International Journal of Computer Vision
Globally constrained deformable models for 3D object reconstruction
Signal Processing - Special issue on deformable models and techniques for image and signal processing
A Unified Framework for Atlas Matching Using Active Appearance Models
IPMI '99 Proceedings of the 16th International Conference on Information Processing in Medical Imaging
Multi-scale 3-D Deformable Model Segmentation Based on Medial Description
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Intuitive, Localized Analysis of Shape Variability
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Medial Models Incorporating Object Variability for 3D Shape Analysis
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
International Journal of Computer Vision
On the Representation and Matching of Qualitative Shape at Multiple Scales
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A shape-navigated image deformation model for 4D lung respiratory motion estimation
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
ISBMS'06 Proceedings of the Third international conference on Biomedical Simulation
Real-Time simulation of deformable soft tissue based on mass-spring and medial representation
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
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This paper describes the basis and behavior of segmentation of single figures in 3D by deformable m-reps models. Results are given for the segmentation of kidneys from CT and of hippocampi from MR images. Special focus is made on multi-scale-level stages of segmentation, on intrinsic correspondences under deformation that are provided by m-reps, and on the match against model-relative templates provided by both theoretical edge strength templates and templates derived from training images.