  We describe a general framework for probabilistic modeling of complex scenes
and inference from ambiguous observations. The approach is motivated by
applications in image analysis and is based on the use of priors defined by
stochastic grammars. We define a class of grammars that capture relationships
between the objects in a scene and provide important contextual cues for
statistical inference. The distribution over scenes defined by a probabilistic
scene grammar can be represented by a graphical model and this construction can
be used for efficient inference with loopy belief propagation.
  We show experimental results with two different applications. One application
involves the reconstruction of binary contour maps. Another application
involves detecting and localizing faces in images. In both applications the
same framework leads to robust inference algorithms that can effectively
combine local information to reason about a scene.
