Visual reconstruction
Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
A level set approach for computing solutions to incompressible two-phase flow
Journal of Computational Physics
Evolutionary fronts for topology-independent shape modeling and recovery
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multidimensional alignment using the Euclidean distance transform
Graphical Models and Image Processing
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Using Prior Shapes in Geometric Active Contours in a Variational Framework
International Journal of Computer Vision
International Journal of Computer Vision
Matching Distance Functions: A Shape-to-Area Variational Approach for Global-to-Local Registration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Recognizing Objects by Matching Oriented Points
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Curves Matching Using Geodesic Paths
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Shape-Based Segmentation Approach: An Improved Technique Using Level Sets
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Shape Registration in Implicit Spaces Using Information Theory and Free Form Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Partial Differential Equations and Image Analysis
Geometric Partial Differential Equations and Image Analysis
MICCAI'10 Proceedings of the 2010 international conference on Prostate cancer imaging: computer-aided diagnosis, prognosis, and intervention
A shape prior constraint for implicit active contours
Pattern Recognition Letters
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
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In this paper, we revisit the implicit front representation and evolution using the vector level set function (VLSF) proposed in [1]. Unlike conventional scalar level sets, this function is designed to have a vector form. The distance from any point to the nearest point on the front has components (projections) in the coordinate directions included in the vector function. This kind of representation is used to evolve closed planar curves and 3D surfaces as well. Maintaining the VLSF property as the distance projections through evolution will be considered together with a detailed derivation of the vector partial differential equation (PDE) for such evolution. A shape-based segmentation framework will be demonstrated as an application of the given implicit representation. The proposed level set function system will be used to represent shapes to give a dissimilarity measure in a variational object registration process. This kind of formulation permits us to better control the process of shape registration, which is an important part in the shape-based segmentation framework. The method depends on a set of training shapes used to build a parametric shape model. The color is taken into consideration besides the shape prior information. The shape model is fitted to the image volume by registration through an energy minimization problem. The approach overcomes the conventional methods problems like point correspondences and weighing coefficients tuning of the evolution (PDEs). It is also suitable for multidimensional data and computationally efficient. Results in 2D and 3D of real and synthetic data will demonstrate the efficiency of the framework.