If we’ve learned anything from post-apocalyptic movies it’s that computers eventually become self-aware and try to eliminate humans.
BYU engineer Dah-Jye Lee isn’t interested in that development, but he has managed to eliminate the need for humans in the field of object recognition. Lee has created an algorithm that can accurately identify objects in images or video sequences without human calibration.
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.
Lee and his students fed their object recognition program four image datasets from CalTech (motorbikes, faces, airplanes and cars) and found 100 percent accurate recognition on every dataset. The other published well-performing object recognition systems scored in the 95-98% range.
The team has also tested their algorithm on a dataset of fish images from BYU’s biology department that included photos of four species: Yellowstone cutthroat, cottid, speckled dace and whitefish. The algorithm was able to distinguish between the species with 99.4% accuracy.
Lee believes that it could spot invasive species and manufacturing defects without requiring constant human oversight.
Let's just hope it doesn't decide that we're the invasive species.
BYU engineer Dah-Jye Lee isn’t interested in that development, but he has managed to eliminate the need for humans in the field of object recognition. Lee has created an algorithm that can accurately identify objects in images or video sequences without human calibration.
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.
Lee and his students fed their object recognition program four image datasets from CalTech (motorbikes, faces, airplanes and cars) and found 100 percent accurate recognition on every dataset. The other published well-performing object recognition systems scored in the 95-98% range.
The team has also tested their algorithm on a dataset of fish images from BYU’s biology department that included photos of four species: Yellowstone cutthroat, cottid, speckled dace and whitefish. The algorithm was able to distinguish between the species with 99.4% accuracy.
Lee believes that it could spot invasive species and manufacturing defects without requiring constant human oversight.
Let's just hope it doesn't decide that we're the invasive species.
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