Bayesian model for subpixel uncertainty determination in optical measurements

Abstract

Uncertainty determination can be obtained by two procedures: GUM and the Monte Carlo Method. This work presents a model that helps to evaluate the uncertainty in measurements collected by optical measuring machines when using the Monte Carlo method. Initially, the model converts intensity, using Bayesian probability, from the pixel image derived from camera into a polygonal area with three to five vertexes. The outer vertexes are fitted using least squares procedures to obtain a measurand shape approximation in a subpixel range. Algorithms have been programmed and verified into Matlab using synthetic images with different triangles. Through a detailed analysis, the usefulness of a new tool, the parameter, will be demonstrated as an alternative method for estimating uncertainty of measurements of pixel images.