Thinning Methodologies-A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Computer Vision and Image Understanding
Zoom-invariant vision of figural shape: the mathematics of cores
Computer Vision and Image Understanding
A fast parallel algorithm for thinning digital patterns
Communications of the ACM
International Journal of Computer Vision
Multiscale Medial Loci and Their Properties
International Journal of Computer Vision - Special Issue on Research at the University of North Carolina Medical Image Display Analysis Group (MIDAG)
Recognition of Shapes by Editing Their Shock Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Strategies for shape matching using skeletons
Computer Vision and Image Understanding
Strategies for part-based shape analysis using skeletons
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
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The extraction of consistent skeletons in the presence of boundary noise is still a problem for most skeletonization algorithms. Many suppress skeletons associated with boundary perturbation, either by preventing their formation or removing them subsequently using additional operations. A more appropriate approach is to view a shape as comprising of structural and textural skeletons. The former describes the general structure of the shape and the latter its boundary characteristics. These two types of skeletons should be encouraged to remaining disconnected to facilitate gross shape matching without the need for branch pruning. Such skeletons can be formed by means of a multi-resolution gradient vector field (MGVF), which can be generated efficiently using a pyramidal framework. The robust scale-invariant extraction of the skeletons from the MGVF is described. Experimental results show that the MGVF structural skeletons are less affected by boundary noise compared to skeletons extract by other popular iterative and non-iterative techniques.