S. Ghodrati - Department of Polymer Engineering and Color Technology, Amirkabir University of Technology, Tehran, Iran.
M. Mohseni - Department of Polymer Engineering and Color Technology, Amirkabir University of Technology, Tehran, Iran.
S. Gorji Kandi - Department of Polymer Engineering and Color Technology, Amirkabir University of Technology, Tehran, Iran.

Surface texture recognition and classification is a challenging issue in both material science and human behavioral studies. In this study a feasible method based on image histogram analysis is introduced for surface roughness prediction. Our results show that image luminance histogram skewness, strongly correlates with both actual and visual surface roughness. Here, we employed a set of sandpapers with varying surface roughness as the stimuli set. To investigate the impact of image resolution on luminance histogram skewness, the images of the samples were captured by a digital scanner with nine different dpi values. The results show that as the image resolution increases luminance histogram skewness performance to predict visual and actual surface roughness increases as well.

kewness- Image processing- Surface texture- Visual perception