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Morphological neural networks for localization and mapping

  • I. Villaverde*
  • , M. Graña
  • , A. D'Anjou
  • *Autor correspondiente de este trabajo

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

5 Citas (Scopus)

Resumen

Morphological Associative Memories (MAM) have been proposed for image denosing and pattern recognition, We have shown that they can be applied to other domains, like image retrieval and hyperspectral image unsupervised segmentation. In both cases the key idea is that Morphological Autoassociative Memories (MAAM) selective sensitivity to erosive and dilative noise can be applied to detect the morphological independence between patterns. The convex coordinates obtained by linear unmixing based on the sets of morphological independent patterns define a feature extraction process. These features may be useful either for pattern classification. We present some results on the task of visual landmark recognition for a mobile robot self-localization task.

Idioma originalInglés
Título de la publicación alojadaProceedings of 2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2006
Páginas9-14
Número de páginas6
DOI
EstadoPublicada - 2006
Publicado de forma externa
Evento2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2006 - La Coruna, Espana
Duración: 12 jul 200614 jul 2006

Serie de la publicación

NombreProceedings of 2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2006

Conferencia

Conferencia2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2006
País/TerritorioEspana
CiudadLa Coruna
Período12/07/0614/07/06

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