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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.
Examples of AI applications based on computer vision:
Face recognition systems used at workplace and schools to mark attendance.
Biometric software is used by law enforcement agencies to scan faces in CCTV footages.
Image enabled searches are initiated at online shopping websites using your camera.
Selfie-based authentication is initiated for banking and payments app using face recognition
technology.
NATURAL LANGUAGE PROCESSING
Natural Language Processing (NLP) is the ability of an AI system to understand human language
as it is spoken. Computer cannot understand the language we speak. Hence, we need software
and programming languages to communicate with the computer.
Artificial Intelligence and Its Domains 101

