Fuzzy ARTMAP classification of invariant features derived using angle of rotation from a neural network
Document Type
Article
Publication Date
1-1-2000
Abstract
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.
Keywords
Backpropagation, Computational methods, Feature extraction, Fuzzy sets, Image analysis, Invariance, Learning algorithms, Neural networks
Divisions
fac_eng
Publication Title
Information Sciences
Volume
130
Issue
1-4
Publisher
Elsevier
Additional Information
P.Raveendran, R.Palaniappan. Department of Electrical and Telecommunication, Engineering Faculty, University of Malaya, Kuala Lumpur 50603, Malaysia.