Discrete scale axis representations for 3D geometry
ACM SIGGRAPH 2010 papers
A similarity-based approach for shape classification using Aslan skeletons
Pattern Recognition Letters
Skeleton growing and pruning with bending potential ratio
Pattern Recognition
Pseudo three-dimensional vision-based nail-fold morphological and hemodynamic analysis
Computers in Biology and Medicine
On Using Anisotropic Diffusion for Skeleton Extraction
International Journal of Computer Vision
A skeleton pruning algorithm based on information fusion
Pattern Recognition Letters
Empirical mode decomposition on skeletonization pruning
Image and Vision Computing
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Medial representations of shapes are useful due to their use of an object-centered coordinate system that directly captures intuitive notions of shape such as thickness, bending, and elongation. However, it is well known that an object's medial axis transform (MAT) is unstable with respect to small perturbations of its boundary. This instability results in additional, unwanted branches in the skeletons, which must be pruned in order to recover the portions of the skeletons arising purely from the uncorrupted shape information. Almost all approaches to skeleton pruning compute a significance measure for each branch according to some heuristic criteria, and then prune the least significant branches first. Current approaches to branch significance computation can be classified as either local, solely using information from a neighborhood surrounding each branch, or global, using information about the shape as a whole. In this paper, we propose a third, groupwise approach to branch significance computation. We develop a groupwise skeletonization framework that yields a fuzzy significance measure for each branch, derived from information provided by the group of shapes. We call this framework the Groupwise Medial Axis Transform (G-MAT). We propose and evaluate four groupwise methods for computing branch significance and report superior performance compared to a recent, leading method. We measure the performance of each pruning algorithm using denoising, classification, and within-class skeleton similarity measures. This research has several applications, including object retrieval and shape analysis.