Landmark-based shape deformation with topology-preserving constraints

Song Wang, Jim Ji, Zhi Pei Liang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

This paper presents a novel approach for landmark-based shape deformation, in which fitting error and shape difference are formulated into a support vector machine (SVM) regression problem. To well describe nonrigid shape deformation, this paper measures the shape difference using a thin-plate spline model. The proposed approach is capable of preserving the topology of the template shape in the deformation. This property is achieved by inserting a set of additional points and imposing a set of linear equality and/or inequality constraints. The underlying optimization problem is solved using a quadratic programming algorithm. The proposed method has been tested using practical data in the context of shape-based image segmentation. Some relevant practical issues, such as missing detected landmarks and selection of the regularization parameter are also briefly discussed.

Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Computer Vision
Pages923-930
Number of pages8
Volume2
Publication statusPublished - 2003
Externally publishedYes
EventNINTH IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION - Nice, France
Duration: 13 Oct 200316 Oct 2003

Other

OtherNINTH IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION
CountryFrance
CityNice
Period13/10/0316/10/03

    Fingerprint

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Wang, S., Ji, J., & Liang, Z. P. (2003). Landmark-based shape deformation with topology-preserving constraints. In Proceedings of the IEEE International Conference on Computer Vision (Vol. 2, pp. 923-930)