@inproceedings{1947,
  title = {Linear Combinations of Optic Flow Vectors for Estimating Self-Motion: a Real-World Test of a Neural Model},
  journal = {Advances in Neural Information Processing Systems},
  booktitle = {Advances in Neural Information Processing Systems 15},
  abstract = {The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. In this study, we examine whether a simplified linear model of these neurons can be used to estimate self-motion from the optic flow. We present a theory for
  the construction of an estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge both about the distance distribution of the environment, and about the noise and self-motion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirectional vision sensor. The experiments show
  that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimates turn out to be less reliable.},
  pages = {1319-1326},
  editors = {Becker, S., S. Thrun and K. Obermayer},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = oct,
  year = {2003},
  slug = {1947},
  author = {Franz, MO. and Chahl, JS.},
  month_numeric = {10}
}