Computer Vision: The Present and Future

Let’s start by thinking about how vision can be. Most people rely on it to prepare food, walk around obstacles, read street signs, watch videos, and do hundreds of tasks. Vision is the highest bandwidth sense; it provides a fire house of information about the state of the world and how to act on it. For this reason, computer scientists have been trying to give computer vision for half a century, birthing the subfield of computer vision. It goals to give computers the ability to extract high-level understanding from digital images and videos. As everyone with a digital camera or smartphone knows, computers are already really good at capturing photos with incredible accuracy and details — much better than humans in fact.

When we see the image above, we can recognize that the group of people running. This ability of humans to recognize images that we see happens in our brains which is the results of millions of years of evolution.

What is Computer Vision?

By definition, computer vision is a subsection of artificial intelligence that aims to teach computers to look at images and videos and “understand” them.

So, when we see a picture of cute dog, we know that this is the picture of cute dog.

But this task extremely hard for a computer, because while we see a picture of a dog, a computer actually sees an array of integers.

So, do we make computers see like us.

One way is to use Deep Learning.

Deep Learning is a machine learning technique which allows computers to do what comes naturally to us learning by examples.

So, let’s talk about computer vision what are some advanced topics in computer vision that beginners often want to learn about.

Object Detection:

As you can imagine object detection is very useful for applications like self-driving cars. If a car is going to be driving on the road autonomously it has to be able to identify other vehicles, traffic lights, pedestrians, bicycles and even just basic things like lane markings. The concept of self-driving not only apply to cars but also to trucks, drones, military robots or even to vacuum cleaners and surveillance systems.

Another huge application is kitchen. Simply, put robots, they can work faster than humans. If they can actually do the job that humans are currently doing such as say picking fruit and food. The best of all, robots don’t get tired.

Facial Recognition:

Another major application of computer vision is facial recognition. Today facial recognition helps you unlock your phone, in mobile phones while capturing photos, in Facebook while trying to tag a friend and in the corporative world this kind of technology that might be used to let you gain entry into your office building.

Image Classification:

It is a process of identifying objects in images. Although its really easy for us humans, it is extremely difficult tasks for computer. But with deep learning computers are able to do that. Some really amazing uses of image classification is actually in securities, like identifying faces in CCTV footage and even in real in airport. Another example of image classification is in health care industry and how it can able to identify tumors in scans of endoscopies and biopsies.

Image Colorization:

It basically adds color to black and white images. However, achieving the real color of a black and white images is extremely difficult. In a grayscale image, it only has one kind of information about the image and that is its intensity. So, to determine the color of each of these pixels in the image turns out each of the pixels in a black and white. Image has 313 different color possibilities to choose from. So essentially this is the biggest problem that image colorization is trying to solve. So, image colorization is used to color black and white images especially old black and white images.

Thanks for reading!