The theory and practice of Bayesian image labeling

摘要

Image analysis that produces an image-like array of symbolic or numerical elements (such as edge finding or depth map reconstruction) can be formulated as a labeling problem in which each element is to be assigned a label from a discrete or continuous label set. This formulation lends itself to algorithms, based on Bayesian probability theory, that support the combination of disparate sources of information, including prior knowledge.