Frequency and orientation sensitive texture measures using linear symmetry

Abstract

Abstract An efficient method for computing texture features based on dominant local orientation is introduced. The features are computed as a Laplacian pyramid is built. At each level of the Laplacian pyramid, the linear symmetry feature is computed. This feature is anisotropic and estimates the optimal local orientation in the Least Square Error (LSE) sense. It is complex valued and hence consists of two components, the local orientation estimate and its confidence measure based on the error. The algorithm is based on convolutions with simple separable filters and pixel-wise non-linear arithmetic operations. These properties allow highly parallel implementation, for example on a pyramid machine, yielding real time applications. Comparative experimental results are presented using the feature for unsupervised segmentation on test images of natural aerial image textures.