Implicit Active Model using Radial Basis Function Interpolated Level Sets

Xie Xianghua, Majid Mirmehdi

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

10 Citations (Scopus)

Abstract

Building on recent work by others who introduced RBFs into level sets for structural topology optimisation, we introduce the concept into active models and present a new level set formulation able to handle more complex topological changes, in particular perturbation away from the evolving front. This allows the initial contour or surface to be placed arbitrarily in the image. The proposed level set updating scheme is efficient and does not suffer from self-flattening while evolving, hence it avoids large numerical errors. Unlike conventional level set based active models, periodic re-initialisation is also no longer necessary and the computational grid can be much coarser, thus, it has great potential in modelling in high dimensional space. We show results on synthetic and real data for active modelling in 2D and 3D.
Translated title of the contributionImplicit Active Model using Radial Basis Function Interpolated Level Sets
Original languageEnglish
Title of host publicationProceedings of the 18th British Machine Vision Conference
PublisherBMVA Press
Publication statusPublished - 2007

Bibliographical note

Other page information: 1040-1049
Conference Proceedings/Title of Journal: Proceedings of the 18th British Machine Vision Conference
Other identifier: 2000752

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