Grading Textured Surfaces with Automated Soft Clustering in a Supervised SOM

We present a method for automated grading of texture samples which grades the sample based on a sequential scan of overlapping blocks, whose texture is classified using a soft partitioned SOM, where the soft clusters have been automatically generated using a labelled training set. The method was devised as an alternative to manual selection of hard clusters in a SOM for machine vision inspection of tuna meat. We take advantage of the sequential scan of the sample to perform a sub-optimal search in the SOM for the classification of the blocks, which allows real time implementation.