3D object surface reconstruction using growing self-organised networks

Carmen Alonso-Montes; Manuel Francisco González Penedo

doi:10.1007/978-3-540-30463-0_20

3D object surface reconstruction using growing self-organised networks

Carmen Alonso-Montes^*
, Manuel Francisco González Penedo

^*Corresponding author for this work

University of A Coruna

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

1 Citation (Scopus)

Abstract

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

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Alberto Sanfeliu, Jose Francisco Martinez-Trinidad, Jesus Ariel Carrasco-Ochoa
Publisher	Springer Verlag
Pages	163-170
Number of pages	8
ISBN (Print)	3540235272
DOIs	https://doi.org/10.1007/978-3-540-30463-0_20
Publication status	Published - 2004
Externally published	Yes

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3287
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/978-3-540-30463-0_20

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Alonso-Montes, C., & Penedo, M. F. G. (2004). 3D object surface reconstruction using growing self-organised networks. In A. Sanfeliu, J. F. Martinez-Trinidad, & J. A. Carrasco-Ochoa (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 163-170). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3287). Springer Verlag. https://doi.org/10.1007/978-3-540-30463-0_20

Alonso-Montes, Carmen ; Penedo, Manuel Francisco González. / 3D object surface reconstruction using growing self-organised networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Alberto Sanfeliu ; Jose Francisco Martinez-Trinidad ; Jesus Ariel Carrasco-Ochoa. Springer Verlag, 2004. pp. 163-170 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "3D object surface reconstruction using growing self-organised networks",

abstract = "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",

author = "Carmen Alonso-Montes and Penedo, \{Manuel Francisco Gonz{\'a}lez\}",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "Alberto Sanfeliu and Martinez-Trinidad, \{Jose Francisco\} and Carrasco-Ochoa, \{Jesus Ariel\}",

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Alonso-Montes, C & Penedo, MFG 2004, 3D object surface reconstruction using growing self-organised networks. in A Sanfeliu, JF Martinez-Trinidad & JA Carrasco-Ochoa (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3287, Springer Verlag, pp. 163-170. https://doi.org/10.1007/978-3-540-30463-0_20

3D object surface reconstruction using growing self-organised networks. / Alonso-Montes, Carmen; Penedo, Manuel Francisco González.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Alberto Sanfeliu; Jose Francisco Martinez-Trinidad; Jesus Ariel Carrasco-Ochoa. Springer Verlag, 2004. p. 163-170 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3287).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AU - Penedo, Manuel Francisco González

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N2 - 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

AB - 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

UR - https://www.scopus.com/pages/publications/35048853587

U2 - 10.1007/978-3-540-30463-0_20

DO - 10.1007/978-3-540-30463-0_20

M3 - Chapter

AN - SCOPUS:35048853587

SN - 3540235272

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 163

EP - 170

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Sanfeliu, Alberto

A2 - Martinez-Trinidad, Jose Francisco

A2 - Carrasco-Ochoa, Jesus Ariel

PB - Springer Verlag

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Alonso-Montes C, Penedo MFG. 3D object surface reconstruction using growing self-organised networks. In Sanfeliu A, Martinez-Trinidad JF, Carrasco-Ochoa JA, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. p. 163-170. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-30463-0_20

3D object surface reconstruction using growing self-organised networks

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