A Line Feature Matching Technique Based on an Eigenvector Approach

In this paper, we propose a new eigenvector-based line feature matching algorithm, which is invariant to the in-plane rotation, translation, and scale. First, in order to reduce the number of possible matches, we use a preliminary correspondence test that generates a set of finite candidate models, by restricting combinations of line features in the input image. This approach resolves an inherent problem relating to ordering and correspondence in an eigenvector/modal approach. Second, we employ the modal analysis, in which the Gaussian weighted proximity matrices for reference and candidate models are constructed to record the relative distance and angle information between line features for each model. Then, the modes of the proximity matrices of the two models are compared to yield the dissimilarity measure, which describes the quantitative degree of the difference between the two models. Experimental results for synthetic and real images show that the proposed algorithm performs matching of the line features with affine variation fast and efficiently and provides the degree of dissimilarity in a quantitative way.