Though early experiments in computer vision started from the 1950s and it was initially put to use to distinguish between handwritten and typed text from the 1970s. Today the applications for computer vision have increased exponentially. In this article, we will share with you some of the recent implementation trends of computer vision.
What's Computer Vision (CV)?Computer vision is the use of computers which process visual data and then make conclusions from it or gain understanding about the situation and the surroundings. One of the factors behind the growth of computer vision is the amount of data today which we use subsequently to train and improve computer vision machines.
We have a bulk amount of visual data in the form of images and videos produced by built-in cameras of our phones alone. However, while visual data can include photos and videos, it can also get information from other sources and detectors. Besides with the massive amount of visual information (over 3 billion pictures are shared online every day), the computing ability requires to examine the information is now accessible and cheaper. Since the field of computer vision has raised with new hardware and algorithms so has the accuracy rates for item identification. In under a decade, today's systems have attained 99 percent accuracy from 50 percent making them more accurate than humans at quickly reacting to the visual input signal.
How Can Computer Vision Work?One of the main components to understanding all of the capabilities of artificial intelligence is to provide machines the power of vision. To emulate eyesight, analyze and process, machines will need to acquire and comprehend graphics. The growth in achieving this landmark was created learning procedure made possible. It starts with a dataset with advice which aids the system to learn a particular topic. If the goal is to detect videos of cats as it was for Google in 2012, the dataset used by the neural networks should get videos and images with cats as well as examples without cats. Each image needs to be tagged with metadata that indicates the right answer.
When a neural network operates through signals and data it has found a picture using a kitty; it is the feedback that is received regarding if it was correct or not that helps it improve. Networks are currently using pattern recognition to differentiate distinct pieces of an image. Rather than a programmer specifying the attributes which make like having a tail and whiskers, a cat, the machines learn in the millions of pictures.
7 Awesome Examples of Computer Vision
Imagine all the things human sight allows and you can begin to recognize the endless applications for computer vision.
1. Self-Driving Vehicle
Computer vision is essential to empower self-driving cars. Manufacturers like Tesla, BMW, Volvo, and Audi use detectors, lidar, radar, and detectors to obtain images so that their automobiles can detect lane markings items, signs and traffic signals to safely drive.
2. Google Translate app
All you have to do is to read signs in a language that you don't understand and to point your cellphone's camera towards and let the Google Translate app tell you exactly what it means in your favorite language immediately. This is, using optical character recognition to see the image and augmented reality to overlay an accurate translation.
3. Facial recognitionChina is definitely on the cutting edge of using facial recognition technology, and they use it for police work, payment portals, security checkpoints at the airport and even to dispense toilet paper and prevent theft of the paper at Tiantan Park in Beijing, among many other applications.
4. HealthcareConsidering that 90 percent of all medical data is picture based, there are various applications for computer vision in medication. From allowing new medical diagnostic methods to analyze X-rays, mammography and other scans to monitoring patients to identify issues sooner and assist with surgery. Our health care institutions and professionals and patients will benefit from computer vision now and much more in the future as its rolled out in healthcare.
5. Profession sports monitoringBall and puck monitoring on sports has been common for a While now, but personal computer vision is also helping play and strategy analysis, player ratings, and performance, and to track the brand sponsorship visibility in sports broadcasts
At CES 2019, John Deere featured a semi-autonomous combine harvester that uses artificial intelligence and computer vision to examine grain quality since it gets to discover the perfect route through the plants and harvest. There’s also a possibility for computer vision to identify weeds so that herbicides can be sprayed directly on them instead of on the crops. This is expected to reduce the number of herbicides by 90 percent.
Computer vision is helping producers operate more intelligently and effectively in various ways. Maintenance is only one example where equipment is monitored to intervene prior to a breakdown could lead to expensive downtime. Packaging and product quality are monitored, and defective products can also be reduced with computer vision.
There is already a huge amount of applications for technology and computer vision is still young. As machines and people continue to associate, the workforce that is human will be freed up to focus on tasks because the machines will automate processes that rely on picture recognition.