Computer vision is an artificial intelligence field that teaches and enables computers to comprehend the visual environment. Computers can effectively recognise and classify items and react to them using digital photos and deep learning algorithms.
The amount of data we generate now is enormous: 2.5 quintillion bytes of data are generated every day. One of the driving forces behind the development of computer vision has been the increase of data.
In the actual world, computer vision is analogous to putting together a jigsaw puzzle. Imagine you have all of these jigsaw pieces and you need to put them together to make a genuine image. That is exactly how computer vision’s neural networks operate. Computers can put all the pieces of an image together through a sequence of filtering and activities and then think for themselves. The computer, on the other hand, is not only given a puzzle of an image; it is frequently fed hundreds of photographs that educate it to detect specific items.
Instead of teaching a computer to recognise a cat’s pointy ears, long tails, paws, and whiskers, software engineers upload and feed the computer millions of photographs of cats. This allows the computer to recognise and understand the various aspects that make up a cat.
Applications of Computer Vision
Computer vision is employed in a lot more places than you might think. Computer vision has entered our lives in a variety of ways, from identifying early signs of cancer to enabling computerised checkouts in retail stores. Here are some more applications for computer vision:
- Face recognition – Face-detection algorithms are used by Snapchat, Instagram, Facebook, and many other social media apps to recognise you in photos and add filters to your face.
- Surveillance – Surveillance or video analytics cameras employ computer vision to detect suspicious behaviour in both public and private spaces.
- Image retrieval – When you upload an image, Google Images will help you discover relevant images. Different algorithms assess the content of the image that has been submitted and return results based on the best-matched content.
- Biometrics – Computer vision is used in biometric identification technologies such as fingerprint and iris recognition.
- Smart Cars – When smart cars are in auto mode, computer vision is used to recognise traffic signs, lights, and other visual characteristics.
Algorithms for Computer Vision
The various approaches used to understand the objects in digital photographs and extract high-dimensional data from the real world to produce numerical or symbolic information are referred to as computer vision algorithms. There are a slew of different computer vision algorithms at work when it comes to detecting objects in photos. Here are a few examples:
- Object Classification – What is the primary classification of the object in this photograph?
- Identifying the Thing – What is the type of object in this photograph?
- Object Detection – What is the location of the object in the photo?
- Object Segmentation – Which pixels in the image belong to the object?
- Verification of the Object – Is the object in the photograph?
- Object Recognition – What are the objects in this shot and where are they?
- Object Landmark Detection – What are the most important features of the object in this image?
Style transfer, colorization, human position estimation, action identification, and other advanced computer vision techniques can be learnt alongside deep learning methods.
Advantages of Computer Vision
Several tasks can be automated using computer vision without the need for human intervention. As a result, it gives a number of advantages to businesses:
- Faster and easier procedure – Computer vision systems can perform repetitive and monotonous jobs more quickly, making human work easier.
- Products and services that are better – Computer vision systems that have been properly taught will make no errors. As a result, high-quality products and services will be delivered more quickly.
- Cost-cutting – Businesses will save money by not having to spend money on correcting their broken processes since computer vision will eliminate the need for faulty goods and services.
Disadvantages of Computer Vision
There is no technology that is without defects, and computer vision systems are no exception. Here are some of computer vision’s drawbacks:
- Lack of specialists – To train computer vision systems, companies must have a team of highly qualified professionals who understand the differences between AI, Machine Learning, and Deep Learning technologies. More professionals are needed to help shape the technologies of the future.
- Regular monitoring is required because if a computer vision system experiences a technical problem or fails, it can result in significant financial loss for businesses. As a result, businesses must have a dedicated team to monitor and analyse these systems.
Despite recent advances in this emerging sector of technology, we are still far from realising the full potential of computer vision. However, a number of businesses have already discovered methods to use this technology to solve real-world challenges. This simply goes to show that computer vision is rapidly evolving and is unlikely to slow down very soon. If you’re interested in learning further about computer vision and some other deep learning benefits, Certification Training is a great place to start. You’ll learn how to use frameworks to grasp all of the deep learning ideas and models, as well as how to create deep learning algorithms. Begin your course right away to set yourself up for a successful career in deep learning.