Abstract
Invariant Thinning and Distance Transform. Thinning is a preprocessing method which is applied to binary (i.e. black-and-white) digital (i.e. discretized) images. The goal of thinning is to reduce the sets of black points in the image to “thin” sets while retaining the “topology” of them as well as “form” properties. Usually thinning methods are organized in an iterative way by “peeling off” outer layers of the sets under consideration. This implies that thinning is an extremely time-consuming task. Recently, Neusius and Olszewski [12] proposed a thinning method which is based on a distance transform. This idea is indeed not new (see e.g. [6]), but Neusius and Olszewski were the first to treat it in a systematic way. Since the distance transform can be calculated efficiently by a two-sweep method, this approach looks attractive. The aim of this paper is to show that under certain assumptions a ‘classical’ thinning method [4], which has invariance with respect to motions as a distinctive feature, also can be interpreted as a distance-transform-based method.
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Eckhardt, U., Latecki, L. (1996). Invariant Thinning and Distance Transform. In: Kropatsch, W., Klette, R., Solina, F., Albrecht, R. (eds) Theoretical Foundations of Computer Vision. Computing Supplement, vol 11. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6586-7_2
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DOI: https://doi.org/10.1007/978-3-7091-6586-7_2
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