An Efficient Class of Alternating Sequential Filters in Morphology

Abstract

In this note, an efficient class of alternating sequential filters (ASFs) in mathematical morphology is presented to reduce the computational complexity in the conventional ASFs about a half. The performance boundary curves of the new filters are provided. Experimental results from applying these new ASFs to texture classification and image filtering (grayscale and binary) show that comparable performance can be achieved while much of the computational complexity is reduced.