Abstract
In this paper a new set of rotation invariant features for image recognition is introduced. The features are the magnitudes of complex Wavelet Transform (WT) of the image. The proposed method offers bigger advantages over Zernike Moment (ZM), for example, the Hamming Distance (HD) between the feature vectors of the different classes are bigger because WT can extract the corresponding local features in the different areas. The performance of the method is experimentally tested on a 26-class data set involving differently oriented binary images. The set consists of 624 images of all English characters. Using Hamming network 99.7% and 98% classification accuracies are obtained respectively by WT and ZM.
| Original language | English |
|---|---|
| Pages (from-to) | 221-224 |
| Number of pages | 4 |
| Journal | National Conference Publication - Institution of Engineers, Australia |
| Volume | 1 |
| Issue number | 94 /9 |
| Publication status | Published - 1994 |
| Event | Proceedings of the International Symposium on Information Theory & Its Applications 1994. Part 1 (of 2) - Sydney, Aust Duration: 1994 Nov 20 → 1994 Nov 24 |
ASJC Scopus subject areas
- General Engineering