Shock Graphs and Shape Matching
International Journal of Computer Vision
Hierarchical Decomposition and Axial Shape Description
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representation and Self-Similarity of Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Untangling the Blum Medial Axis Transform
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
A skeletal measure of 2D shape similarity
Computer Vision and Image Understanding
Structural and textural skeletons for noisy shapes
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
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Skeletons are often used as a framework for part-based shape analysis. This paper describes some useful strategies that can be employed to improve the performance of such shape matching algorithms. Four key strategies are proposed. The first is to incorporate ligature-sensitive information into the part decomposition and shape matching processes. The second is to treat part decomposition as a dynamic process in which the selection of the final decomposition of a shape is deferred until the shape matching stage. The third is the need to combine both local and global measures when computing shape dissimilarity. Finally, curvature error between skeletal segments must be weighted by the limb-width profile along the skeleton. Experimental results show that the incorporation of these strategies significantly improves the retrieval accuracy when applied to LEMS’s 99 and 216 silhouette database [10].