2D vector-cyclic deformable templates

In this paper the theory of deformable templates is a vector cycle in 2D is described. The deformable template model originated in (Grenander, 1983) and was further investigated in (Grenander et al., 1991). A template vector distribution is induced by parameter distribution from transformation matrices applied to the vector cycle. An approximation in the parameter distribution is introduced. The main advantage by using the deformable template model is the ability to simulate a wide range of objects trained by e.g. their biological variations, and thereby improve restoration, segmentation and classification tasks. For the segmentation the Metropolis algorithm and simulated nnealing are used in a Bayesian scheme to obtain a maximum a posteriori estimator. Different energy functions are introduced and applied to different tasks in a case study. The energy functions are local mean, local gradient and probabillity measurement. The case study concerns estimation of meat percent in pork carcasses. Given two cross-sectional images - one at the front and one near the ham of the carcass - the areas of lean and fat and a muscle in the lean area are measured automatically by the deformable templates.