Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12710
Title: Extraction of the Euclidean skeleton based on a connectivity criterion
Authors: Choi, WP
Lam, KM 
Siu, WC 
Keywords: Skeletonization
Maximal disk
Medial axis transform
Distance transform
Issue Date: 2003
Publisher: Elsevier
Source: Pattern recognition, 2003, v. 36, no. 3, p. 721-729 How to cite?
Journal: Pattern recognition 
Abstract: The skeleton is essential for general shape representation. The commonly required properties of a skeletonization algorithm are that the extracted skeleton should be accurate; robust to noise, position and rotation; able to reconstruct the original object; and able to produce a connected skeleton in order to preserve its topological and hierarchical properties. However, the use of a discrete image presents a lot of problems that may influence the extraction of the skeleton. Moreover, most of the methods are memory-intensive and computationally intensive, and require a complex data structure. In this paper, we propose a fast, efficient and accurate skeletonization method for the extraction of a well-connected Euclidean skeleton based on a signed sequential Euclidean distance map. A connectivity criterion is proposed, which can be used to determine whether a given pixel is a skeleton point independently. The criterion is based on a set of point pairs along the object boundary, which are the nearest contour points to the pixel under consideration and its 8 neighbors. Our proposed method generates a connected Euclidean skeleton with a single pixel width without requiring a linking algorithm or iteration process. Experiments show that the runtime of our algorithm is faster than the distance transformation and is linearly proportional to the number of pixels of an image.
URI: http://hdl.handle.net/10397/12710
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/S0031-3203(02)00098-5
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