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

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaPattern Recognition - Third Mexican Conference, MCPR 2011, Proceedings
Páginas184-193
Número de páginas10
DOI
EstadoPublicada - 2011
Evento3rd Mexican Conference on Pattern Recognition, MCPR 2011 - Cancun, México
Duración: 29 jun. 20112 jul. 2011

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen6718 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia3rd Mexican Conference on Pattern Recognition, MCPR 2011
País/TerritorioMéxico
CiudadCancun
Período29/06/112/07/11

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