“Just like to hear is not the same as to listen, to take pictures is not the same as to see.” — Fei-Fei Li
Our minds process tons of data every day. While walking on the street, we see a lot of objects and instantly define them without even putting any effort. We look at a flower, and we already know that it is a flower. Such an impressive ability is evolutionary-based. Humanity has developed that skill, the name of which is the vision, for millennia, so now we can easily identify what an object is in front of us.
The essence of vision has excited the imaginations and minds of many bright people. Since the 16th century, scientists have investigated this phenomenon, and today, they puzzle over teaching machines to “see” things. Indeed, they made a big leap in it with so-called computer vision.
So, let’s go into details to investigate this intricate phenomenon and understand what it is, how it works, and why you need it for your next project!
What is Computer Vision?
First off, let’s find a definition of computer vision to understand what it offers to us.
Computer vision is the field of computer science that aims to mimic the human visual system to identify and process objects depicted on images and videos.
The human brain can look at a picture and immediately know what is depicted on it. Our minds indeed do countless tasks related to image processing. Our visual senses are our irreplaceable assistants who provide tons of information and help us process it to comprehend the state of the world and how to act on it.
Impressive, isn’t it? Yes! That’s what computer scientists think too. For half of the century, they’ve toiled over creating computer vision. The goal of this technological field is to give computers the ability to “see” meaning “understand” digital images and videos. Computers are already good at capturing images and videos, but they cannot really process them in the way to understand what elements they contain and what meaning they convey to the world.
What Do We Know About Image Processing?
The image processing is much more intricate and complex. The humans’ visual system perceives images with the help of the eye (for capturing light), receptors in the brain (for accessing light), and a virtual cortex (for processing it). But machines’ visual systems perceive images in pixels, which make the task of “seeing things” more challenging.
Images on computers are stored as vast grids of pixels, which are combined according to the three primary colors: red, green, and blue. Each pixel has its RGB (Red Green Blue) value meaning that they have different intensities of a specific color. Besides, the brightness of each pixel is defined by a single 8-bit number, the range of which varies from 0 (black) to 255 (white).
Here is an example:
Let us say, we want a computer to detect a duck on an image. The machine knows a specific combination of pixels that make a duck. Therefore, computer vision algorithms check each pixel and calculate the difference from the target colors. The computer knows that the duck’s color is a combination of different shades of brown. So, it tries to detect these colors analyzing every single pixel in an object. If most of the pixels match to the targeted, the computer states that it is a duck. In the past, if anything on the photo was changed, a computer could be perplexed to recognize the animal. For instance, if you gave a picture of another duck with different lighting and composition to the computer, it would not be able to detect that it was also a duck.
Today, computer vision has progressed to unbelievable extent. We were lucky enough to see that massive leap in development that took place thanks to the evolution of deep learning and neural networks. More than 3 billion images are shared within the Internet daily and this amount of data is actively used to train computer vision. Big data made it possible to improve the accuracy of computers detecting and labeling objects, which has grown to 99% in recent years.
Where is Computer Vision Applied?
If you think computer vision is something overly complicated and accessible to big enterprises only, you are probably wrong. It is actively applied to solve a number of issues businesses face today. This computer science tries to mimic the way the human brain works, but in essence, it is more about pattern recognition. To train the computer, one needs to feed it with millions of images, and in a perfect world, they have to be labeled.
Since computer vision has revolutionized with the advent of deep learning, you no longer need to code everything manually. The statistical algorithms, including logistic regression and linear regression, successfully and aptly detect images with minimum human involvement.
So, what is essential for us is the ability of computer vision technology to solve human problems. Let’s examine the fields which were highly affected by the AI revolution:
Even a decade ago, the idea that cars can be sufficient vehicles without a driver seemed to be frantic. And when they were depicted in the movie “Back to the Future” of the 1980s, we considered them to be pure and simple fiction. But time has changed, and self-driving cars are a part of modern reality (even though they are still on the stage of development). And how does computer vision contribute to their development? Let us explain.
Self-driving cars have cameras that capture videos from a variety of angles, and the computer vision software receives such images and processes them to detect other vehicles, pedestrians, objects on the roads, traffic lights, and the extremities of ways.
Such widely used phenomenal technology in China as facial recognition also has its roots in computer vision. The essence of this technology is to recognize people’s faces and match them with their personal information in the database.
Facial recognition systems are supported by face-profiles databases, which help it to learn specific algorithms to detect facial features. Computer vision technology is not only Chinese thing anymore. Many consumer devices, social media apps, and law enforcement agencies in various countries rely on facial recognition to optimize processes and solve real-life problems.
AR and Mixed Reality
Computer vision makes augmented and mixed reality the way they are. Smartphones, tablets, and headsets can “read” the surrounding world and overlay virtual objects on it with the help of computer vision technology. As a result, a device can identify a human face and put a face of, let us say, Sylvester Stallone on it. That approach is what exactly Deepfake adopts for entertaining its users. Besides, computer vision helps detect where a floor or a wall is and placing virtual objects on it.
Implementation of computer vision in the healthtech is going to become that long-waited revolution. Even though the technology can aptly detect objects on the images, it is good at identifying cancerous moles in skin and deviations on X-rays and MRI scans. Thus, computer vision algorithms save precious time for healthcare providers. These systems perform the vital pre-checkup function and allow medical workers to prioritize their tasks more efficiently.
Jump Start Your Computer Vision Project with LITSLINK!
The world is continually changing, and shying away from these changes means missing many opportunities. Computer vision in such a world is a harbinger of change and an ambassador of the opportunities. It’s a vehicle that drives us directly to outstanding and world-changing discoveries. It does not only facilitate humans’ lives but can save the lives of thousands of them.
Now, it’s your turn to make a positive input on the world with a meaningful computer vision project.
As a top computer vision software company, LITSLINK will guide you through this intricate process and provide a skilled team of developers and designers to get an awesome product.