Kernel density estimation and intrinsic alignment for knowledge-driven segmentation: teaching level sets to walk

Daniel Cremers, Stanley J. Osher, Stefano Soatto

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

46 Scopus citations

Abstract

We address the problem of image segmentation with statistical shape priors in the context of the level set framework. Our paper makes two contributions: Firstly, we propose a novel multi-modal statistical shape prior which allows to encode multiple fairly distinct training shapes. This prior is based on an extension of classical kernel density estimators to the level set domain. Secondly, we propose an intrinsic registration of the evolving level set function which induces an invariance of the proposed shape energy with respect to translation. We demonstrate the advantages of this multi-modal shape prior applied to the segmentation and tracking of a partially occluded walking person.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsCarl Edward Rasmussen, Heinrich H. Bulthoff, Bernhard Scholkopf, Martin A. Giese
PublisherSpringer Verlag
Pages36-44
Number of pages9
ISBN (Print)3540229450, 9783540229452
DOIs
StatePublished - 2004
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3175
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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