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A smart-object recognition algorithm that doesn't need humans

January 17, 2014 | Telepresence Options

byu_pattern_recognition2

Story and images by BYU / Kurzweil

BYU engineer Dah-Jye Lee has created an algorithm that can accurately identify objects in images or video sequences -- without human calibration.

"In most cases, people are in charge of deciding what features to focus on and they then write the algorithm based off that," said Lee, a professor of electrical and computer engineering. "With our algorithm, we give it a set of images and let the computer decide which features are important."

Humans need not apply

Not only is Lee's genetic algorithm able to set its own parameters, but it also doesn't need to be reset each time a new object is to be recognized --� it learns them on its own.

Lee likens the idea to teaching a child the difference between dogs and cats. Instead of trying to explain the difference, we show children images of the animals and they learn on their own to distinguish the two. Lee's object recognition does the same thing: Instead of telling the computer what to look at to distinguish between two objects, they simply feed it a set of images and it learns on its own.

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