[1]丁南南.墨西哥帽小波和归一化伪Zernike矩的图像配准[J].计算机技术与发展,2014,24(04):72-76.
　DING Nan-nan.Image Registration Based on Mexican-hat Wavelets and Normalized Pseudo-Zernike Moments[J].,2014,24(04):72-76.
点击复制

墨西哥帽小波和归一化伪Zernike矩的图像配准()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
24
期数:
2014年04期
页码:
72-76
栏目:
智能、算法、系统工程
出版日期:
2014-04-30

文章信息/Info

Title:
Image Registration Based on Mexican-hat Wavelets and Normalized Pseudo-Zernike Moments
文章编号:
1673-629X（2014）04-0072-05
作者:
丁南南
中国科学院 长春光学精密机械与物理研究所
Author(s):
DING Nan-nan
关键词:
图像配准; 墨西哥帽小波; 归一化伪Zernike矩; 双向匹配; 迭代加权最小二乘法
Keywords:
image registration; Mexican-hat wavelets; normalized Pseudo-Zernike moments; bidirectional match; iterative weighted least square method
分类号:
TP391.4
文献标志码:
A
摘要:
图像配准是现代图像处理技术中的一项关键技术,在许多实际的应用领域都占有举足轻重的地位。文中介绍了一种结合尺度相互作用模型下墨西哥帽小波和归一化伪Zernike矩的图像配准方法。首先利用尺度相互作用模型下加入尺度因子的墨西哥帽小波分别提取参考图像和实时图像中的特征点,然后利用归一化伪Zernike矩不变量的方法和双向匹配策略对参考图像和实时图像的特征点进行匹配,用迭代加权最小二乘法估算出最佳仿射变换参数,最后用所得变换参数对实时图像进行变换和重采样来实现图像配准。实验结果表明：该算法能够精确提取和匹配特征点,有效地消除误匹配点对,被测加噪实物图像的特征点均方根误差为0.41,达到了像素级配准精度。
Abstract:
Image registration is a key technique in modern image processing,and it is very important in many real applications. A method for image registration combining scale-interaction of Mexican-hat wavelets and normalized Pseudo-Zernike moments is proposed. First, feature points are extracted using scale-interaction of Mexican-hat wavelets in the reference image and sensed image respectively. Then, normalized Pseudo-Zernike moments and a bidirectional matching strategy are used to match them,and iterative weighted least square method is used to estimate the best affine transform parameters. At last,the sensed image is transformed and resampled to accomplish the image registration. The experiment indicates that the proposed algorithm extracts feature points and matches them exactly and eliminates wrong matched points effectively. The RMSE of the feature points of images of practicality with Gaussian noise is 0. 41 and it achieves pixel precision registration result.

相似文献/References:

[1]焦晶萍 廖文和 沈建新.一种基于模板匹配法的眼底图像拼接方法[J].计算机技术与发展,2010,(04):148.
　JIAO Jing-ping,LIAO Wen-he,SHEN Jian-xin.A Fundus Image Mosaic Method Based on Template Matching[J].,2010,(04):148.
[2]翟利志 王敬东 李鹏.基于邻域信息的红外与可见光图像互信息配准[J].计算机技术与发展,2008,(10):151.
　ZHAI Li-zhi,WANG Jing-dong,LI Peng.Infrared and Visible Light Image Mutual Information Registration Based on Neighborhood Information[J].,2008,(04):151.
[3]冯林 颜世鹏 孙焘.图像配准中的一种特定区域轮廓提取算法[J].计算机技术与发展,2006,(03):11.
　FENG Lin,YAN Shi-peng,SUN Tao.A Contour Extraction Algorithm of Special Region in Image Registration[J].,2006,(04):11.
[4]邰伟鹏 栾干 岳建华[].基于轮廓特征匹配的数字人多模态图像配准[J].计算机技术与发展,2006,(07):186.
　TAI Wei-peng,LUAN Gan,YUE Jian-hua.Image Registration Among Multimodal Medical Based on Matching of Contour Characteristic[J].,2006,(04):186.
[5]吴福虎 罗斌 汤进 杨龙.基于边缘相关的红外热像配准[J].计算机技术与发展,2012,(07):88.
　WU Fu-hu,LUO Bin,TANG Jin,et al.Infrared Image Registration Based on Edge Correlation[J].,2012,(04):88.
[6]孙登第 卜令斌 赵海峰 罗斌.基于梯度相似性与Rényi熵图的图像配准算法[J].计算机技术与发展,2012,(12):97.
　SUN Deng-di,BU Ling-bin,ZHAO Hai-feng,et al.Image Registration Based on Rényi Entropic Graph Combined with Gradient Similarity[J].,2012,(04):97.
[7]王凤娇,陈光化,周文. 基于SIFT的POCS图像超分辨率重建[J].计算机技术与发展,2014,24(11):39.
　WANG Feng-jiao,CHEN Guang-hua,ZHOU Wen. Multi-frame Image Super-resolution Reconstruction Based on SIFT[J].,2014,24(04):39.
[8]雷飞,王文学,王雪丽,等. 基于改进SURF的实时视频拼接方法[J].计算机技术与发展,2015,25(03):32.
　LEI Fei,WANG Wen-xue,WANG Xue-li,et al. Real-time Video Stitching Method Based on Improved SURF[J].,2015,25(04):32.
[9]张凯[],杨红雨[][],兰时勇[][].基于CUDA的SIFT特征与拼接缝的全景图生成[J].计算机技术与发展,2015,25(09):22.
　ZHANG Kai[],YANG Hong-yu[][],LAN Shi-yong[][]. Panorama Generation of SIFT and Stitch Line Based on CUDA[J].,2015,25(04):22.
[10]袁媛,滕奇志,何小海,等.岩石薄片图像拼接中的色差校正算法[J].计算机技术与发展,2018,28(07):1.[doi:10.3969/ j. issn.1673-629X.2018.07.001]
　YUAN Yuan,TENG Qi-zhi,HE Xiao-hai,et al. Chromatic Aberration Correction Algorithm for Splicing of Rock Slice[J].,2018,28(04):1.[doi:10.3969/ j. issn.1673-629X.2018.07.001]

更新日期/Last Update: 1900-01-01