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Author: El-Sayed M. El-Alfy

Affiliation: King Fahd University of Petroleum and Minerals, Saudi Arabia

ISBN: 978-989-8425-95-9

Keyword(s): Pattern Recognition, Shape Classification, Industrial Automated Inspection, Neural Networks, Radial-Basis Function Networks.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial Applications of AI ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods ; Vision and Perception

Abstract: This paper describes a system for automatic classification of geometric shapes based on radial-basis function (RBF) neural networks even in the existence of shape deformation. The RBF network model is built using ring-wedge energy features extracted from the Fourier transform of the spatial images of geometric shapes. Using a benchmark dataset, we empirically evaluated and compared the performance of the proposed approach with two other standard classifiers: multi-layer perceptron neural networks and decision trees. The adopted dataset has four geometric shapes (ellipse, triangle, quadrilateral, and pentagon) which may have deformations including rotation, scaling and translation. The empirical results showed that the proposed approach significantly outperforms the other two classification methods with classification error rate around 3.75% on the testing dataset using 5-fold stratified cross validation.

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Paper citation in several formats:
M. El-Alfy, E. (2012). CLASSIFICATION OF DEFORMABLE GEOMETRIC SHAPES - Using Radial-Basis Function Networks and Ring-wedge Energy Features.In Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-95-9, pages 355-362. DOI: 10.5220/0003750603550362

@conference{icaart12,
author={El{-}Sayed M. El{-}Alfy.},
title={CLASSIFICATION OF DEFORMABLE GEOMETRIC SHAPES - Using Radial-Basis Function Networks and Ring-wedge Energy Features},
booktitle={Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2012},
pages={355-362},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003750603550362},
isbn={978-989-8425-95-9},
}

TY - CONF

JO - Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - CLASSIFICATION OF DEFORMABLE GEOMETRIC SHAPES - Using Radial-Basis Function Networks and Ring-wedge Energy Features
SN - 978-989-8425-95-9
AU - M. El-Alfy, E.
PY - 2012
SP - 355
EP - 362
DO - 10.5220/0003750603550362

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