Bayesian Image Understanding: From Images to Virtual Forests

T. Caelli, L. Cheng, and Q. Fang


Image Understanding, Bayesian method, CAD-based vision, forestry photo interpretation, stochastic L-system


In this paper we consider the full cycle of Image Understanding (IU): the generation of 3D object hypotheses (inverse model) from images and their projections back onto image data (forward model) in terms of Bayesian inference processes. Each subprocess is framed as a local optimization problem based on a component model and observations. The end result is an IU system that not only provides a symbolic description of scenes but also generates fully 3D versions of the scene being sensed, providing validation criteria for image annotation.

Important Links:

Go Back