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A closed-form solution for image sequence segmentation with dynamical shape priors

  • University of Bonn

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

In this paper, we address the problem of image sequence segmentation with dynamical shape priors. While existing formulations are typically based on hard decisions, we propose a formalism which allows to reconsider all segmentations of past images. Firstly, we prove that the marginalization over all (exponentially many) reinterpretations of past measurements can be carried out in closed form. Secondly, we prove that computing the optimal segmentation at time t given all images up to t and a dynamical shape prior amounts to the optimization of a convex energy and can therefore optimized globally. Experimental results confirm that for large amounts of noise, the proposed reconsideration of past measurements improves the performance of the tracking method.

Original languageEnglish
Title of host publicationPattern Recognition - 31st DAGM Symposium, Proceedings
Pages31-40
Number of pages10
DOIs
StatePublished - 2009
Externally publishedYes
Event31st Annual Symposium of the Deutsche Arbeitsgemeinschaft fur Mustererkennung, DAGM 2009 - Jena, Germany
Duration: 9 Sep 200911 Sep 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5748 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference31st Annual Symposium of the Deutsche Arbeitsgemeinschaft fur Mustererkennung, DAGM 2009
Country/TerritoryGermany
CityJena
Period9/09/0911/09/09

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