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Stanford team developing "super 3D" 12,616 lens camera
Information Technology News
Stanford team developing "super 3D" 12,616 lens camera | Stanford team developing "super 3D" 12,616 lens camera |
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| by Stan Beer | |
| Wednesday, 19 March 2008 | |
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Page 2 of 2 According to the researchers, taking a photo of a face
will precisely record the distances to the subject's eyes, nose, ears,
chin, etc, leading to possible applications for facial recognition for
security purposes, biological imaging, 3D printing, creation of 3D
objects or people to inhabit virtual worlds, or 3D modeling of
buildings.Featured Whitepaper
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Knowing the exact distance to an object might give robots better spatial vision than humans and allow them to perform delicate tasks now beyond their abilities. "People are coming up with many things they might do with this," Fife said. The three researchers published a paper on their work in the February edition of the IEEE ISSCC Digest of Technical Papers. According to the researchers, their super 3D camera would look and feel like an ordinary camera, or even a smaller cell phone camera. The cell phone aspect is important, Fife said, given that "the majority of the cameras in the world are now on phones." The Stanford report contains more detailed applications for a camera with the ability to digitise images in a detailed depth map which can be stored on a computer for later processing and manipulation. "You can choose to do things with that image that you weren't able to do with the regular 2-D image," Fife said. "You can say, 'I want to see only the objects at this distance,' and suddenly they'll appear for you. And you can wipe away everything else." The researchers are now working out the manufacturing details of fabricating the micro-optics onto a camera chip. They claim the finished product may cost less than existing digital cameras, the researchers say, because the quality of a camera's main lens will no longer be of paramount importance. "We believe that you can reduce the complexity of the main lens by shifting the complexity to the semiconductor," Fife said. |
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