Technology

What is Computer Vision and How Does it Work?

Computer vision is an area of artificial intelligence (AI) and machine learning (ML), enabling systems and computers to acquire significant information from digital images, visual inputs, and videos. It takes action or makes recommendations accordingly based on the visual information. If artificial intelligence solutions make computers think, then computer vision makes them see, observe and understand.

Computer vision works much similarly to human vision, except it performs all the functions in less time and with more accuracy.

Here are a few of the tasks computer vision systems are used for: 

Object Tracking: The computer vision enables the system to process video and find an object or object matching with search criteria and track its movement. 

Object Classification: The computer recognizes visual content and classifies the object on a picture or video to the defined category. For instance, the system can help find a dog among other objects in the image. 

Object Identification: The system identifies a particular object on a picture or video by analyzing the visuals. For instance, the system can find a specific breed of cat among other cats in the image. 

How Does Computer Vision Work? 

Computer vision is one of the machine learning solutions that need a lot of data. It analysis the data over and over to recognize distinctions and images. For instance, in order to train a computer to recognize automobile tires, a vast quantity of tires images and tire-related items needs to be fed to the system to learn the differences and recognize a tire, particularly the one with no defects.

To accomplish this, two technologies are used. One is a type of machine learning called deep learning, and the other one is a convolutional neural network (CNN). 

Machine learning allows the system to learn itself in the context of visual data using algorithmic models. If sufficient data is fed through the model, the system scans the data and learns to distinguish one image from the other. Machines learn on their own using algorithms instead of someone programming them to recognize an image. 

CNN helps machine learning models look at objects by breaking an image into pixels. These pixels are then given tags or labels. It uses the labels for making predictions regarding what it is seeing. Just like a human eye makes out an image at a distance, a CNN first recognizes simple shapes and hard edges, then fills in the data as it runs iterations of its predictions. 

Applications of Computer Vision In Different Areas 

Some people believe that computer vision is one of the artificial intelligence solutions from the distant future of design. That’s not true. This technology has already been integrated into many areas of our lives, and here are some of the examples: 

  1. Facial Recognition 

Facial recognition technology is used to match pictures of people’s faces with their identities. It is integrated into some major products we use in our daily life, including smartphones, Facebook, and other biometric authentication devices. 

  1. Self-Driving Cars 

Computer vision enables cars to observe and identify their surroundings. A smart car has cameras at different angles to captures videos. These videos are sent to computer vision software. It processes the video in real-time and detects surrounding objects like cars nearby, traffic lights, and road marking. Autopilot in Tesla cars is one of the examples of this technology.

  1. Content Organization 

Computer vision technology, through its machine learning solutions, is already helping us organize our content. Your smartphone pictures are an excellent example. The system automatically adds tags to pictures and allows the users to browse a more structured collection of photos. 

  1. Augmented Reality 

Computer vision is a crucial element of AR apps. It helps AR apps detect objects in real-time and use the information to place virtual objects to get an idea within the physical environment.

  1. Health

Image information is a vital element for the diagnosis of different diseases. Most of the healthcare diagnoses are based on image processing, including MRI, X-rays, CT scans, Mammography, etc. 

Conclusion 

Computer vision will be a vital element in developing artificial superintelligence as well as artificial general intelligence. In the future, it will process information as well as a human does, or maybe even better. If you want to leverage the power of this technology, there are many vendors that can help you out. One such organization is Xavor Corporation. Xavor has been delivering quality artificial intelligence solutions and machine learning solutions to its customers for the past 25 years. Through proprietary algorithms, Xavor is helping businesses process digitally and be at the frontier of the new industrial realm.

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