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Quality assessment is an important step in production processes of metal parts. This step is required in order to check whether surface quality meets the requirements. Progress in the field of computing technologies and computer vision gives the possibility of visual surface quality control with industrial cameras and image processing methods. Authors of different papers proposed various texture feature algorithms which are suitable for different fields of images processing. In this research 27 texture features were calculated for surface images taken in different lighting conditions. Correlation coefficients between these 2D texture features and 11 roughness 3D parameters were calculated. A strong correlation between 2D features and 3D parameters occurred for images captured under ring light conditions.

The application of texture features to quality control of metal surfaces