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Computers Will Never See Like Humans :Report
January 26, 2008 at 12:10 PM
News on IT in Healthcare
Any such results may be misleading, they say, because the tests being used are inadvertently stacked in favour of computers.
Recent computational models show 60 per cent success rates in classifying natural photographic image sets. These include the widely used Caltech101 database, which is intended to test computer vision algorithms against the variety of images seen in the real world.
However, MIT experts argue that these image sets have design flaws that enable computers to succeed where they would fail with more authentically varied images.
While photographers tend to centre objects in a frame and to prefer certain views and contexts, the visual system, by contrast, encounters objects in a much broader range of conditions, the experts add.
“The ease with which we recognize visual objects belies the computational difficulty of this feat,” says James DiCarlo, a neuroscientist in the McGovern Institute for Brain Research at MIT.
“The core challenge is image variation. Any given object can cast innumerable images onto the retina depending on its position, distance, orientation, lighting and background,” adds DiCarlo, who is also a senior author of the study posted online in PLoS Computational Biology.
The flaws were exposed in current tests of computer object recognition, during which a simple “toy” computer model inspired by the earliest steps in the brain's visual pathway was used.
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