Paper Cut-Out Pattern Recognition Based on Wavelet Moment Invariants

摘要：

Wavelet moment features of image can reflect the images part and whole characteristics and have strong antijamming ability. We use wavelet moments extracted from Paper-cut patterns to get multi-scale features. Combined with the paper-cut images characteristics, the different mean and standard deviation of eigenvector are used to compute resolution and produce N class model feature selection. Finally, the eigenvectors are sent to nearest neighbor classifier for recognition. Experiments show that this method is effective in distinguishing paper cut-cut patterns with noise contamination or geometric deformation.

关键词： wavelet moment invariants feature extraction feature selection patterns recognition

作者: Xiaoyun Wang Guoxiang Li Xianquan Zhang Fangyuan Qin

作者单位: College of Mathematics & Computer Science Yangtze Normal University Fuling Chongqing,China Department of Computer and Information Manufacturing Guangxi University of Finance and Economics Nan Department of Computer Science Guangxi Normal University Guilin Guangxi, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 31-34

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）