EXM Eigen Templates for Detecting and Classifying Arbitrary Junctions

A novel method for extracting parametric junction and corner features in images is presented. By treating each complex feature as a combination of elementary line and edge features, the method provides an efficient way to locate features of interest with reduced number of filtering operations. The Expansion Matching (EXM) method is used to design optimal detectors for a set of elementary model shape features. Next, the principal components of the Karhunen-Loeve (KL) representation of these model EXM detectors are used to filter the image and extract candidate interest points derived from the energy peaks of the eigen coefficients. The KL coefficients at these candidate points are then used to efficiently reconstruct the response and differentiate real junctions and corners from arbitrary features in the image. The method is robust to additive noise and is able to successfully extract, classify and find the myriad compositions of corner and junction features formed by combinations of two or more elementary edges or lines.