An artificial neural net assisted approach to editing edges in petrographic images collected with the rotating polarizer stage

A practical methodology to edit edges within petrographic images is presented. The procedure uses the existing output of a standard segmentation routine as input. Edges are skeletonized and converted into segments which separate two grains and join at nodal points. As series of colour and texture parameters are extracted from all grains. For each edge segment, an artificial neural net (ANN) evaluates differences in the parameters for the grains separated by the segment. ANN output is used to classify segments as true or false edges and can be thresholded at different levels. A manual procedure allows for the final classification of the segments. This method significantly improves the speed with which edges can be edited in preparation for other studies.