Automated region of interest retrieval of metallographic images for quality classification in industry

Abstract

The aim of the research is the development and testing of new methods to classify the quality of metallographic samples of steels with high added value, for example, grades X70 according API. In this paper, we address the development of methods to classify the quality of slab samples images with the main emphasis on the quality of the image center called as a segregation area. For this reason, we introduce an alternative method for automated retrieval of the region of interest. In the first step, the metallographic image is segmented using both spectral method and thresholding. Then, the extracted macrostructure of the metallographic image is automatically analyzed by statistical methods. Finally, the automatically extracted region of interest is compared with the results of human experts. A practical experience with retrieval of non-homogeneous noised digital images in an industrial environment is discussed as well.