Image segmentation for quantifying damage based on Bayesian updating scheme is proposed for diagnosis and prognosis
in structural health monitoring. This scheme enables taking into account the prior information of the state of the
structures, such as spatial constraints and image smoothness. Bayes' law is employed to update the segmentation with
the spatial constraint described as Markov Random Field and the current observed image acting as a likelihood function.
Segmentation results demonstrate that the proposed algorithm holds promise of searching a crack area in the SHM image
and focusing on the real damage area by eliminating the pseudo-shadow area. Thus more precise crack estimation can be
obtained than the conventional K-means segmentation by shrinking the fuzzy tails which often exist on both sides of the