Hybrid texture identification method

Abstract

The importance of the modeling mode in systems of computer visual pattern recognition is shown. The purpose of the mode is to determine the types of textures that are present on the images processed in intelligent diagnostic systems. Images processed in technical diagnostic systems contain texture regions, which can be represented by different types of textures - spectral, statistical and spectral-statistical. Texture identification methods, such as, statistical, spectral, expert, multifractal, which are used to identify and analyze texture images, have been analyzed. To determine texture regions on images that are of a combined spectral-statistical nature, a hybrid texture identification method has been developed which makes it possible to take into account the local characteristics of the texture based on multifractal indicators characterizing the non-stationarity and impulsite of the data and the sign of the spectral texture. The stages of the developed hybrid texture identification method are: preprocessing; formation of the primary features vector; formation of the secondary features vector. The formation of the primary features vector is performed for the selected rectangular fragment of the image, in which the multifractal features and the spectral texture feature are calculated. To reduce the feature space at the stage of formation of the secondary identification vector, the principal component method was used. An experimental study of the developed hybrid texture identification method textures on model images of spectral, statistical, spectralstatistical textures has been carried out. The results of the study showed that the developed method made it possible to increase the probability of correct determination of the region of the combined spectral-statistical texture. The developed identification method was tested on images from Brodatz album of textures and images of wear zones of cutting tools, which are processed in intelligent systems of technical diagnostics. The probability of correctly identifying areas of spectral-statistical texture in the images of wear zones of cutting tools averaged 0.9, which is sufficient for the needs of practice