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meaningful information from those images, videos, and other visual inputs. Computer vision is
important and linked to AI as AI enabled systems must interpret what it sees just like human
vision and act accordingly.
The goal of computer vision is to train machines to see, process, and provide useful results
based on its observations within a very short time. This can only be possible if lots of data is
provided to it which can be analysed over and over until it detects distinctions and ultimately
recognises images. For example, to train a machine to recognise tyres, it needs to study a
lot of images of tyres. and items related to it to learn to differentiate and recognise a tyre
without fail.
Human Vision System
bowl, oranges,
bananas, lemons,
peaches
Eye Brain
(sensing device responsible for capturing interpreting device responsible for
images of the environment) understanding the image content
Computer Vision System
bowl, oranges,
bananas, lemons,
peaches
Input Sensing device Interpreting device Output
How does computer vision work?
The two technologies required to accomplish this are:
Machine Learning (Deep Learning)
Convolutional Neural Network (CNN)
Machine learning enables a computer to understand the context of visual data by using algorithmic
models. To achieve this, enough data needs to be fed through the model so that the computer can
teach itself to recognise images.
Convolutional Neural Network (CNN) helps a machine learning model to process the image by
breaking it down into pixels. It then transforms it into digital data by applying algorithms before
comparing the captured images with those stored in the database. These systems are used to
identify an individual based on their facial features like spacing of eyes, ears, chin, etc.
Artificial Intelligence and its Domains 143

