Method and apparatus for detecting anomalies in two-dimensional digital images of products

Abstract

The invention relates to a method for detecting anomalies in digital images of products. A digital image is divided into regions, wherein a region is detected as a maximum anomaly if the value of the at least one property is greater than a predetermined maximum threshold value, or as a minimum anomaly if the value of the at least one property is less than a predetermined minimum threshold. In a test process, a plurality of digital bad images is generated from real or fictitious bad products, each which of which has at least one already known anomaly. Every bad image is divided into regions and the maximum value of the relevant property of the regions is determined as the maximum sample value of a maximum value sample or a minimum sample value of a minimum value sample. A detection rate for the at least one already known anomaly is determined from a sample generated in this way. For this purpose, according to one alternative, a suitable predetermined probability density function for describing the maximum value sample or the minimum value sample is parameterized using a statistical estimation method. The detection rate can thereby be calculated by integrating the parametrized probability density function using the predetermined maximum threshold value or the minimum threshold value as an integration limit. According to another alternative, the detection rate can be determined as a ratio of the number of values of the maximum value sample or the minimum value sample, which are greater than or equal to the predetermined maximum threshold value or less than or equal to predetermined minimum threshold value, and the total number of values of the maximum value sample or the minimum value sample. The detection rate that is detected in this manner can then be assigned to the at least one already known anomaly.