How Does Computer Vision Work?

While there are infinite amounts of visual information around us, our brain isn’t nearly capable of comprehending or processing all of it. Most of us never stop to consider how much visual information we automatically perceive and interpret every day. The human brain was built for visual data as it has millions of neurons devoted explicitly to our visual processes. They take up to 30% of the brain’s entire cortex. Until recently, machines couldn’t comprehend the optical parts of the world as efficiently as we do. 

With the adoption of Artificial Intelligence (AI), computers got the ability to understand the visual world. Thanks to deep learning models and digital images from videos and cameras, machines can identify and categorize objects. 

It took scientists more than half a century to teach machines to “see”. The first experiments can be traced back to the 1950s. AI was also used to interpret typed and handwritten texts. Back then, computer vision was quite limited and required lots of human input. Let’s find out what computer vision is and how it works today. 

What Is Computer Vision?

Computer vision is an interdisciplinary field that deals with digital systems capable of perceiving, processing, and identifying visual data just like humans do. Thanks to AI, machines can be taught to understand the visual world. From an engineering point of view, machines process images and videos at a pixel level and extract a high-level understanding of them with the help of special software algorithms.  

Top Three Tasks Computer Vision Can Perform:

  • Object classification. The machine parses through thousands of images or videos and classifies objects found according to the defined category. For example, the system can find a cat among every object found in the picture. 

Source: cs231n.github.io

  • Object identification. The machine can analyze visual content and identify defined objects. For example, it can find a specific cat in the pictures below. 

Source: cs231n.stanford.edu

  • Object tracking. The machine can analyze video and keep track of the objects that match the defined criteria. For example, the system can find the cat with a particular plate number and track its movements. 

Computer vision can solve a variety of business problems, depending on the goals and needs. Contact us to find out how your business can benefit from computer vision technologies. 

How Does Computer Vision Work?

Machines can mimic the way people see and perceive visual content. But the key difference is that computers can only see the digital representation of an image. Humans can see far beyond visual content. We can understand the semantic meaning of an image. Machines can only detect pixels. 

The semantic gap is one of the things challenging today’s researchers and software engineers. Machines cannot completely mimic the thinking process. Building an AI-powered solution as supreme as the human brain took years of research. 

Today, computer vision systems are based on pattern recognition. Machines are trained using massive amounts of visual information. They process images, find objects, and identify patterns. 

From a technical point of view, machines perceive images as a set of pixels, each having its own set of colors. Each pixel has its own brightness represented by an 8-bit number. The numbers range from black (0) to white (255). Computer vision algorithms convert images and videos into 8-bit pictures for further processing and decision making.

Source: https://openframeworks.cc/ofBook/chapters/image_processing_computer_vision.html

Computer vision relies on a sophisticated deep learning algorithm. It’s a subset of Machine Learning that, in turn, relies on artificial intelligence. Deep learning takes advantage of a specific AI algorithm called a neural network. It mimics the brain functions, and in particular, the interrelationship of neurons in the cerebral cortex. 

The core of a neural network forms a perceptron, a mathematically formulated neuron. Like biological neurons, perceptrons can also form several interconnected layers where information is actually processed and classified. 

Source: https://www.quora.com/What-is-the-difference-between-deep-learning-and-usual-machine-learning

How We Use Computer Vision

Computer vision technologies have become one of the most sought-after tech advancements in recent years. These solutions can augment a vast array of tasks and revolutionize traditional ways of solving certain business challenges. 

Health

Computer vision algorithms are widely across the healthcare industry. Among the main tasks of computer vision technologies in healthcare is the analysis of MRI/ CT scans and ultrasonic images. 

Though these technologies can’t yet replace medical supervisors, they can simplify the process and minimize the risks of a false diagnosis. When the COVID pandemic struck, computer vision systems were taught to find and identify any suspicious areas of the lungs on CT scans. While saving lives, the following technologies helped physicians and scientists study the unknown disease and its course. 

Facial recognition

Facial recognition is integrated into an array of products that we use every day, from smartphones to social media. Facebook was one of the first social media platforms to take advantage of facial recognition to match people with photos.

It’s a crucial technology when it comes to biometric authentication. Most handheld devices allow users to unlock their devices with their faces as a standard feature. It adds an extra layer of security as well as streamlines the unlocking process. 

Self-driving cars

Thanks to computer vision, cars can self navigate in space and sense their surroundings. Autonomous cars come loaded with a complex AI-based algorithm that controls the environment at all times. 

These cars come equipped with several cameras that constantly capture video from different perspectives and send it to the computer vision machine. The machine processes the video in real-time to adapt to the constantly changing environment. But the problem is that there can be hundreds of situations that the machine wasn’t trained for. The following algorithms still need more training data to prevent every possible traffic situation. It’s one of the reasons why cars still need human drivers. 

Industries that can still benefit from computer-vision technologies include agriculture and retail. Farmers can increase the efficiency of their harvesting while computer vision technologies check their soil for pests and plants for diseases. With computer vision technologies, retailers can study customer behavior, track their attention, and optimize the customer journey. 

Summing Up 

Computer vision is a booming field in software development. This tech breakthrough continues to influence every industry, from car manufacturing to farming. It can be a real challenge for businesses to choose the right set of emerging technologies. At Unicsoft, we know how to apply technologies to your business needs.