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Descripción
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| 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 | |
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Internacional
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No |
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Nombre congreso
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The Conference of the Spanish Association for Artificial Intelligence |
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Tipo de participación
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960 |
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Lugar del congreso
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San Cristobal de La Laguna. Tenerife |
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Revisores
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Si |
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ISBN o ISSN
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DOI
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Fecha inicio congreso
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06/11/2011 |
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Fecha fin congreso
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11/11/2011 |
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Desde la página
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0 |
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Hasta la página
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10 |
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Título de las actas
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Avances en inteligencia artificial |