New Hausdorff distances based on robust statistics for comparing images

Abstract

A Hausdorff distance (HD) is one of commonly used measures for object matching. It calculates the distance between two point sets of edges in two-dimensional binary images without establishing correspondences. This paper analyzes the conventional HD measures based on robust statistics, and proposes robust HD measures based on M-estimation, least trimmed square (LTS), and /spl alpha/-trimmed mean methods. The matching performance by the conventional and proposed HD measures is compared with synthetic and real images.