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Author Oriol Pujol; Debora Gil; Petia Radeva
Title Fundamentals of Stop and Go active models Type Journal Article
Year 2005 Publication Image and Vision Computing Abbreviated Journal
Volume 23 Issue 8 Pages 681-691
Keywords Deformable models; Geodesic snakes; Region-based segmentation
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.
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Corporate Author Thesis
Publisher Butterworth-Heinemann Place of Publication Newton, MA, USA Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0262-8856 ISBN Medium
Area Expedition Conference
Notes IAM;MILAB;HuPBA Approved no
Call Number IAM @ iam @ PGR2005 Serial 1629
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