Analysis of surface roughness parameters digital image identification

Abstract

The article analyses some surface roughness parameters of metal parts determining the ability of the surface of digital image identification, covariance functions and Wavelet’s wave theory. Expressions of covariance functions are formed using random functions, made by spreading digital image pixel arrays by columns in the form of individual vectors. The digital images used for research may vary in scale, because the frequencies of colour waves with individual pixels remain constant in the images, therefore, the image change does not influence the scale in computing covariance functions. The colour spectrum of RGB format was applied to identify the surface images of the parts. There was analysed the influence of individual RGB colour tensor components on the estimates of digital image covariance functions. The identity of digital images was evaluated by the change of correlation coefficient values in the range of RGB colours. The software was applied to compute the above process.