A class of robust edge detectors based on latin squares

Abstract

The theory of Latin Square experimental designs is extended to edge detection of multi-grey level pictorial data. Latin Square designs are realized using mask operations either as a square or in linear forms using ANOVA to estimate the model parameters. The test statistics are based upon the robust F-test and the thresholds are selected by an empirical interactive process. A post hoc comparison method is used to confine the edge element ambiguities to 2-pixel layer thickness in masks greater than 2 × 2 × k. Computer simulations are shown to verify the theory. © 1979.