Dynamic 3D Models with Local and Global Deformations: Deformable Superquadrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Closed-Form Solutions for Physically Based Shape Modeling and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Evolution With Structural and Topological Changes Using Blending
IEEE Transactions on Pattern Analysis and Machine Intelligence
Time-Continuous Segmentation of Cardiac Image Sequences Using Active Appearance Motion Models
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
3-D Deformable Registration of Medical Images Using a Statistical Atlas
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
A shock grammar for recognition
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Hierarchical Vibrations: A Structural Decomposition Approach for Image Analysis
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Stable Structural Deformations
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
Hierarchical vibrations for part-based recognition of complex objects
Pattern Recognition
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A new deformable shape model is defined with the following properties: (1) A-priori knowledge describes shapes not only by statistical variation of a fixed structure like active shape/appearance model but also by variability of structure using a production system. (2) Multiresolution description of shape structures enable more constrained statistical variation of shape as the model evolves in fitting the data. (3) It enables comparison between different shapes as well as characterizing and reconstructing instances of the same shape. Experiments on simulated 2D shapes demonstrate the ability of the algorithm to find structures of different shapes and also to characterize the statistical variability between instances of the same shape.