Flexible multi-modal graph-based segmentation

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

3 Citations (Scopus)

Abstract

This paper aims at improving the well-known local variance segmentation method by adding extra signal modi and specific processing steps. As a key contribution, we extend the uni-modal segmentation method to perform multi-modal analysis, such that any number of signal modi available can be incorporated in a very flexible way. We have found that the use of a combined weight of luminance and depth values improves the segmentation score by 6.8%, for a large and challenging multi-modal dataset. Furthermore, we have developed an improved uni-modal texture-segmentation algorithm. This improvement relies on a clever choice of the color space and additional pre- and post-processing steps, by which we have increased the segmentation score on a challenging texture dataset by 2.1%. This gain is mainly preserved when using a different dataset with worse lighting conditions and different scene types.
Original languageEnglish
Title of host publicationAdvanced Concepts for Intelligent Vision Systems : 15th International Conference, ACIVS 2013, Poznan, Poland, October 28-31, 2013 : proceedings
EditorsJ. Blanc-Talon, A. Kasinski, W. Philips, D. Popescu, P. Scheunders
Place of PublicationCham
PublisherSpringer
Pages492-503
ISBN (Print)978-3-319-02894-1
DOIs
Publication statusPublished - 2013
Eventconference; ACIVS; 2013-10-28; 2013-10-31 -
Duration: 28 Oct 201331 Oct 2013

Publication series

NameLecture Notes in Computer Science
Volume8192
ISSN (Print)0302-9743

Conference

Conferenceconference; ACIVS; 2013-10-28; 2013-10-31
Period28/10/1331/10/13
OtherACIVS

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