Amazon, Google, Microsoft, Facebook and Tesla are investing billions in research and development of computer vision technology. Driverless cars, unmanned convenience stores, medical diagnostics and surveillance systems – the applications for computer vision and pattern recognition technologies are numerous. This stupendous impact is not just on customer experiences but on improving business processes as well.
First came machine vision, then came computer vision
The idea of machines being able to behave like humans has been the stuff of sci-fi for decades. The amazing part is that life has emulated art and it is now a reality that will keep evolving.
When it comes to machine vision vs computer vision, the first is a subset of the other. Machine vision refers to the industrial application of computer vision to mechanically “see” steps through image analysis in a production line and trigger an action that instructs other components in the system to respond. For instance, in the F&B sector, the bottling facility has a machine vision system that is pre-programmed to recognize if bottles are chipped, it can check if correct levels are filled and lids or caps are properly sealed. It can also check if the date stamp has been applied. These jobs would have previously required human supervision but humans are prone to error and fatigue. Machines are superhuman that way, they can keep going and accuracy remains high.
Computer vision can be used alone and does not have to be a part of a larger machine system. This is where it differs from machine vision. Computer vision has much greater processing power since it uses deep learning to identify a large number of variables, identify trends and gain advanced feedback from abstract visual data.
Neural networks for CV work like a jigsaw
You read that right! Computer vision puts together images exactly like we put together a jigsaw. It recognizes edges, identifies different pieces and then starts to bring together the pieces. While we humans have the picture on the jigsaw box to guide us on putting the puzzle together, computer vision arrives at the composite picture differently.
If we were trying to put together a picture of a do