Department of Pattern Recognition

(eng): The aim of this article is to propose a model to automatically predict visual judgement of sparkle and graininess of special effect pigments used in industrial coatings. Many applications in the paint and coatings, printing and plastics industry rely on multi-angle color measurements with the aim of properly characterizing the appearance, i.e., the color and texture of the manufactured surfaces. However, when it comes to surfaces containing effect pigments, these methods are in many cases insufficient and it is particularly texture characterization methods that are needed. There are two attributes related to texture that are commonly used: (1) diffuse coarseness or graininess and (2) sparkle or glint impression. In this paper, we analyzed visual perception of both texture attributes using two different psychophysical studies of 38 samples painted with effect coatings including different effect pigments and 31 test persons. Our previous work has shown a good agreement between a study using physical samples with one that uses high-resolution photographs of these sample surfaces. We have also compared the perceived (1) graininess and (2) sparkle with the performance of two commercial instruments that are capable of capturing both attributes. Results have shown a good correlation between the instruments’ readings and the psychophysical studies. Finally, we implemented computational models predicting these texture attributes that have a high correlation with the instrument readings as well as the psychophysical data. By linear scaling of the predicted data using instruments readings, one can use the proposed model for the prediction of graininess and both static and dynamic sparkle values.