Fuzzy ARTMAP classification of invariant features derived using angle of rotation from a neural network

Raveendran, P. and Palaniappan, R. and Omatu, Sigeru (2000) Fuzzy ARTMAP classification of invariant features derived using angle of rotation from a neural network. Information Sciences, 130 (1-4). pp. 67-84. ISSN 0020-0255, DOI https://doi.org/10.1016/S0020-0255(00)00087-6.

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Official URL: https://doi.org/10.1016/S0020-0255(00)00087-6


Conventional regular moment functions have been proposed as pattern sensitive features in image classification and recognition applications. But conventional regular moments are only invariant to translation, rotation and equal scaling. It is shown that the conventional regular moment invariants remain no longer invariant when the image is scaled unequally in the x- and y-axis directions. We address this problem by presenting a technique to make the regular moment functions invariant to unequal scaling. However, the technique produces a set of features that are only invariant to translation, unequal/equal scaling and reflection. They are not invariant to rotation. To make them invariant to rotation, moments are calculated with respect to the principal axis of the image. To perform this, the exact angle of rotation must be known. But the method of using the second-order moments to determine this angle will also be inclusive of an undesired tilt angle. Therefore, in order to correctly determine the amount of rotation, the tilt angle which differs for different scaling factors in the x- and y-axis directions for the particular image must be obtained. In order to solve this problem, a neural network using the back-propagation learning algorithm is trained to estimate the tilt angle of the image and from this the amount of rotation for the image can be determined. Next, the new moments are derived and a Fuzzy ARTMAP network is used to classify these images into their respective classes. Sets of experiments involving images rotated and scaled unequally in the x- and y-axis directions are carried out to demonstrate the validity of the proposed technique.

Item Type: Article
Additional Information: P.Raveendran, R.Palaniappan. Department of Electrical and Telecommunication, Engineering Faculty, University of Malaya, Kuala Lumpur 50603, Malaysia.
Uncontrolled Keywords: Backpropagation; Computational methods; Feature extraction; Fuzzy sets; Image analysis; Invariance; Learning algorithms; Neural networks
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 02 Sep 2021 03:49
Last Modified: 02 Sep 2021 03:49
URI: http://eprints.um.edu.my/id/eprint/26020

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