Segmentation of noisy images using the rank m-type l-filter and the fuzzy c-means clustering algorithm

Dante Mújica-Vargas, Francisco J. Gallegos-Funes, Rene Cruz-Santiago

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

2 Scopus citations

Abstract

In this paper we present an image processing scheme to segment noisy images based on a robust estimator in the filtering stage and the standard Fuzzy C-Means (FCM) clustering algorithm to segment the images. The main objective of paper is to evaluate the performance of the Rank M-type L-filter with different influence functions and to establish a reference base to include the filter in the objective function of FCM algorithm in a future work. The filter uses the Rank M-type (RM) estimator in the scheme of L-filter, to get more robustness in the presence of different types of noises and a combination of them. Tests were made on synthetic and real images subjected to three types of noise and the results are compared with six reference modified Fuzzy C-Means methods to segment noisy images.

Original languageEnglish
Title of host publicationPattern Recognition - Third Mexican Conference, MCPR 2011, Proceedings
Pages184-193
Number of pages10
DOIs
StatePublished - 2011
Event3rd Mexican Conference on Pattern Recognition, MCPR 2011 - Cancun, Mexico
Duration: 29 Jun 20112 Jul 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6718 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd Mexican Conference on Pattern Recognition, MCPR 2011
Country/TerritoryMexico
CityCancun
Period29/06/112/07/11

Keywords

  • Fuzzy C-Means
  • L-filter
  • RM-estimator
  • noise
  • robust estimators
  • segmentation

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