Estimation of Multiple Orientations at Corners and Junctions

Features like junctions and corners are a rich source of information for image understanding. We present a novel theoretical framework for the analysis of such 2D features in scalar and multispectral images. We model the features as occluding superpositions of two different orientations and derive a new constraint equation based on the tensor product of two directional derivatives. The eigensystem analysis of a 3 × 3-tensor then provides the so-called mixed-orientation parameters (MOP) vector that encodes the two orientations uniquely, but only implicitly. We then show how to separate the MOP vector into the two orientations by finding the roots of a second-order polynomial. Based on the orientations, the occluding boundary and the center of the junction are easily determined. The results confirm the validity, robustness, and accuracy of the approach.