Design of Perimeter Estimators for Digitized Planar Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVGIP: Image Understanding
Multidimensional digital boundaries
CVGIP: Graphical Models and Image Processing
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Medical image segmentation using topologically adaptable surfaces
CVRMed-MRCAS '97 Proceedings of the First Joint Conference on Computer Vision, Virtual Reality and Robotics in Medicine and Medial Robotics and Computer-Assisted Surgery
Deformable Model with Non-euclidean Metrics
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Defuzzification of spatial fuzzy sets by feature distance minimization
Image and Vision Computing
Fully deformable 3D digital partition model with topological control
Pattern Recognition Letters
Analysis and comparative evaluation of discrete tangent estimators
DGCI'05 Proceedings of the 12th international conference on Discrete Geometry for Computer Imagery
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Energy-minimizing techniques are an interesting approach to the segmentation problem. They extract image components by deforming a geometric model according to energy constraints. This paper proposes an extension to these works, which can segment arbitrarily complex image components in any dimension. The geometric model is a digital surface with which an energy is associated. The model grows inside the component to segment by following minimal energy paths. The segmentation result is obtained a posteriori by examining the energies of the successive model shapes. We validate our approach on several 2D images.