Material Classification Using Morphological Pattern Spectrum for Extracting Textural Features from Material Micrographs

Abstract

In this paper, we address one very important industrial application of computer vision – automatic classification of materials. In our work, we have considered materials that are mixtures of two or more elements. Such materials are called alloys. It is observed at the microscopic level that an alloy is composed of small randomly distributed crystals of varying shapes and sizes called grains. Also, the color and hence the intensity of the grains vary in alloys. Generally, this shape-size-intensity distribution of the grains is different for different materials. This means micrographs obtained from different materials form texture-like images that differ from one material to another in appearance. Therefore, in principle, any texture analysis method may be used for material classification. In our method, we propose to extract textural features corresponding to grain geometry and intensity and use them for analysis and classification of alloys. These features are extracted via gray-scale morphological operations and are measured in terms of Size-Intensity-Diagram (SID) and Tri-variate Pattern Spectrum (TPS) coefficients. In our experiments, we achieved 83.43% and 89.43% classification accuracies in cases of SID and TPS, respectively. This demonstrates the effectiveness of the proposed method for material classification which in turn confirms that our choice of features is indeed appropriate for the purpose.