Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Jump-Diffusion Process for Computing Medial Axes in Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shock Graphs and Shape Matching
International Journal of Computer Vision
International Journal of Computer Vision
Matching Free Trees, Maximal Cliques, and Monotone Game Dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representation and Self-Similarity of Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Free Trees, Maximal Cliques, and Monotone Game Dynamics
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
On the Local Form and Transitions of Symmetry Sets, Medial Axes, and Shocks
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Stochastic Computation of Medial Axis in Markov Random Fields
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Segmenting by Seeking the Symmetry Axis
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Gray skeletons and segmentation of shapes
Computer Vision and Image Understanding
Skeleton Pruning by Contour Partitioning with Discrete Curve Evolution
IEEE Transactions on Pattern Analysis and Machine Intelligence
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Gray skeletons and segmentation of shapes
Computer Vision and Image Understanding
A skeleton family generator via physics-based deformable models
IEEE Transactions on Image Processing
Distance functions and skeletal representations of rigid and non-rigid planar shapes
Computer-Aided Design
Image skeletonization based on curve skeleton extraction
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: design and development approaches - Volume Part I
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
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Medial zones: Formulation and applications
Computer-Aided Design
Flexible shape-based query rewriting
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
NURBS skeleton: a new shape representation scheme using skeletonization and NURBS curves modeling
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Empirical mode decomposition on skeletonization pruning
Image and Vision Computing
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Representing shapes is a significant problem for vision systems that must recognize or classify objects. We derive a representation for a given shape by investigating its self-similarities, and constructing its shape axis(SA) and shape axis tree (SA-tree).We start with a shape, its boundary contour, and two different parameterizations for the contour. To measure its self-similarity we consider matching pairs of points (and their tangents) along the boundary contour, i.e., matching the two parameterizations. The matching, or self-similarity criteria may vary, e.g., co-circularity, parallelism, distance, region homogeneity. The loci of middle points of the pairing contour points are the shape axis and they can be grouped into a unique tree graph, the SA-tree. The shape axis for the co-circularity criteria is compared to the symmetry axis. An interpretation in terms of object parts is also presented.