Abstract

Factor graphs were first studied in the context of error correction decoding and have since been shown to be a useful tool in a wide variety of applications. In this paper, we provide a brief introduction to factor graphs with an emphasis on their broad applicability, and then describe a new algorithm for segmenting binary images that have been blurred and corrupted by additive white Gaussian noise. Though the algorithm is developed for this particular class of images, generalizations are immediate. Simulation results detail the performance of the algorithm for images in three separate blurring conditions. The results suggest the potential for this approach, providing additional evidence of the usefulness of the factor graph framework.

Original languageEnglish (US)
Title of host publicationProceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003
PublisherIEEE Computer Society
Pages150-153
Number of pages4
ISBN (Electronic)0780379977
DOIs
StatePublished - 2003
EventIEEE Workshop on Statistical Signal Processing, SSP 2003 - St. Louis, United States
Duration: Sep 28 2003Oct 1 2003

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings
Volume2003-January

Other

OtherIEEE Workshop on Statistical Signal Processing, SSP 2003
CountryUnited States
CitySt. Louis
Period9/28/0310/1/03

Keywords

  • Additive white noise
  • Decoding
  • Error correction
  • Filtering
  • Graphical models
  • Image segmentation
  • Kalman filters
  • Machine vision
  • Pattern analysis
  • Sum product algorithm

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications

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