Computer Vision : What and How???

AI (Artificial Intelligence) has become one of the hot topics in today's era. Within this vast topic, Today I am going to discuss about Computer Vision .

One of the most powerful and compelling types of AI is computer vision—which you’ve almost surely experienced in any number of ways without even knowing it. Here’s a look at what it is, how it works, and why it’s so awesome (and is only going to get better).

What's Computer Vision??
Computer vision, or CV for short, is an academic term that describes the ability of a machine to receive and analyze visual data on its own, and then make decisions about it. That can include photos and videos, but more broadly might include “images” from thermal, or infrared sensor, detectors and other sources. CV is already in use for a number of purposes, but on the consumer level, it is already relied upon by remote control drones to avoid obstacles, as well as by cars from Tesla and Volvo, among others.

Why Computer Vision??
CV allows computers, and thus robots, other computer-controlled vehicles, and everything from factories and farm equipment to semi-autonomous cars and drones, to run more efficiently and intelligently and even safely. But CV’s importance has become even more obvious in a world deluged with digital images. Since the advent of camera-equipped phones, we’ve been amassing astonishing amounts of visual imagery that, without someone or something to process it all, is far less useful and usable than it should be. We’re already seeing computer vision help consumers organize and access their photo collections without needing to add tags in, say, Google Photos, but how to stay on top of the billions of images shared online every day (approximately 3 billion, according to Mary Meeker).

To get an idea of how much we’re talking about here, last year photo-printing service Photoworld crunched the numbers and found it would take a person 10 entire years to even look at all the photos shared on Snapchat—in just the last hour. And of course, in those 10 years, another 880,000 years' worth of photos would have been already been spawned if things continue at the same rate. Simply put, our world has become increasingly filled with digital images and we need computers to make sense of it all—it’s already well past human capabilities to keep up.

How Computer Vision Works??
On a certain level CV is all about pattern recognition. So one way to train a computer how to understand visual data is to feed it images, lots of images—thousands, millions if possible—that have been labeled, and then subject those to various software techniques, or algorithms, that allow the computer to hunt down patterns in all the elements that relate to those labels. So, for example, if you feed a computer a million images of penguins, it will subject them all to algorithms that let them analyze the colors in the photo, the shapes, the distances between the shapes, where objects border each other, and so on, so that it identifies a profile of what “penguin” means. When it’s finished, the computer will (in theory) be able to use its experience if fed other unlabeled images to find the ones that are of penguins.

Microsoft recently created an algorithm that incorrectly identified what was in pictures just 3.5 percent of the time. That means it was correct 96.5 percent of the time.

Fortunately, some of the geniuses at Google thought up another option: Back in 2012, they fed a computer loads and loads of images and let it figure out patterns on its own and see what happened—a process dubbed deep learning. Turns out that, with good enough algorithms, computers are able to find patterns on their own and begin to sort through images without requiring humans to handhold along the way. Today, some deep learning algorithms are surprisingly accurate.

Does this process take a long time?
Increasingly, no. That’s the key to why computer vision is so thrilling: Whereas in the past even supercomputers might take days or weeks or even months to chug through all the calculations required, today’s ultra-fast chips and related hardware, along with the a speedy, reliable internet and cloud networks, make the process lightning fast. Once crucial factor has been the willingness of many of the big companies doing AI research to share their work—Facebook, Google, IBM, and Microsoft, notably—by open sourcing some of their machine learning work. This allows others to build on their work rather than starting from scratch. As a result, the AI industry is cooking along, and experiments that not long ago took weeks to run might take 15 minutes today. And for many real-world applications of computer vision, this process all happens continuously in microseconds, so that a computer today is able to be what scientists call “situationally aware.”