Statistical methods to compare the texture features of machined surfaces

Texture studies play a paramount role in many image processing applications. In this paper an attempt is made to study the textural features of machined surfaces (grinding, milling and shaping) using the most widely used statistical methods, viz, co-occurrence matrix approach, the amplitude varying rate statistical approach (AVRS) and the run length matrix approach. Textural features derived from these matrices are studied and analysed. A new matrix for the qualitative evaluation of surfaces, namely the gray-level difference-pixel distance matrix, is presented and its usefulness in texture analysis is analysed. The features calculated from these matrices are correlated with surface parameters, such as roughness, and the different features are studied for classification of these surfaces. Copyright (C) 1996 Pattern Recognition Society. Published by Elsevier Science Ltd.