Modified fuzzy c-means applied to a Bragg grating-based spectral imager for material clustering

Aida Rodríguez*, Juan Luis Nieves, Eva Valero, Estíbaliz Garrote, Javier Hernández-Andrés, Javier Romero

*Autor correspondiente de este trabajo

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

4 Citas (Scopus)

Resumen

We have modified the Fuzzy C-Means algorithm for an application related to segmentation of hyperspectral images. Classical fuzzy c-means algorithm uses Euclidean distance for computing sample membership to each cluster. We have introduced a different distance metric, Spectral Similarity Value (SSV), in order to have a more convenient similarity measure for reflectance information. SSV distance metric considers both magnitude difference (by the use of Euclidean distance) and spectral shape (by the use of Pearson correlation). Experiments confirmed that the introduction of this metric improves the quality of hyperspectral image segmentation, creating spectrally more dense clusters and increasing the number of correctly classified pixels.

Idioma originalInglés
Título de la publicación alojadaProceedings of SPIE-IS and T Electronic Imaging - Image Processing
Subtítulo de la publicación alojadaMachine Vision Applications V
DOI
EstadoPublicada - 2012
EventoImage Processing: Machine Vision Applications V - Burlingame, CA, Estados Unidos
Duración: 25 ene 201225 ene 2012

Serie de la publicación

NombreProceedings of SPIE - The International Society for Optical Engineering
Volumen8300
ISSN (versión impresa)0277-786X

Conferencia

ConferenciaImage Processing: Machine Vision Applications V
País/TerritorioEstados Unidos
CiudadBurlingame, CA
Período25/01/1225/01/12

Huella

Profundice en los temas de investigación de 'Modified fuzzy c-means applied to a Bragg grating-based spectral imager for material clustering'. En conjunto forman una huella única.

Citar esto