For decades, algorithms engineers have been trying to make computers “see” as well as we do. That’s no small feat: though today’s smartphone cameras provide about the same high-resolution image sensing ability as the human eye—seven megapixels or so—the computer that processes that data is nowhere near a match for the human brain. Consider that roughly half the neurons in the human cortex are devoted to visual processing, and it’s no surprise it’s a pretty hard task for a computer too.
Algorithms engineers have been trying to make computer vision perform as well as our brain for decades, developing increasingly sophisticated algorithms to help machine vision inch its way forward. This process was primarily one of trial and error. In order to make a computer understand a picture, algorithms engineers tried to figure out what kinds of features to look for in the images. Should it look for colors, edges, points, gradients, histograms, or even complex combinations of those? These detected features were then fed into classical machine learning algorithms such as SVM, Adaboost, and random forests to train them. The results were pretty good — but not really good enough.
Then, in 2012, three developments came together to turbocharge computer vision progress.
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