@article{Ackerman_Itti05tro,
  author = {C. Ackerman and L. Itti},
  title = {Robot Steering With Spectral Image Information},
  journal = {IEEE Transactions on Robotics},
  year = {2005},
  month = {Apr},
  volume = {21},
  number = {2},
  pages = {247-251},
  abstract = {We introduce a method for rapidly classifying visual scenes, globally along a small number of navigationally relevant dimensions: depth of scene, presence of obstacles, path vs. non-path, and orientation of path. We show that the algorithm reliably classifies scenes in terms of these high-level features, based on global or coarsely localized spectral analysis analogous to early-stage biological vision. We use this analysis to implement a real-time visual navigational system on a mobile robot, trained online by a human operator. We demonstrate successful training and subsequent autonomous path following for two different out-door environments, a running track and a concrete trail. Our success with this technique suggests a general applicability to autonomous robot navigation in a variety of environments.},
  keywords = {autonomous robot ; Fourier transform ; vision ; path following ; navigation ; gist of a scene},
  type = {bb ; cv ; sc},
  file = {http://ilab.usc.edu/publications/doc/Ackerman_Itti05tro.pdf},
  if = {2003 impact factor: 2.103},
}