Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
International Journal of Computer Vision
Gradient vector flow deformable models
Handbook of medical imaging
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
A Variational Framework for Active and Adaptative Segmentation of Vector Valued Images
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
A New Active Convex Hull Model for Image Regions
Journal of Mathematical Imaging and Vision
A New Automatic Concavity Extraction Model
SSIAI '06 Proceedings of the 2006 IEEE Southwest Symposium on Image Analysis and Interpretation
Pattern Theory: From Representation to Inference
Pattern Theory: From Representation to Inference
Active Contour External Force Using Vector Field Convolution for Image Segmentation
IEEE Transactions on Image Processing
AUTOMATIC OBJECT IDENTIFICATION USING VISUAL LOW LEVEL FEATURE EXTRACTION AND ONTOLOGICAL KNOWLEDGE
Journal of Integrated Design & Process Science
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This paper presents a new deformable model capable of segmenting images with multiple complex objects and deep concavities. The method integrates a shell algorithm, an active contour model and two active convex hull models. The shell algorithm automatically inscribes every image object into a single convex curve. Every curve is evolved to the boundary's vicinity by the exact solution of a specific form of the heat equation. Further, if re-parametrization is applied at every time step of the evolution the active contour will converge to deep concavities. But if distance function minimization or line equation is used to stop the evolution, the active contour will define the convex hull of the object. Set of experiments are performed to validate the theory. The contributions, the advantages and bottlenecks of the model are underlined at the end by a comparison against other methods in the field.