s several ways of how you could get into computer vision development. The only prerequisites being a thorough knowledge of computer science and image recognition. Developing for computer vision and AI aren’t too different, after all one relies on the other to function.
You can either start from scratch, that is build your own image recognition model and train it before implementing it into your application or use an already available API to add computer vision functionality. Starting from scratch requires more time and development experience while adding an API just requires you to get familiarized with the documentation. That being said, to get a model to work exactly the way you want and with accuracy takes a lot of training. For example, say you want your model to identify a car, you will feed the model thousands of images of that exact car and only hope that it’s enough for it to recognize it. This process takes an enormous amount of time and has a chance of having poor end results.
Thankfully, cloud based AI platforms make the process a lot easier. Platforms like Google Cloud and Microsoft’s Azure have tools that enable developers to design,create and train computer vision models. Being cloud based platforms all the computing is done on cloud servers so your computer doesn’t have to do the intensive work. If you still want to do it all on your own, using something like Tensorflow, which is a machine learning library is probably the best route. It’s used by companies like Google and Intel for its advanced machine learning applications including computer vision.
So where is it used?
You may not realize it but you’re probably already using some form of computer vision. How do those fancy Snapchat filters work? Yep, it's done using computer vision. It's not just filters. Computer vision has applications in critical fields such as medical, defence and even education. As new systems with more processing and analysis capabilities pop up the better and faster computer vision gets. Here’s some of the existing applications of this technology:
Magic Leap, Google Lens and Other AR headsets
If you haven’t used it yet, Google Lens is an application developed by Google that gives you information on all sorts of things just by pointing your phone’s camera at them. It can be used to scan text, read barcodes, identify items etc. It returns information such as translations, definitions, web results and similar images. In fact, it's so good that you can connect to a WiFi network just by scanning the sticker containing the network’s name and details. Lens is also part of other google apps like Google Photos to identify images of similar people.
Cancer can be deadly if not treated at the right time. Due to progression in computer vision technology, it is now possible to identify moles from melanomas with human accuracy. Cancer variants like skin cancer can be hard to identify even to the trained eye as it resembles common skin diseases. As a remedy scientists have developed computer vision cameras that are able to correctly identify the signs of cancer from the skin. This has never been possible before and is truly a remarkable application of the technology.
The places you could call ‘safe’ are decreasing day after day. Computer vision is being actively used to monitor places for suspicious activity. It’s also used by the police to identify the faces of criminals even if their faces are half hidden. Using AI it is possible to cross reference the image to a large database of citizens within a few hours. It’s also used along with motion detectors to only trigger when a human being is detected in the area. Systems like these are used in banks and places that require high security
For years people have relied on the human eye to identify the time of harvest for crops that have been farmed. This isn’t accurate and is time consuming with larger farms. Now with the aid of computer vision it’s possible to monitor and track the growth of an entire field simultaneously. Machine learning algorithms along with computer vision are able to detect even the smallest changes in the growth of crops and this allows farms to run more efficiently
Conclusion: The Future of Computer Vision
There's no doubt that AI with the aid of computer vision is going to play a huge part in the future of humanity. The rise of better and more trained AI models means that the computer vision program has more data to work with resulting in faster and better identification. Soon enough we’ll be seeing computer vision used more in sectors like marketing, healthcare, traffic, transportation and other industries.The trends prove that developers are interested in the technology and will continue to create more life changing applications. It’s something that allows already existing programs to take in more data dynamically and the possibilities are limitless.