Paper Info
Title
A Moving Grid Framework for Geometric Deformable Models.
Abstract
Geometric deformable models based on the level set method have become very popular in the last decade. To overcome an inherent limitation in accuracy while maintaining computational efficiency, adaptive grid techniques using local grid refinement have been developed for use with these models. This strategy, however, requires a very complex data structure, yields large numbers of contour points, and is inconsistent with the implementation of topology-preserving geometric deformable models (TGDMs). In this paper, we investigate the use of an alternative adaptive grid technique called the moving grid method with geometric deformable models. In addition to the development of a consistent moving grid geometric deformable model framework, our main contributions include the introduction of a new grid nondegeneracy constraint, the design of a new grid adaptation criterion, and the development of novel numerical methods and an efficient implementation scheme. The overall method is simpler to implement than using grid refinement, requiring no large, complex, hierarchical data structures. It also offers an extra benefit of automatically reducing the number of contour vertices in the final results. After presenting the algorithm, we demonstrate its performance using both simulated and real images.
Year
DOI
Venue
2009
10.1007/s11263-009-0231-3
International Journal of Computer Vision
Keywords
Field
DocType
Adaptive grid method,Geometric deformable model,Deformation moving grid,Topology preservation,Level set method
Computer science,Artificial intelligence,Hierarchical database model,Data structure,Computer vision,Mathematical optimization,Level set method,Contour line,Grid method multiplication,Algorithm,Real image,Grid,Mesh generation
Journal
Volume
Issue
ISSN
84
1
0920-5691
Citations 
PageRank 
References 
3
0.39
27
Authors
3
Name
Order
Citations
PageRank
Xiao Han181675.26
Chenyang Xu252335.12
Jerry L. Prince34990488.42