Identification of wood rings from sparse tomographic data

This paper concerns estimation of the thickness of year rings by tomographic inspection of wood. Motivated by applications in the woodworking industry, we consider a setting where few tomographic data are available and classical reconstruction methods are not practicable. We introduce a stochastic model of the density of the wood in a log section. Based on this model, we derive a maximum likelihood and a nonlinear least squares estimator of the average ring thickness. Numerical simulations are reported showing the goodness of the model and the effectiveness of the estimators