Exploiting symbolic learning in visual inspection

The paper describes the use of data analysis techniques in the computer-vision inspection of industrial workpieces. Computer-vision inspection aims at accomplishing quality verification of fabricated parts by means of automated visual procedures. Gathering the visual information into models proves a critical task, especially when subjective judgement is involved in quality verification. In this work, intelligent data analysis techniques based on symbolic learning by examples have been explored in order to automatically devise and parametrize effective quantitative models. The paper reports and discusses the experimental results achieved in an industrial application.