Estimators for Orientation and Anisotropy in Digitized Images

This paper describes a technique for characterization and segmentation of anisotropic patterns that exhibit a single local orientation. Using Gaussian derivatives we construct a gradient-square tensor at a selected scale. Smoothing of this tensor allows us to combine information in a local neighborhood without canceling vectors pointing in opposite directions. Whereas opposite vectors would cancel, their tensors reinforce. Consequently, the tensor characterizes orientation rather than direction. Usually this local neighborhood is at least a few times larger than the scale parameter of the gradient operators. The eigenvalues yield a measure for anisotropy whereas the eigenvectors indicate the local orientation. In addition to these measures we can detect anomalies in textured patterns.