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3D object surface reconstruction using growing self-organised networks

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Resumen

This paper studies the adaptation of growing self-organised neural networks for 3D object surface reconstruction. Nowadays, input devices and filtering techniques obtain 3D point positions from the object surface without connectivity information. Growing self-organised networks can obtain the implicit surface mesh by means of a clustering process over the input data space maintaining at the same time the spatial-topology relations. The influence of using additional point features (e.g. gradient direction) as well as the methodology characterized in this paper have been studied to improve the obtained surface mesh. Keywords: Neural networks, Self-organised networks, Growing cell structures, Growing neural gas, 3D surface reconstruction, Gradient direction

Idioma originalInglés
Título de la publicación alojadaLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditoresAlberto Sanfeliu, Jose Francisco Martinez-Trinidad, Jesus Ariel Carrasco-Ochoa
EditorialSpringer Verlag
Páginas163-170
Número de páginas8
ISBN (versión impresa)3540235272
DOI
EstadoPublicada - 2004
Publicado de forma externa

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen3287
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

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