Combined Features of Cubic B-Spline Wavelet Moments and Zernike Moments for Invariant Character Recognition

Authors:
Chao Kan
Affiliations:
-
Venue:
ITCC '01 Proceedings of the International Conference on Information Technology: Coding and Computing
Year:
2001

Citing 0
Cited 5

Rotationally invariant filter bank for pattern recognition of noisy images

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Translation and scale invariants of Tchebichef moments

Pattern Recognition
Numerical experiments on the accuracy of rotation moments invariants

Image and Vision Computing
Discriminative Zernike and Pseudo Zernike Moments for Face Recognition

International Journal of Computer Vision and Image Processing
A novel free format Persian/Arabic handwritten zip code recognition system

Computers and Electrical Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

Abstract: In this paper, a new method of combining cubic B-spline Wavelet Moments (WMs) and Zernike Moments (ZMs) into a common feature vector is proposed for invariant pattern classifi-cation. By doing so, the ability of ZMs to capture global features and WMs to differentiate between subtle variations in description can be utilized at the same time. Analysis and simulations verify that the new method achieves better performance with respect to classification accuracy than using ZMs or WMs separately. In addition, this new method should also be applicable to other areas of pattern recognition.