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Neural network classification of symmetrical and nonsymmetrical images using new moments with high noise tolerance

Palaniappan, Ramaswamy, Raveendran, P., Omatu, S. (1999) Neural network classification of symmetrical and nonsymmetrical images using new moments with high noise tolerance. International Journal of Pattern Recognition and Artificial Intelligence, 13 (8). pp. 1233-1250. ISSN 0218-0014. (doi:10.1142/S0218001499000707) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:70777)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1142/S0218001499000707

Abstract

The classification of images using regular or geometric moment functions suffers from two major problems. First, odd orders of central moments give zero value for images with symmetry in the x and/or y directions and symmetry at centroid. Secondly, these moments are very sensitive to noise especially for higher order moments. In this paper, a single solution is proposed to solve both these problems. The solution involves the computation of the moments from a reference point other than the image centroid. The new reference centre is selected such that the invariant properties like translation, scaling and rotation are still maintained. In this paper, it is shown that the new proposed moments can solve the symmetrical problem. Next, we show that the new proposed moments are less sensitive to Gaussian and random noise as compared to two different types of regular moments derived by Hu. Extensive experimental study using a neural network classification scheme with these moments as inputs are conducted to verify the proposed method.

Item Type: Article
DOI/Identification number: 10.1142/S0218001499000707
Additional information: Unmapped bibliographic data: LA - English [Field not mapped to EPrints] J2 - Int J Pattern Recognit Artif Intell [Field not mapped to EPrints] AD - Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia [Field not mapped to EPrints] AD - Dept. of Computer and System Science, College of Engineering, University of Osaka Perfecture, Sakai, Osaka, 593, Japan [Field not mapped to EPrints] AD - University of Malaya, Kuala Lumpur, Malaysia [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Article [Field not mapped to EPrints]
Uncontrolled keywords: Computational geometry, Image analysis, Image quality, Neural networks, Geometric moment functions, Regular moment functions, Object recognition
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 15 Dec 2018 18:21 UTC
Last Modified: 30 May 2019 08:30 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70777 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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