Computer Vision: Mechanical Eye towards a better World

Computer Vision: Mechanical Eye towards a better World

Nothing can replace nature’s way of visualizing and adapting the ecosystem around us, but what if human-assisted machine possesses capabilities of human vision i.e. recognising patterns, faces and rendering 2D imagery from a 3D world into 3D.

A human eye has between six and seven million cone cells, containing one of three colour-sensitive proteins known as opsins. When photons of light hit these opsins, they change shape, triggering a cascade that produces electrical signals, which in turn transmit the messages to the brain for interpretation.

It seems ‘Complex’ right? But, still, it is the present & future of the development of ‘Intelligent Machines’. It all started from the 1950’s – Two-dimensional imaging for statistical pattern recognition and now ‘Driverless Cars’ are revolutionising the automobile industry.

As we’re talking about ‘Driverless Cars’, let’s understand how Computer vision is aiding into this future reality. An autonomous vehicle needs a system to constantly collect data, which is predicted to be 4TB/day/car. This gigantic amount of data will be collected from Road conditions, Potential Hazards, Pedestrians, surrounding vehicles, Routes and Roads. So, computer vision provides Inertial Measurement Units, Large Dataset of Labelled Driving Data and GPS Location Data to be implemented into an AI model, which augments an autonomous vehicle to efficiently decide the extent of Steering, Acceleration and Braking.

Computer vision allows an organisation to automate contextual tagging of image and videos. Customizable tagging can include image location, probable demographic information, and facial matches. Let’s take an example of Identity Verification at an airport. The current biometric system can only work upon single data of a particular person and so on there is always a loophole for the escape of a fraud person. Computer vision provides a AI model which can train itself according to different data of different aspects of the same person and hence it can identify whether the security is seeing the same person or the different one.

Possibilities and use cases are limitless. To achieve better business decision making, governments, companies, and organisations need to turn anonymized images and videos into valuable insights and reduce the time spent analyzing a large volume of contents.

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