The Rapid Advancement of Computer Vision and Its future Implications
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Around last two Years ago, Apple launched the iPhone 10, which marked over a decade of smartphone production by the company. This new model broad sets in new features, like the bezel less screen and wireless charging, but none of those features as huge as the one path into that tiny notch in the front of the phone with face ID up were the first to introduce computer vision to millions of people around the world.
Now I don't expect most people to know the actual term for it, or even understand the specifics of how it works. But computer vision is poised to completely change how we interact with machines in the future.
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Computer Vision is a field of artificial intelligence, which aims to give computers, a visual understanding of the world around them by image processing techniques on machine learning. This is not to be confused with machine vision, however, which is used in industries to automatically analyze and inspect predictable or known events.
The field of computer vision emerged in the 1950s when Frank Rosenblatt invented a perceptron, the machine used a very early artificial neural network, and was able to sort images into very simple categories like triangles and squares.
The goal of computer vision has always been to emulate human vision, using digital images to three main processing components, which are
1. image acquisition
2. image processing
3. image analysis and understanding
To properly explain how all these processes combined to make a computer vision system. I'll use the iPhone 10s face ID.
Face ID uses a combination of light projectors and sensors to take several images of your facial features. In order to form a 3d map. This serves as a combined image acquisition and processing phase, which ends up sending a 3d mathematical template of your face to the neural engine within the smartphone. The final process occurs when the user tries to unlock the phone again.
The aforementioned 3d template is then used to compare with the image acquired in real time. And once authenticated, the device is unlocked. To be honest, apples, application of computer vision is only at the tip of the iceberg. When we consider just how much we can achieve with this technology, Once that's fully mature.
We will debuted the visual search tool known as Google lense, late last year. And today, owners of Pixel phones can get details on just about anything they see by pointing their cameras at it.
self-driving cars are all the rage today in the tech sector, as companies like way wayman, Uber and GM fight to be the first to give customers access to true, autonomous vehicles. What do you get when you add robots which can carry out repetitive actions, plus computer vision?
You can service bots and they are coming for low skilled jobs. And when I say low skilled jobs, I mean everything from food production to secretarial jobs. These robots will be capable of carrying out tasks without the assistance of a human being.
Fashion is yet another sector which could benefit greatly from computer vision in 2016 Couture designer Jason Grech worked with IBM Watson to design a 12 piece collection called cognitive couture. The collection was made by analyzing 10 years of runway fashion on social media to help the designer explore favor trends, colors, and textures.
Of course the technology this powerful was inevitably going to find its way into government surveillance platforms, Europe, America and China have all begun applying computer vision to hunt criminals or those intending to commit a crime. As if that wasn't bad enough, Google, the same guys who profess the Don't be evil mantra, not too long ago are openly aid in the United States drone program, known internally as Project Maven project aims to speed up analysis of drone footage by automatically classifying images of objects and people.
Other possible uses of computer vision can be found in fields of sports refereeing and medical diagnosis. This is of course not a comprehensive list.
However, considering there are tons of applications which will spring up in the coming years. For those to find out the prospects of drones hunting them down, autonomously in the not too distant future, have some good news. Current machine learning techniques utilized for computer vision require significant computational power and data processing the data in particular is a big problem.
Even reports that big companies like Tesla Weymo, and Uber employed thousands of people offering offshore outsourcing centers in India or China.
The primary jobs of these workers is to teach the self-driving cars to recognize pedestrians, cyclists and other obstacles to workers do this by manually marking up or labeling thousands of hours of video footage, often frame by frame, taking from prototype vehicles, which operates are on test beds, such as Silicon Valley.
The primary reason why this manual approach is utilized, is because self-driving vehicles, simply lack the computational power to track stationary and moving objects, simultaneously in real time.
In a nutshell, it might take at least another decade, or even to to bring reliable and precise computer vision to the mass market, or make no mistake, it will come eventually. And when it does, it'll change our perception of the world, almost completely.
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