Multiphase dynamic labeling for variational recognition-driven image segmentation

Daniel Cremers*, Nir Sochen, Christoph Schnörr

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

22 Scopus citations

Abstract

We propose a variational framework for the integration multiple competing shape priors into level set based segmentation schemes. By optimizing an appropriate cost functional with respect to both a level set function and a (vector-valued) labeling function, we jointly generate a segmentation (by the level set function) and a recognition-driven partition of the image domain (by the labeling function) which indicates where to enforce certain shape priors. Our framework fundamentally extends previous work on shape priors in level set segmentation by directly addressing the central question of where to apply which prior. It allows for the seamless integration of numerous shape priors such that - while segmenting both multiple known and unknown objects - the level set process may selectively use specific shape knowledge for simultaneously enhancing segmentation and recognizing shape.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsTomas Pajdla, Jiri Matas
PublisherSpringer Verlag
Pages74-86
Number of pages13
ISBN (Print)3540219811
DOIs
StatePublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3024
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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