Automatic extraction of texture-periodicity using superposition of distance matching functions and their forward differences

In this paper, we propose a method of automatic detection of texture-periodicity using superposition of distance matching functions (DMFs) followed by computation of their forward differences. The method has been specifically devised for automatically identifying row and column periodicities and thereby the size of periodic units from textile fabrics belonging to any of the 17 wallpaper groups and is a part of automatic fabric defect detection scheme being developed by us that needs periodicities along row and column directions. Overall row-DMF (or overall column-DMF) is obtained based on superposition of DMF of all rows (or columns) from the input image and its second forward difference is computed to get the overall maximum which is a direct measure of periodicity along row (or column) direction. Results from experiments on various near-regular textures demonstrate the capability of the proposed method for automatic periodicity extraction without the need of human intervention.

► We propose a method of automatic detection of texture-periodicity.
► It does not need any predefined threshold or human intervention.
► It uses superposition of distance matching functions and their forward differences.
► It can identify periodicities from images belonging to 17 wallpaper groups.