Wavelet transform based rotation invariant feature extraction in object recognition

Feihu Qi, Dinggang Shen, Lin Quan

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)

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 languageEnglish
Pages (from-to)221-224
Number of pages4
JournalNational Conference Publication - Institution of Engineers, Australia
Volume1
Issue number94 /9
Publication statusPublished - 1994
Externally publishedYes
EventProceedings of the International Symposium on Information Theory & Its Applications 1994. Part 1 (of 2) - Sydney, Aust
Duration: 1994 Nov 201994 Nov 24

ASJC Scopus subject areas

  • Engineering(all)

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