COMPUTER VISION:Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they “see.”


Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do.

Computer vision (CV) is the subcategory of artificial intelligence (AI) that focuses on building and using digital systems to process, analyze and interpret visual data. The goal of computer vision is to enable computing devices to correctly identify an object or person in a digital image and take appropriate action.

Computer vision uses convolutional neural networks (CNNs) to processes visual data at the pixel level and deep learning recurrent neural network (RNNs) to understand how one pixel relates to another.

Uses for computer vision include:

  • Biometric access management — CV plays an important role in both facial and iris recognition.
  • Industrial robots and self-driving cars — CV allows robots and autonomous vehicles to avoid collisions and navigate safely.
  • Digital diagnostics — CV can be used in tandem with other types of artificial intelligence programming to automate the analysis of X-rays and MRIs.
  • Augmented reality — CV allows mixed reality programming to know where a virtual object should be placed.

There are many types of computer vision that are used in different ways:

  • Image segmentation partitions an image into multiple regions or pieces to be examined separately.
  • Object detection identifies a specific object in an image. Advanced object detection recognizes many objects in a single image: a football field, an offensive player, a defensive player, a ball and so on. These models use an X,Y coordinate to create a bounding box and identify everything inside the box.
  • Facial recognition is an advanced type of object detection that not only recognizes a human face in an image, but identifies a specific individual.
  • Edge detection is a technique used to identify the outside edge of an object or landscape to better identify what is in the image.
  • Pattern detection is a process of recognizing repeated shapes, colors and other visual indicators in images.
  • Image classification groups images into different categories.
  • Feature matching is a type of pattern detection that matches similarities in images to help classify them.

Computer vision is similar to solving a jigsaw puzzle in the real world. Imagine that you have all these jigsaw pieces together and you need to assemble them in order to form a real image. That is exactly how the neural networks inside a computer vision work. Through a series of filtering and actions, computers can put all the parts of the image together and then think on their own. However, the computer is not just given a puzzle of an image – rather, it is often fed with thousands of images that train it to recognize certain objects.

For example, instead of training a computer to look for pointy ears, long tails, paws and whiskers that make up a cat, software programmers upload and feed millions of images of cats to the computer. This enables the computer to understand the different features that make up a cat and recognize it instantly.

Computer Vision advantages

Computer vision can automate several tasks without the need for human intervention. As a result, it provides organizations with a number of benefits:

  • Faster and simpler process – Cv systems can carry out repetitive and monotonous tasks at a faster rate, which simplifies the work for humans.
  • Better products and services – CV systems that have been trained very well will commit zero mistakes. This will result in faster delivery of high-quality products and services.
  • Cost-reduction – Companies do not have to spend money on fixing their flawed processes because computer vision will leave no room for faulty products and services.

CV Disadvantages

There is no technology that is free from flaws, which is true for cV systems. Here are a few limitations of computer vision:

  • Lack of specialists – Companies need to have a team of highly trained professionals with deep knowledge of the differences between AI vs. Machine Learning vs. Deep Learningtechnologies to train computer vision systems. There is a need for more specialists that can help shape this future of technology.
  • Need for regular monitoring – If a computer vision system faces a technical glitch or breaks down, this can cause immense loss to companies. Hence, companies need to have a dedicated team on board to monitor and evaluate these systems.