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Author: Antonio Fernández-Caballero

Affiliation: Universidad de Castilla-La Mancha, Spain

Abstract: A general-purpose neural model that challenges image understanding is presented in this paper. The model incorporates accumulative computation, lateral interaction and double time scale, and can be considered as biologically plausible. The model uses - at global time scale t and in form of accumulative computation - all the necessary mechanisms to detect movement from the grey level change at each pixel of the image. The information on the detected motion is useful as part of an object’s shape can be obtained. On a second time scale base T< pared to the most important current knowledge on motion analysis showing, this way its suitability to most well-known problems in silhouette detection. (More)

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Paper citation in several formats:
Fernández-Caballero, A. (2005). Knowledge-Based Silhouette Detection. In Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems (ICEIS 2005) - PRIS; ISBN 972-8865-28-7, SciTePress, pages 114-123. DOI: 10.5220/0002570001140123

@conference{pris05,
author={Antonio Fernández{-}Caballero},
title={Knowledge-Based Silhouette Detection},
booktitle={Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems (ICEIS 2005) - PRIS},
year={2005},
pages={114-123},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002570001140123},
isbn={972-8865-28-7},
}

TY - CONF

JO - Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems (ICEIS 2005) - PRIS
TI - Knowledge-Based Silhouette Detection
SN - 972-8865-28-7
AU - Fernández-Caballero, A.
PY - 2005
SP - 114
EP - 123
DO - 10.5220/0002570001140123
PB - SciTePress