Active shape models—their training and application
Computer Vision and Image Understanding
The crust and the &Bgr;-Skeleton: combinatorial curve reconstruction
Graphical Models and Image Processing
Robust Image Corner Detection Through Curvature Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new and simple algorithm for quality 2-dimensional mesh generation
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
A Framework for Automatic Landmark Identification Using a New Method of Nonrigid Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A System for Person-Independent Hand Posture Recognition against Complex Backgrounds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Undersampling and oversampling in sample based shape modeling
Proceedings of the conference on Visualization '01
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape context and chamfer matching in cluttered scenes
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Open-Curve shape correspondence without endpoint correspondence
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Pre-organizing Shape Instances for Landmark-Based Shape Correspondence
International Journal of Computer Vision
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This paper describes a novel approach to automatically recover accurate correspondence over various shapes. In order to detect the features points with the capability in capturing the characteristics of an individual shape, we propose to calculate the skeletal representation for the shape curve through the medial axis transform. Employing this shape descriptor, mathematical landmarks are automatically identified based on the local feature size function, which embodies the geometric and topological information of the boundary. Before matching the resulting landmarks, shape correspondence is first approached by matching the major components of the shape curves using skeleton features. This helps in keeping the consecutive order and reducing the search space during the matching process. Point matching is then performed within each pair of corresponding components by solving a consecutive assignment problem. The effectiveness of this approach is demonstrated through experimental results on several different training sets of biomedical object shapes.