.png){kind=icon}
返回
Pattern classification by using improved wavelet Compressed Zernike Moments
.png){kind=icon}
PRE
AI
DOI：10.1016/j.amc.2009.02.029.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, an improved Feature Extraction Method (FEM), which selects discriminative feature sets able to lead to high classification rates in pattern recognition tasks, is presented. The resulted features are the wavelet coefficients of an improved compressed signal, consisting of the Zernike moments amplitudes. By applying a straightforward methodology, it is aimed to construct optimal feature vectors in the sense of vector dimensionality and information content for classification purposes. The resulting surrogate feature vector is of lower dimensionality than the original Zernike moment feature vector and thus more appropriate for pattern recognition tasks. Appropriate validation tests have been arranged, in order to investigate the performance of the proposed algorithm by measuring the discriminative power of the new feature vectors despite the information loss. (c) 2009 Elsevier Inc. All rights reserved.
Keyword:
Pattern classification
Zernike moments
Compressed features
Feature extraction
期刊
IF:
3.4
论文数:
2.3W
被引数:
3.3W
机构
引用论文
Invariant character recognition with Zernike and orthogonal Fourier-Mellin moments.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Hydrogen Mobility Europe (H2ME): Vehicle and Hydrogen Refuelling Station Deployment Results欧洲氢能移动 (H2ME): 车辆和加氢站部署结果
OAAI
OAAI分享收藏Bloch surface wave-coupled emission from quantum dots by ensemble and single molecule spectroscopyRSC AdvancesOAAI
RSC AdvancesIF0
OAAI分享收藏
查看更多内容