Observatorio de I+D+i UPM

Descripción
Image segmentation is a process by which an image is partitioned into regions with similar features. Many approaches have been proposed for color image segmentation, but Fuzzy C-Means has been widely used, because it has a good performance in a large class of images. However, it is not adequate for noisy images and it also takes more time for execution as compared to other method as K-means. For this reason, several methods have been proposed to improve these weaknesses. Method like Possibilistic C-Means, Fuzzy Possibilistic C-Means, Robust Fuzzy Possibilistic C-Means and Fuzzy C-Means with Gustafson-Kessel algorithm. In this paper we perform a comparison of these clustering algorithms applied to feature extraction on vineyard images. Segmented images are evaluated using several quality parameters such as the rate of correctly classied area and runtime
Internacional	No
Nombre congreso	The Conference of the Spanish Association for Artificial Intelligence
Tipo de participación	960
Lugar del congreso	San Cristobal de La Laguna. Tenerife
Revisores	Si
ISBN o ISSN
DOI
Fecha inicio congreso	06/11/2011
Fecha fin congreso	11/11/2011
Desde la página	0
Hasta la página	10
Título de las actas	Avances en inteligencia artificial

Esta actividad pertenece a memorias de investigación

• Autor: Christian Correa Farias (UPM)
• Autor: Pilar Barreiro Elorza (UPM)
• Autor: Constantino Valero Ubierna (UPM)

• Creador: Grupo de Investigación: LPF-TAGRALIA: Técnicas Avanzadas en Agroalimentación