Automatic finding of main roads in aerial images by usinggeometric-stochastic models and estimation
Barzohar, M.
Cooper, D.B.
Div. of Eng., Brown Univ., Providence, RI;
Abstract
An automated approach to finding main roads in aerial images is
presented. The approach is to build geometric-probabilistic models for
road image generation. Gibbs distributions are used. Then, given an
image, roads are found by MAP (maximum aposteriori probability)
estimation. The MAP estimation is handled by partitioning an image into
windows, realizing the estimation in each window through the use of
dynamic programming, and then, starting with the windows containing high
confidence estimates, using dynamic programming again to obtain optimal
global estimates of the roads present. The approach is model-based from
the outset. It produces two boundaries for each road, or four boundaries
when a midroad barrier is present
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