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How to use CV (computer vision) techniques that can change how you see the world

Introducing Computer Vision

If you are looking for a way to improve your resume, this is not the right article. If you are curious about computer vision, read on!

Computer Vision, aka CV, is the field of study that pursues the enterprise of teaching computers how to see and extract information from digital visual content (photos, videos, etc.).

A little background

The history of out topic starts with first experiments in the 1950s, when some early models of neural networks were used to detect the edges of simple objects and categorize them into categories such as circles and squares.

We had to wait a few more years (1970s) before meeting the first commercial version of computer vision, with the creation of a software able to interpret typed or handwritten text using optical character recognition.

Nowadays computer vision is widely used for face detection and profile matching (Facebook), for content control (Instagram) and more. The efficiency and capacity of the technology are always improved, even thanks to the high amount of data that users upload every day in their social media platforms, which are used to train the CV systems.

More has yet to come: by 2022, the CV hardware and software market is expected to reach $48.6 billion volume, with an increased impact in people’s everyday life.

Some pretty cool examples of Computer Vision in practice

  1. Google Translate App – can now automatically detect languages so you can point your camera at a flyer or sign and get results in your native tongue even if you don’t know what language you’re reading. Check this cool music video that shows the capabilities.
  2. Facial recognition systems – are technologies capable of identifying or verifying a person from a digital image or a video frame from a video source. Check how Sighthound used this technology to create a simple and effective concierge system here.
  3. Autonomous vehicles – In the world of autonomous vehicles, computer vision is often referred to as “perception”, because cameras are the primary (but not the only) tool that vehicle uses to perceive its environment.


Cameras are key to a variety of essential tasks for autonomous vehicles: lane finding, road curvature estimation, obstacle detection and classification, traffic sign detection and classification, traffic light detection and classification, and more. Tesla, for example, equips its cars with “eight surround cameras that provide 360 degrees of visibility around the car at up to 250 meters of range.

See the streets in the eyes of Tesla Autopilot here!

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