From ramp discontinuities to segmentation tree

Emre Akbas, Narendra Ahuja

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a new algorithm for low-level multiscale segmentation of images. The algorithm is designed to detect image regions regardless of their shapes, sizes, and levels of interior homogeneity, by doing a multiscale analysis without assuming any prior models of region geometry. As in previous work, a region is modeled as a homogeneous set of connected pixels surrounded by ramp discontinuities. A new transform, called the ramp transform, is described, which is used to detect ramp discontinuities and seeds for all regions in an image. Region seeds are grown towards the ramp discontinuity areas by utilizing a relaxation labeling procedure. Segmentation is achieved by analyzing the output of this procedure at multiple photometric scales. Finally, all detected regions are organized into a tree data structure based on their recursive containment relations. Experiments on real and synthetic images verify the desired properties of the proposed algorithm.

Original languageEnglish (US)
Title of host publicationComputer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers
Pages123-134
Number of pages12
EditionPART 1
DOIs
StatePublished - Dec 29 2010
Event9th Asian Conference on Computer Vision, ACCV 2009 - Xi'an, China
Duration: Sep 23 2009Sep 27 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5994 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th Asian Conference on Computer Vision, ACCV 2009
CountryChina
CityXi'an
Period9/23/099/27/09

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Akbas, E., & Ahuja, N. (2010). From ramp discontinuities to segmentation tree. In Computer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers (PART 1 ed., pp. 123-134). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5994 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-12307-8_12