Strategies for shape matching using skeletons
Computer Vision and Image Understanding
Retrieving articulated 3-D models using medial surfaces
Machine Vision and Applications
An intelligent system for Chinese calligraphy
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Distance functions and skeletal representations of rigid and non-rigid planar shapes
Computer-Aided Design
Shape recognition and retrieval: A structural approach using velocity function
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Bone graphs: Medial shape parsing and abstraction
Computer Vision and Image Understanding
Object categorization using bone graphs
Computer Vision and Image Understanding
A family of skeletons for motion planning and geometric reasoning applications
Artificial Intelligence for Engineering Design, Analysis and Manufacturing - Representing and Reasoning About Three-Dimensional Space
Medial zones: Formulation and applications
Computer-Aided Design
Learning-Based symmetry detection in natural images
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
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Skeletal representations of 2-D shape, including shock graphs, have become increasingly popular for shape matching and object recognition. However, it is well known that skeletal structure can be unstable under minor boundary deformation, part articulation, and minor shape deformation (due to, for example, small changes in viewpoint). As a result, two very similar shapes may yield two significantly different skeletal representations which, in turn, will induce a large matching distance. Such instability occurs both at external branches as well as internal branches of the skeleton. We present a framework for the structural simplification of a shape's skeleton which balances, in an optimization framework, the desire to reduce a skeleton's complexity by minimizing the number of branches, with the desire to maximize the skeleton's ability to accurately reconstruct the original shape. This optimization yields a canonical skeleton whose increased stability yields significantly improved recognition performance.