Computer vision, also abbreviated as “CV,” is an interdisciplinary scientific approach that aims to develop comprehensive understanding as to how computers can understand the content from digital images and videos. CV is one of the important fields of Artificial Intelligence and Computer Science. CV aims to give computers and machines the kind of tasks that the human visual system can already perform.
We can further elaborate CV here with the example: if we are shown a picture of a huge crowd and buildings, we can infer many conclusions out of it that it may be an event or some personal memory. Computers, however, cannot do that. For them, that picture is nothing but another set of arrays and pixels. People who study CV want to give computers certain information so that they can also perform meaningful reasoning. In order words, CV tackles the problems for computers to see and conclude just like a human brain does.
Problems may be as easy as to address using a hand-crafted statistical method, and as complex as requiring the designing of a machine learning algorithm. Keep reading to learn more about Computer Vision. Later, we will discuss why you should study this multi-disciplinary field.
It is, by far, the best computer vision technique. What happens is that we are given a set of images with distinct features, and now we have to predict the set of images in a completely different and new category. After doing that, we also have to check the accuracy of the predictions. It is obviously easier said than done because certain challenges need to be overcome, for example, image deformation, scales, and viewpoints, conditions of lighting, etc.
Hence, to perform such complex tasks, what we may need is a computer vision algorithm. Within these algorithms, researches do not provide the exact coding; they give the computer machine many examples of the image class. The computer then has to study images and consequently adapt and learn about their visual appearances.
Other techniques of computer vision
- One another helpful technique of CV includes Object Detection. With this, computers detect certain objects in an image, label them, and then output them using bounding boxes. Some businesses that have warehouses and other locations can take the help of this technology. All they have to do is get a robot with a camera and a scanner.
- One more technique involves instance segmentation. Here, recognition of certain instance classes takes place, for example labeling ten buildings with ten different colors. Within this classification process, there is typically the main image, and the end-goal of this entire process is to determine what exactly the picture is.
- Imagine you have bits and pieces of a torn-out old photo that is extremely personal to you. You will try to restore it, right? That is what the computer will do, too, in this new technique known as Image Reconstruction. There will be datasets that will incorporate numerous datasets of current photos so that they can design the corrupted versions of the images that some models have already started learning to repair.
Why should you study Computer Vision?
If you are a university student or even in your early career, you may face this question. Well, the most obvious answer that we can easily provide you is that the abundance of applications derived from this field of study is astounding. It technically means that there is demand in the job market, with higher salaries and guaranteed future scope. The average salary of a computer vision engineer in the United States is USD 99,619 per year. The numbers are attractive enough to drag you towards CV!
While you shouldn’t just base your decision on average salary, you will get to learn a lot, and you can easily make improvements as this is a research-related field. And whenever you come across a significant discovery, you can claim that to your advantage.
Some examples of applications that use CV are Snapchat, Facebook, and Instagram, as they use face-detection software to apply certain filters. Furthermore, gaming and control devices also use it, for example, Microsoft Kinect. Surveillance cameras are usually present at public locations to detect suspicious behavior. Biometric identification also uses face identification and fingerprint matching. While these are some of the examples we mentioned, the opportunities in this field are endless. As we move towards a more globalized world, Artificial Intelligence and related fields are taking over the industrial sector. Hence, you do not have to worry about a bright future, because it is already here!
Final Words: Look around you. Do you realize that machines control almost everything from your TV to your refrigerator, from your phone to laptop? Almost everything is systematic, and technology for sure is taking over. In this tech-based world, you should ponder over the fact if computer science interests you, because if it does, then the world is metaphorically in your fist. Work hard, stay determined, and get cracking onto those interesting algorithms!