Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Dynamic Programming for Solving Variational Problems in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic 3D Models with Local and Global Deformations: Deformable Superquadrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature extraction from faces using deformable templates
International Journal of Computer Vision
A Bayesian Approach to Dynamic Contours Through Stochastic Sampling and Simulated Annealing
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
Dynamic Programming for Detecting, Tracking, and Matching Deformable Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
International Journal of Computer Vision
Deformable template models: a review
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Smooth surface reconstruction via natural neighbour interpolation of distance functions
Proceedings of the sixteenth annual symposium on Computational geometry
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boundary Finding with Prior Shape and Smoothness Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using a geometric formulation of annular-like shape priors for constraining variational level-sets
Pattern Recognition Letters
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To incorporate prior shape information into a deformable model either local or global shape modeling must be carried out. Local shape modeling involves manual interaction to accumulate information on the shape variability of any object. It depends on the existence of homologous points, or landmarks, that must be unambiguously and consistently located in different specimens. Global shape modeling does not require the existence of landmarks. Global properties can be characterized using only a few parameters, and tend to be much more stable than local properties.In this work we propose a new approach that combines the benefits of local and global shape modeling in the field of level-set approaches. The method starts with local shape parameterization, which eases user interaction. Then, the shape is converted into an implicit representation which exploits the stability and compactness of global shape parameters.