On parameter estimation in deformable models

Deformable templates have been intensively studied in image analysis through the last decade, but despite its significance the estimation of model parameters has received little attention. We present a method for supervised and unsupervised model parameter estimation using a general Bayesian formulation of deformable templates. In the supervised estimation the parameters are estimated using a likelihood and a least squares criterion given a training set. For most deformable template models the supervised estimation provides the opportunity for simulation of the prior model. The unsupervised method is based on a modified version of the EM algorithm. Experimental results for a deformable template used for textile inspection are presented