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Authors: Hendrik Annuth and Christian-A. Bohn

Affiliation: Wedel University of Applied Sciences, Germany

Keyword(s): Unsupervised learning, Competitive learning, Growing Cell Structures, Surface Reconstruction, SurfaceFitting.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Supervised and Unsupervised Learning ; Theory and Methods

Abstract: Strictly iterative approaches derived from unsupervised artificial neural network (ANN) methods have been surprisingly efficient for the application of surface reconstruction from scattered 3D points. This comes from the facts, that on the one hand, ANN are able to robustly cluster samples of arbitrary dimension, size, and complexity, and on the second hand, ANN algorithms can easily be adjusted to specific applications by inventing simple local learning rules without loosing the robustness and convergence behavior of the basic ANN approach. In this work, we break up the idea of having just an ``adjustment'' of the basic unsupervised ANN algorithm but intrude on the central learning scheme and explicitly use learned topology within the training process. We demonstrate the performance of the novel concept in the area of surface reconstruction. In this work, we break up the idea of having just an “adjustment” of the basic unsupervised ANN algorithm but intrude on the central learning scheme and explicitly use the learned topology within the training process. We demonstrate the performance of the novel concept in the area of surface reconstruction. (More)

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Paper citation in several formats:
Annuth, H. and Bohn, C. (2013). Growing Surface Structures. In Proceedings of the 5th International Joint Conference on Computational Intelligence (IJCCI 2013) - NCTA; ISBN 978-989-8565-77-8; ISSN 2184-3236, SciTePress, pages 349-359. DOI: 10.5220/0004529203490359

@conference{ncta13,
author={Hendrik Annuth. and Christian{-}A. Bohn.},
title={Growing Surface Structures},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence (IJCCI 2013) - NCTA},
year={2013},
pages={349-359},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004529203490359},
isbn={978-989-8565-77-8},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 5th International Joint Conference on Computational Intelligence (IJCCI 2013) - NCTA
TI - Growing Surface Structures
SN - 978-989-8565-77-8
IS - 2184-3236
AU - Annuth, H.
AU - Bohn, C.
PY - 2013
SP - 349
EP - 359
DO - 10.5220/0004529203490359
PB - SciTePress