Purpose. The goal of this work was to develop and study the methods of texture classification of SEM images of micro surfaces of objects based on the statistical and spectral characteristics of texture fragments, as well as a comparative analysis of segmentation methods of SEM images. Methods. The determination of the texture characteristics was based on statistical moments computed by the brightness histogram of a SEM- image or its region. The spectral measures of texture of SEM image were based on properties of the Fourier spectrum. To determine the spectral texture characteristics, the parameters of the amplitude and axial functions were chosen. SEM images were segmented using four methods, namely: the global thresholding; the region growing; the region splitting and merging; and the watershed using markers. Results. The experiments on texture classification of the SEM series of soils and metals images showed the best result of texture classification by the feature of homogeneity compared to other statistical characteristics. Calculation of the spectral characteristics was used to detect the directionality of periodic or almost periodic texture elements in the SEM images of metals. Classification results using spectral properties and homogeneity values made it possible to obtain generalized texture characteristics of SEM images of metals. A comparative analysis of the four segmentation methods showed that the best result of finding the boundaries of objects in the SEM image was obtained by the watershed method using markers. Software implementation of texture classification and image segmentation methods were performed in the MatLab system. Scientific novelty. The authors proposed a method for classifying SEM-images based on spectral texture characteristics using the parameters of the amplitude and axial functions. It is shown that the segmentation by the splitting and merging method allows you to set the conditions for selecting regions with certain texture characteristics in the SEM-image. The practical significance. A generalized characteristic of SEM-image texture, determined by statistical and spectral measurements, is that it would be useful for automatic texture recognition and SEM-images analysis. The selection of regions with certain texture characteristics is the preprocessing step for finding points of interest suitable for the SEM-image matching and objects recognition.
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