Uncertainty driven multi-scale optimization

Pushmeet Kohli, Victor Lempitsky, Carsten Rother

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

9 Citations (Scopus)

Abstract

This paper proposes a new multi-scale energy minimization algorithm which can be used to efficiently solve large scale labelling problems in computer vision. The basic modus operandi of any multi-scale method involves the construction of a smaller problem which can be solved efficiently. The solution of this problem is used to obtain a partial labelling of the original energy function, which in turn allows us to minimize it by solving its (much smaller) projection. We propose the use of new techniques for both the construction of the smaller problem, and the extraction of a partial solution. Experiments on image segmentation show that our techniques give solutions with low pixel labelling error and in the same or less amount of computation time, compared to traditional multi-scale techniques.

Original languageEnglish
Title of host publicationPattern Recognition - 32nd DAGM Symposium, Proceedings
Pages242-251
Number of pages10
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event32nd Annual Symposium of the German Association for Pattern Recognition, DAGM 2010 - Darmstadt, Germany
Duration: 22 Sep 201024 Sep 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6376 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference32nd Annual Symposium of the German Association for Pattern Recognition, DAGM 2010
Country/TerritoryGermany
CityDarmstadt
Period22/09/1024/09/10

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