Twenty leading computer vision and imaging scientists from
(NYSE:XRX) will join their peers from Google, Facebook, Microsoft Research, Amazon and many of the world’s top academic institutions later this month to share their research on making computers more “human like,” mimicking how the brain sees and thinks.
Steadily closing the gap between reality and Hollywood depictions of artificial intelligence, the annual
IEEE Computer Vision/Pattern Recognition Conference
set for June 23-28 in Columbus, Ohio, draws top scientists worldwide working on ways to advance
, a field that empowers machines to “see” and make sense of the world, augmenting and often exceeding human capabilities.
“Xerox has firsthand knowledge of
across many industries, and is a pioneer in teaching computers to extract meaningful and actionable analytics from images and video,” said Raja Bala, a Xerox principal scientist in Webster, N.Y. “Although there’s been significant progress in recent years, a number of scientific challenges remain to be resolved.”
Xerox research presented at this year’s conference includes:
Detecting cell phone use by highway drivers
Motivated by its impact on public safety and property, several state and federal government organizations prohibit cell phone use while driving. Xerox scientists are working on a camera system for highways that uses pattern recognition technology to detect if a driver is using a cell phone.
Turning smartphones into a personal driving coach
Researchers in Webster are also working on a computer vision project that would turn smartphones into driving assistants. Using facial feature detection technology the phone would estimate a driver’s gaze direction, and detect if a driver is distracted and not paying attention to the road.
Making images more eye catching
Researchers from both Xerox in Europe and at Harvard University are studying what attracts people’s attention first when they look at a picture. Understanding that eye-catching element enables visuals to be composed for greater effect and can predict where people will look when facing a scene, a photo or game.