Non-Gibbsian Markov Random Field for modelling spatial relationships in structured scenes.
Daniel Heesch, Maria Petrou
We propose a non-Gibbsian Markov random field to model the
spatial and topological relationships between objects in structured scenes. The
field is formulated in terms of conditional probabilities learned from a set of
training images. A locally consistent labelling of new scenes is achieved by
relaxing the Markov random field directly using these conditional probabilities.
We evaluate our model on a varied collection of several hundred hand-segmented
images of buildings.
||D Heesch and M Petrou.
Non-Gibbsian Markov random field models for contextual labelling of
structured scenes, British Machine Vision Conference 2007