Convergence behavior of the active mask segmentation algorithm

Doru C. Balcan, Gowri Srinivasa, Matthew Fickus, Jelena Kovačević

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

Abstract

We study the convergence behavior of the ActiveMask (AM) framework, originally designed for segmenting punctate image patterns. AM combines the flexibility of traditional active contours, the statistical modeling power of region-growing methods, and the computational efficiency of multiscale and multiresolution methods. Additionally, it achieves experimental convergence to zero-change (fixedpoint) configurations, a desirable property for segmentation algorithms. At its a core lies a voting-based distributing function which behaves as a majority cellular automaton. This paper proposes an empirical measure correlated to the convergence behavior of AM, and provides sufficient theoretical conditions on the smoothing filter operator to enforce convergence.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages453-456
Number of pages4
ISBN (Print)9781424442966
DOIs
StatePublished - 2010
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: Mar 14 2010Mar 19 2010

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
Country/TerritoryUnited States
CityDallas, TX
Period3/14/103/19/10

Keywords

  • Active mask
  • Cellular automata
  • Convergence
  • Segmentation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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