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基于ORB与RANSAC融合改进的图像配准

Improved fast Image registration algorithm based on ORB and RANSAC fusion

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摘要

针对旋转不变性二进制描述算法(Oriented Fast and Rotated Brief, ORB)的尺度旋转性配准误差大, 配准率?#31995;图?#38543;机采样一致性(Random Sample Consensus, RANSAC)算法随机性强且不稳定的问题, 提出一种ORB与RANSAC结合的快速特征匹配算法。首先, 对特征点提取方式进行优化选择, 消除特征边缘影响。之后构建简化的金字塔式尺度空间模型, 改进分层图像的尺度空间结构, 减少生成图像层数和数目; 然后采用梯度方向改进传统ORB算法中的主方向提取模式, 提高特征角点主方向的准确性。最后, 通过构建分块随机取样检测的方式改进RANSAC算法, 提高RANSAC算法的稳定性和图像配准的准确性。实验结果表明改进后的ORB和RANSAC融合算法在尺?#32676;?#26059;转配准方面性能有很大提高, 并且配准的精度较传统ORB算法高, 尺度配准精度提高55.41%, 旋转配准精度提高26.66%。满足复杂图像快速精确配准拼接的精?#32676;?#23454;时性要求。

Abstract

In the binary description algorithm (Oriented Fast and Rotated Brief, ORB), scale and rotation cause a great error in the registration, and the registration rate is low. Meanwhile, the RANdom Sample Consensus (RANSAC) algorithm has an instability issue. Therefore, in this study, a fast feature matching algorithm was presented based on ORB with RANSAC. First, the feature point extraction method was optimized to eliminate the influence of feature edges. After constructing a simplified pyramid scale-space model, the scale-space structure of the layered image was improved by reducing the number of generated image layers. Subsequently, the gradient direction was used to improve the main direction extraction mode of the traditional ORB algorithm, and the accuracy of the main direction of the feature angular point was improved. Finally, the RANSAC algorithm was improved by applying block random sampling, which improved the stability and accuracy of image registration. Experimental results reveal that the improved ORB and RANSAC fusion algorithm performance greatly improved in terms of scale and rotation registration, and higher registration precision is exhibited in comparison with traditional ORB. The scale registration accuracy is improved by 55.41%, and the rotational registration accuracy is improved by 26.66%. These results indicate that the proposed algorithm basically meets the accuracy and real-time requirements for fast and accurate registration of complex images.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP391

DOI:10.3788/ope.20192703.0702

所属?#25913;浚?a href='../Journals/JColumnList?cid=892' title='查看该期刊此?#25913;?#19979;其他论文' class='TagKey' target='_blank'>信息科学

基金项目:国家自然科学基金青年基金资助项目(No.61701542);海洋动力遥感与声学重点实验室开放基金资助项目(No.KHYS1402)

收稿日期:2018-08-08

修改稿日期:2018-10-16

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作者单位    点击查看

樊彦国:中国石油大学(华东) 地球科学与技术学院, 山东 青岛 266580
柴江龙:中国石油大学(华东) 地球科学与技术学院, 山东 青岛 266580
许明明:中国石油大学(华东) 地球科学与技术学院, 山东 青岛 266580
王 斌:中国石油大学(华东) 地球科学与技术学院, 山东 青岛 266580
侯秋实:中国石油大学(华东) 地球科学与技术学院, 山东 青岛 266580

联系人作者:樊彦国([email protected])

备注:樊彦国(1965-), 男, 河北望都人, 博士, 教授, 1992年、1998年于武汉测绘科技大学(现武汉大学)分别获得学士学位、硕士学位, 2007年于中国矿业大学(?#26412;?获得博士学位, 现为中国石油大学(华东)地球科学与技术学?#33322;?#25480;、系主任。主要从事遥感图像处理, 数字图像处理方面的研究。

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引用该论文

FAN Yan-guo,CHAI Jiang-long,XU Ming-ming,WANG Bin,HOU Qiu-shi. Improved fast Image registration algorithm based on ORB and RANSAC fusion[J]. Optics and Precision Engineering, 2019, 27(3): 702-717

樊彦国,柴江龙,许明明,王 斌,侯秋实. 基于ORB与RANSAC融合改进的图像配准[J]. 光学 精密工程, 2019, 27(3): 702-717

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