A similarity metric for matching incomplete edge curves Online publication date: Sat, 15-Nov-2014
by Yong Li; Robert L. Stevenson
International Journal of Machine Intelligence and Sensory Signal Processing (IJMISSP), Vol. 1, No. 2, 2014
Abstract: This paper presents a similarity metric for use in comparing incomplete edge curves extracted from multimodal images. A common problem with multimodal image registration based on extracted integral curves is that a curve in one image may not be exactly mapped to a corresponding curve in a different image. To address this problem, this work proposes a similarity metric based on extracted subcurves as a base primitive. Given a test and reference image, curves are extracted from both images. Junctions are detected on test curves, and used to partition the test curves into subcurves. Partitioning allows for the test subcurves being matched to a reference curve if they correspond to each other. A test subcurve is defined to be matched to a reference curve if its average distance to the reference curve is small. The number of matched curves and the number of pixels on matched curves are shown to be a useful similarity metric. Experimental results show that the presented similarity metric gives more robust and reliable results.
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