Abstract
The usual regular moment functions are only invariant to image translation, rotation and uniform scaling. These moment invariants are not invariant when an image is scaled non-uniformly in the x- and y-axes directions. This paper addresses this problem by presenting a new technique to obtain moments that are invariant to non-uniform scaling. However, this technique produces a set of features that arc only invariant to translation and uniform/non-uniform scaling. To obtain invariance to rotation, moments are calculated with respect to the x-y-axis of the image. To perform this, a neural network is used to estimate the angle of rotation from the x-y-axis and the image is unrotated to the x-y-axis. Consequently, we are able to obtain features that are invariant to translation, rotation and uniform/non-uniform scaling. The mathematical background behind the development and invariance of the new moments are presented. The results of experimental studies using English alphabets and Arabic numerals scaled uniformly/non-uniformly, rotated and translated are discussed to further verify the validity of the new moments.
Original language | English |
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Pages (from-to) | 78-87 |
Number of pages | 10 |
Journal | Pattern Analysis and Applications |
Volume | 3 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2000 |
Externally published | Yes |
Keywords
- Neural network
- Non-uniform scaling
- Principal axis
- Regular moments
- Rotation
- Tilt angle