Fundamentals of Stop and Go active models

Oriol Pujol, Debora Gil, Petia Radeva

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)

Abstract

An efficient snake formulation should conform to the idea of picking the smoothest curve among all the shapes approximating an object of interest. In current geodesic snakes, the regularizing curvature also affects the convergence stage, hindering the latter at concave regions. In the present work, we make use of characteristic functions to define a novel geodesic formulation that decouples regularity and convergence. This term decoupling endows the snake with higher adaptability to non-convex shapes. Convergence is ensured by splitting the definition of the external force into an attractive vector field and a repulsive one. In our paper, we propose to use likelihood maps as approximation of characteristic functions of object appearance. The better efficiency and accuracy of our decoupled scheme are illustrated in the particular case of feature space-based segmentation. © 2005 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)681-691
JournalImage and Vision Computing
Volume23
DOIs
Publication statusPublished - 1 Aug 2005

Keywords

  • Deformable models
  • Geodesic snakes
  • Region-based segmentation

Cite this

Pujol, O., Gil, D., & Radeva, P. (2005). Fundamentals of Stop and Go active models. Image and Vision Computing, 23, 681-691. https://doi.org/10.1016/j.imavis.2005.03.007