Wednesday 12 May 2021

Computer Vision: What is Computer Vision & AI Technologies

 Computer Vision

If you have watched an image trying to load on a social media app, you will usually see a description in its place. It will be a descriptor of things in the image such as a field of flowers or a child holding a balloon, and so on. This is computer vision. Facial recognition that is again the rage on social media apps and other software is another aspect of computer vision. Read on to find out more about this fascinating technology.

Computer vision

Computer vision or computer vision AI is a field of artificial intelligence where computers are trained to identify, interpret, and process images in videos and images in a similar way as humans do. Significant leaps in artificial intelligence, especially in the aspects of neural networks and deep learning have enabled computer vision AI to take great strides in recent years.

Computer vision uses

Computer vision AI is used by multiple industries ranging from retail to the military to healthcare and more. Here is a quick overview of how computer vision AI is used by different industries today:

  1. Retail:
    Retailers are using computer vision in a number of ways. They use it to increase loss prevention, uncover out-of-stock shelves, enhance shopping experiences, using self-checkout counters, and so on.

  2. Manufacturing:
    Here computer vision is being used to detect and identify manufacturing defects in real-time. During the assembly line process, the computer vision AI can detect even the smallest of defects on a product.

  3. Healthcare:
    Computer vision AI is being used to screen and thoroughly examine images from X-rays, CAT scans, and MRIs with a high rate of accuracy.

  4. Insurance:
    In the insurance industry, computer vision AI is being used to reduce fraud by enabling accurate and consistent assessments of vehicular damage.

  5. Defence and security:
    Computer vision is used extensively in the defence and security sectors. For example, casinos and banks use computer vision AI to analyse images from their security cameras. It is also used extensively to improve surveillance of power plants, embassies, hospitals, stadiums, railroads, and so on. It is also used to enhance the inspection of cargo at ports. Computer vision AI is also being used in reconnaissance missions by the military, as well as to quickly identify enemies, and to automate machine and vehicular movements.

  6. Agriculture:
    Computer vision has a tremendous impact on the agricultural industry. It allows farmers to detect diseases and pests quickly and on time. Early diagnosis prevents unnecessary loss and ensures product quality. Computer vision-enhanced robots monitor farms for weeds and spray herbicides to prevent or stop weeds from taking over. Furthermore, computer vision AI is used to help in the sorting of vegetables, fruits, and flowers by weight, size, quality, and other identifying markers.

  7. Autonomous vehicles:
    Computer vision AI
    plays an important role in self-driving vehicles. Here it plays a major role in perceiving and identifying the environment around the vehicle, thus allowing for navigation.

  8. Augmented reality:
    Here it detects objects in real-time and uses the information it has processed to place virtual objects within the same space.

  9. Facial recognition:
    This is where computer vision AI matches the images of people to their identities. This technology is not just restricted to security and defence sectors but is also used by social media apps and biometric authentication on smartphones, and so on.

Aspects of computer vision AI

There are four main aspects of computer vision AI also called the four eyes of computer vision. These are object recognition, video recognition, image recognition, and machine vision.

        Image recognition:
This is where computer vision is used to identify people, places, actions, and objects in an image.

        Object recognition:
This has a similar process to image recognition but it also allots a class label to which the image belongs. For example, in self-driving cars, computer vision recognises distinctions in traffic lights, and can also differentiate between a pedestrian and a lamppost.

        Video recognition:
This is where computer vision AI analyses video clips and compares those clips to a database of other content to ascertain if there is a match.

        Machine vision:
This is a hybrid of hardware and software. The technology here can conduct inspections and provide guidance to robots via visual feedback.

How does it work?

Here is a basic outline of how a computer vision model is built:

  1. A dataset is created incorporating annotated images. The annotation can be comprised of the image category, pixel-wise segmentation of the objects present, or pairs of classes and bounding boxes.
  2. From each image, features that are relevant to the task are extracted. For example, in images, for a task to recognise people, the computer vision AI will recognise features based on facial features.
  3. Then a deep learning model is trained based on the features that are isolated in the dataset. This means that the AI is fed many more images to learn how to solve the required task.
  4. Thereafter, the computer vision model is evaluated by using images that were not used during the training phase.

Limitations of computer vision

        The need for specialists: Companies need a team of highly skilled specialists that can build and use computer vision AI.

        The need for regular monitoring: To prevent technical glitches, companies need a dedicated team to constantly monitor and evaluate the system

        No replacement for human intelligence and vision: Computer vision AI has come a long way but it is not a replacement for human intelligence and human vision. Machines do not understand the complexities and intricacies of the world around us the way humans can.

The bottom line

Computer vision AI is an exciting technology that has taken the field of artificial intelligence to new heights. The world will surely see much more advancements and innovations in this field in the future and it will continue to revolutionise our world as we know it.



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