Texture analysis of an image by using a rotation-invariant model
Christophe Rosenberger, Kacem Chehdi, Claude Cariou, Jean-Marc Ogier · 1999
Texture analysis is an important problem in image processing because it conditions the quality of image segmentation and interpretation. We propose in this communication a texture model which is invariant by rotation and whose parameters allow to characterize at the same time the type of texture and its tonal primitive. The originality of the model proposed lies in the use of the Wold decomposition to model the 1D normalized autocovariance. This function is computed from the 2D normalized autocovariance of a texture. Finally, parameters of the model are estimated by using a genetic algorithm. Experimental results on textures from the Brodatz album and synthetic textures show a modeling error lower than 0.06.