@press{Diop02,
  author = {J. C. Diop},
  title = {Upstream: Robotic Vision, Neuroscience-based strategies give robots a new outlook},
  journal = {Technology Review},
  pages = {33},
  month = {Oct},
  year = {2002},
  volume = {105},
  number = {8},
  keywords = {Visual perception ; research ; robots},
  abstract = {There are some sights and noises that people just can?t help but notice. Indeed, research in neuroscience now suggests that the recognition of salient objects is a key part of how we make sense of our environment. But building robots that can intelligently pick out items of interest using sight or sound remains a daunting challenge. So a handful of engineers are working on a new approach called selective-attention modeling, which attempts to program robots to evaluate scenes critically as some neuroscientists believe people do. ``General scene understanding is the Holy Grail for computer vision,'' says University of Southern California computer scientist Laurent Itti. Neuroscience-based algorithms, he contends, ``should be the new approach.''},
  type = {mod;cv;bb},
  url = {http://www.technologyreview.com/articles/upstream1002.asp},
  file = {http://iLab.usc.edu/publications/doc/Diop02.pdf}
}