Ivana Tomić

Selection of optimal features for texture characterization and perception

Different approach to texture characterization can be considered. In this work texture are analyzed through second order statistical measurements based on the Grey-Level Co-occurrence Matrix proposed by Haralick [1]. By this method is possible to compute 22 different features to describe texture. Usually, in previous works, only 5 features are considered among the complete set, but no reasons are exposed for that selection. In this work, using Principal Component Analysis, the set of features is studied and 5 features, different from former, are proposed as the most convenient describing and characterizing the considered textures. Finally, the relationship between the proposed features and perception of texture is analyzed.

Reflectance recovery for coated printed color samples via multiangular RGB camera measurements

In several cases, the spectral properties of objects change and depend on the viewing angle. Some example of this kind of samples can be found in the printing industry where special effect coatings are used to obtain some effects on the surface of the printed media. This effect can be an additional gloss and even an effect of a color change with the viewing angle (goniochromism). To be colorimetrically characterized these samples require multiangular reflectance measurements. In this paper we represent reflectance recovery results for these coated samples using regression models and RGB measurements from multiple camera capture angles. The estimations are performed using 3-dimensional and 9-dimensional RGB data. We evaluated the differences in estimation performance for the chosen three angles by using standard color and spectral metrics and compared the recovery with 3-dimensional and 9-dimensional RGB data.