Texture periodicity detection: features, properties, and comparisons

The structure extraction problem is analyzed. The cooccurrence matrices (CMs) are the popular basis for this goal. We show that a binary preparation of an arbitrary periodical texture preserves its structure. This transformation decreases the computation time of analysis and the required memory. Twenty-two features adapted for detecting displacement vectors on binarized images are analyzed and compared. We suggest using the CM elements jointly as the united feature for this goal. We show that it is a stable detector for noisy images and simpler than well-known /spl chi//sup 2/ and /spl kappa/ statistics.