Constraints on deformable models: recovering 3D shape and nongrid motion
Artificial Intelligence
Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
A fast level set method for propagating interfaces
Journal of Computational Physics
Tree methods for moving interfaces
Journal of Computational Physics
Shape and topology constraints on parametric active contours
Computer Vision and Image Understanding
Three Dimensional Object Modeling via Minimal Surfaces
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Deformable model with a complexity independent from image resolution
Computer Vision and Image Understanding
EURASIP Journal on Applied Signal Processing
Surface Extraction from Multi-Material Components for Metrology using Dual Energy CT
IEEE Transactions on Visualization and Computer Graphics
Interactive Volume Exploration for Feature Detection and Quantification in Industrial CT Data
IEEE Transactions on Visualization and Computer Graphics
3D object segmentation using B-Surface
Image and Vision Computing
Edge Detection by Adaptive Splitting II. The Three-Dimensional Case
Journal of Scientific Computing
Extended Topological Active Nets
Image and Vision Computing
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This paper proposes a generic methodology for segmentation and reconstruction of volumetric datasets based on a deformable model, the topological active volumes (TAV). This model, based on a polyhedral mesh, integrates features of region-based and boundary-based segmentation methods in order to fit the contours of the objects and model its inner topology. Moreover, it implements automatic procedures, the so-called topological changes, that alter the mesh structure and allow the segmentation of complex features such as pronounced curvatures or holes, as well as the detection of several objects in the scene. This work presents the TAV model and the segmentation methodology and explains how the changes in the TAV structure can improve the adjustment process. In particular, it is focused on the increase of the mesh density in complex image areas in order to improve the adjustment to object surfaces. The suitability of the mesh structure and the segmentation methodology is analyzed and the accuracy of the proposed model is proved with both synthetic and real images.