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Oct 12

Reported by Larry Hardesty, MIT News Office, 7 October 2013.

A neglected statistical tool could help robots better understand the objects in the world around them.

A statistical construct called the Bingham distribution enables a new algorithm to identify an object's orientation using far fewer data points (red and purple circles) than previous algorithms required. Images courtesy of the researchers.

Object recognition is one of the most widely studied problems in computer vision. But a robot that manipulates objects in the world needs to do more than just recognize them; it also needs to understand their orientation. Is that mug right-side up or upside-down? And which direction is its handle facing?

To improve robots’ ability to gauge object orientation, Jared Glover, a graduate student in MIT’s Department of Electrical Engineering and Computer Science, is exploiting a statistical construct called the Bingham distribution. In a paper they’re presenting in November at the International Conference on Intelligent Robots and Systems, Glover and MIT alumna Sanja Popovic ’12, MEng ’13, who is now at Google, describes a new robot-vision algorithm, based on the Bingham distribution, that is 15 percent better than its best competitor at identifying familiar objects in cluttered scenes. Continue reading »

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Aug 31

Reported by Dave Moshe,in Wired Science, 28 Aug. 2012.

Seeking a way to crowdsource better computer vision, roboticists have launched a website that allows users to record pieces of their environments in 3-D with a Kinect camera.

Called Kinect@Home, the open source and browser-based effort remains in its infancy. Users have uploaded only a few dozen models of their living room couches, kitchen countertops and themselves. Continue reading »

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