Uncertainty characterization in image-based measurements: a preliminary discussion

There are a large number of application fields where measurements deriving from digital images can assume a great relevance. Nevertheless, to make a profitable use of such measurements, it is indispensable to achieve a complete and quantitative control on uncertainties that real systems introduce along the chain of steps going from real-world objects to the results of the measurement process. This paper deals with this nontrivial task and, in particular, with the analytical expression of uncertainty characterizing the results of image processing software. At first, a simplified model of uncertainty of digital images is derived and experimentally tested; then the Canny edge detector output uncertainty is analytically expressed and verified both in artificial and real-world images.