Estimators for Orientation and Anisotropy in Digitized Images
Lucas J. van Vliet, P.W. Verbeek · Data Archiving and Networked Services (DANS) · 1995
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.