A Similarity Metric for Multimodal Images Based on Modified Hausdorff Distance

Abstract

This paper presents a similarity metric on multimodal images utilizing curves as comparing primitives. Curves are detected from images, and then junctions are detected along curves and used to partition curves into subcurves. A modified Hausdorff distance is applied to determine whether a test subcurve is matched to a reference curve. The similarity metric is defined to be the number of matched curves. The number of overlapped edge pixels between two images is also defined on the basis of matched curves, which does not require accurately localizing edge pixels. The partitioning scheme avoids addresing curve partial matching and allows for test subcurves being matched to a reference curve if they correspond to each other. Experimental results show that the presented similarity metric gives more robust and reliable results, especially under noise.