2010•Unpublished venueRequires access

A variationally based weighted re-initialization method for geometric active contours

Susana Merino‐Caviedes, G. Vegas-Sanchez, Maria Teresa Perez, Santiago Aja‐Fernández, Marcos Martín‐Fernández

Open publisher page 1 citations

Abstract

In geometric active contour algorithms, a re-initialization step must be performed by the level set function to remain close to a signed distance function, in order to avoid numerical instabilities. We propose a new re-initialization method that may be employed as a standalone method to recover the signed distance condition, or may be embedded directly into a variational framework as an additional term for the energy functional. Its purpose is to make the pixels near the propagating contour be less affected by the re-initialization. Experimental results show that whereas previous approaches change the position of the zero level set, our method keeps it virtually unchanged.

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What this paper is about

In geometric active contour algorithms, a re-initialization step must be performed by the level set function to remain close to a signed distance function, in order to avoid numerical instabilities. We propose a new re-initialization method that may be employed as a standalone method to recover the signed distance condition, or may be embedded directly into a variational framework as an additional term for the energy functional. Its purpose is to make the pixels near the propagating contour be less affected by the re-initialization. Experimental results show that whereas previous approaches change the position of the zero level set, our method keeps it virtually unchanged.

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Available abstract

In geometric active contour algorithms, a re-initialization step must be performed by the level set function to remain close to a signed distance function, in order to avoid numerical instabilities. We propose a new re-initialization method that may be employed as a standalone method to recover the signed distance condition, or may be embedded directly into a variational framework as an additional term for the energy functional. Its purpose is to make the pixels near the propagating contour be less affected by the re-initialization. Experimental results show that whereas previous approaches change the position of the zero level set, our method keeps it virtually unchanged.

Key concepts: Initialization, Signed distance function, Position (finance), Level set (data structures), Active contour model, Pixel, Computer science, Function (biology)

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