Skeletons in Digital Image Processing

Skeletonization is a transformation of a component of a digital image into a subset of the original component. There are different categories of skeletonization methods: one category is based on distance transforms, and a specified subset of the transformed image is a distance skeleton. The original component can be reconstructed from the distance skeleton. Another category is defined by thinning approaches; and the result of skeletonization using thinning algorithms should be a connected set of digital curves or arcs. Motivations for interest in skeletonization algorithms are the need to compute a reduced amount of data or to simplify the shape of an object in order to find features for recognition algorithms and classifications. Additionally the transformation of a component into an image showing essential characteristics can eliminate local noise at the frontier. Thinning algorithms are a very active area of research, with a main focus on connectivity preserving methods allowing parallel implementation. There are hundreds of publications on different aspects of these transformations. This report reviews contributions in this area with respect to properties of algorithms and characterizations of simple points, and informs about a few new results. 1 Centre for Image Technology and Robotics, Tamaki Campus, Building 731, The University of Auckland, Morrin Road, Glen Innes, Auckland 1005, New Zealand. You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the CITR Tamaki web site under terms that include this permission. All other rights are reserved by the author(s). Skeletons in Digital Image Processing Gisela Klette CITR Tamaki, University of Auckland Tamaki Campus, Building 731, Auckland, New Zealand