Performance of Object Classification Using Zernike Moment

查看全文 作　　者：Ariffuddin [1]Joret;Mohammad Faiz Liew [1]Abdullah;Muhammad Suhaimi [1]Sulong;Asmarashid [1]Ponniran;Siti Zuraidah [1]Zainudin

机构地区：[1]the Faculty of Electrical and Electronic Engineering,Universiti Tun Hussein Onn Malaysia

出　　处：《Journal of Electronic Science and Technology》索引2014年第12卷第1期,共5页

基　　金：supported by the Ministry of Higher Education Malaysia under Fundamental Research Grant No.0719 摘　　要：Moments have been used in all sorts of object classification systems based on image. There are lots of moments studied by many researchers in the area of object classification and one of the most preference moments is the Zernike moment. In this paper, the performance of object classification using the Zernike moment has been explored. The classifier based on neural networks has been used in this study. The results indicate the best performance in identifying the aggregate is at 91.4% with a ten orders of the Zernike moment. This encouraging result has shown that the Zernike moment is a suitable moment to be used as a feature of object classification systems. 关键词：ZERNIKE矩最佳性能目标分类分类系统对象分类图像目标研究人员神经网络

相关文献

参考文献（19）
引证文献（0）
耦合文献（2）

参考文献(19)

耦合文献(2)

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费

Performance of Object Classification Using Zernike Moment

参考文献(19)

耦合文献(2)

用户提示