Comparison of three rough surface classifiers

Texture analysis techniques are used to segment rough surfaces into regions of homogeneous texture. The performance of three rough surface classifiers was assessed and compared. The classifiers differ in their discrimination as well as in their input and computational requirements. Simulation and experiment were used to identify the limitations of the classifiers and to identify which classifier is best suited to a particular task. A series of guidelines for the choice of classifier is presented and justified.