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Computer Vision: How Machines Interpret the Visual World

Computer vision is the field of that enables machines to “see”.

Copyright by learn.g2.com

SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learningHumans have the gift of vision, and the organ that makes it possible is complex. Although it’s incomparable with the long-distance vision of eagles or the eyes of a bluebottle butterfly, which can see in the UV spectrum, it still does an excellent job.

A part of seeing is understanding what you’re seeing. Otherwise, it’s just receiving the light being reflected from objects in front of you. This is what happens if you have a pair of eyes but not the visual cortex inside the occipital lobe (the part of the brain responsible for visual processing).

For computers, cameras are their eyes. And computer vision acts as the occipital lobe and processes the thousands of pixels on images. In short, computer vision enables machines to comprehend what they’re seeing.

Computer vision is critical for several technological innovations, including self-driving cars, facial recognition, and augmented reality. The increasing amount of image data we generate is one reason why this field of  is growing exponentially. This increase also makes it easier for data scientists to train algorithms.

Simply put, the two main tasks of computer vision are identifying the objects of an image and understanding what they mean as a whole.

Humans take virtual perception, a product of millions of years of evolution, for granted. A 5-year-old could easily name the items placed on a table and comprehend that the entire setup is a dining table. For machines, it’s a Herculean task, and this is what computer vision is trying to solve.

Artificial general intelligence, if ever possible, wouldn’t be feasible without computer vision. That’s because accurately identifying and reacting to objects around us is one of the notable traits of our intelligence. In other words, to teach machines to think, you must give them the ability to see.

Along with the exponential growth in the number of digital photographs and videos available, advancements in and artificial neural networks also contribute to the current glory of computer vision. […]

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