Computer vision is a technology that aims to help computers see and understand visual content. This seems like a simple task given that humans are naturally capable of it. But is this really the case?
Pursuing the idea that artificial intelligence tends to imitate the functioning of a human brain, computer vision allows machines to acquire some of their skills. Computer vision combines artificial intelligence with cameras to enable the interpretation of visual data automatically, without the help of a human.
What is computer vision?
Simply, computer vision is a field of artificial intelligence that gives computers the ability to extract meaningful information from digital data. The data in question may correspond to images, videos, or other types of visual input. This information will allow computers to make decisions or recommend solutions to a given problem.
Computer vision, which is also a field of machine learning, studies the technologies and tools that allow computers to perceive and interpret real-world information. AI gives machines a cognitive ability, while cameras allow them to see. Together they form the computer vision that allows observation and understanding.
The principles of computer vision
Unlike computers, humans can naturally distinguish objects, estimate their distance, and understand their motion. A machine, on the other hand, has to train to perform these functions and it takes longer. Instead of eyes, it uses data and algorithms. But once trained enough, the computer can easily perform tasks like inspecting products, tracking production assets, different analyses, etc. It is even possible that a machine trained in computer vision will eventually become faster than humans.
So the first part of the process is to give the “sight” to a computer. To do this, simply connect a camera to it. Then, the computer will have to classify and interpret the collected data and conclude their relation and their context. Its tasks, therefore, boil down to explaining what it sees in real-time.
The origin of computer vision
The idea of developing ways for computers to see and understand visual data dates back some sixty years. Over the years, the uses and technologies used have changed the notions of computer vision.
In 1959, neurophysiologists established that image processing begins above all with the identification of simple and basic forms. Much later, neuroscientist David Marr would establish that vision works in a hierarchical fashion. Algorithms will be introduced to detect the different base shapes. At the same time, computer scientist Kunihiko Fukushima will develop a network of cells called Neocognitron, comprising convolutional neural networks (CNNs), capable of recognizing patterns.
How does computer vision work?
Like any artificial intelligence technology, the basis of computer vision is data. The system must train itself to discern and recognize images on a large amount of data.
In order to develop a computer vision system, developers must use deep learning or a convolutional neural network (CNN). Deep learning models allow a computer to automatically learn about the contexts of visual data.
As for CNN, it decomposes the pixels and assigns them tags or labels to perform convolutions and make predictions. When these come true, that's the very definition of computer vision. The processing of images by a CNN is done in a hierarchical way, like in a human brain.
Advantages of the vision computer
The vision computer can automate several tasks without requiring human intervention. Therefore, it offers organizations a number of advantages. Below are some of them
A simpler process
Now, several industries are benefiting from computer vision technology. These systems serve a multitude of purposes, ranging from predictive maintenance to quality control and on-site safety. In addition, the vision computer makes it possible to perform repetitive and monotonous tasks more quickly. Thus, the work of the employees is simplified.
Better products and services
It's no secret that machines never make mistakes. Likewise, computer vision systems with image processing capabilities are unlikely to make errors, unlike humans. Ultimately, this results in faster delivery of high-quality products and services.
Cost reduction
With machines taking responsibility for performing tedious tasks, errors will be minimized, leaving no room for faulty products or services. As a result, companies can save a lot of money that would otherwise be spent fixing faulty processes and products.
