林宁.改进的多尺度Retinex耦合夹角约束的图像匹配算法[J].包装工程,2018,39(23):191-199.
LIN Ning.An Image Matching Algorithm Based on Improved Multi-scale Retinex Coupled Angle Constraint[J].Packaging Engineering,2018,39(23):191-199. |
| 改进的多尺度Retinex耦合夹角约束的图像匹配算法 |
| An Image Matching Algorithm Based on Improved Multi-scale Retinex Coupled Angle Constraint |
| 投稿时间：2018-08-17 修订日期：2018-12-10 |
| DOI：10.19554/j.cnki.1001-3563.2018.23.032 |
| 中文关键词: 图像匹配 多尺度Retinex方法 Harris算法 Haar小波 夹角约束法则 归一化互相关函数 |
| 英文关键词: image matching multi-scale Retinex algorithm Harris algorithm Haar wavelet angle constraint rule normalized cross correlation function |
| 基金项目:广西高校科学技术研究项目基金（KY2015YB525）；2018年度南宁学院科研项目（2018XJ33） |
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| 中文摘要: |
| 目的 解决当前图像匹配算法难以适应缩放等仿射变换图像之间的匹配，导致其鲁棒性以及匹配正确性不佳的问题。方法 提出基于改进多尺度Retinex方法耦合夹角约束法则的图像匹配算法。利用双边滤波代替多尺度Retinex方法中的高斯滤波，对多尺度Retinex方法进行改进，以降低图像中噪声与光晕等因素的影响。随后再引入Harris算法来检测图像的特征，通过求取特征点圆域内的Haar小波响应向量和主方向，并以主方向为起点构建扇区，提取扇区内的灰度特征，以获取相应的特征向量，从而生成特征描述符。通过特征点对应的特征向量构成的夹角，建立夹角约束法则，以完成特征点匹配。最后，利用归一化互相关函数检测错误匹配点，并对匹配效果进行优化。结果 文中算法较当前图像匹配方法，具有更好的匹配正确度以及鲁棒性能，当缩放比例达到50%时，其匹配准确率仍可维持在90.08%左右。结论 文中算法在多种几何攻击下仍具有较高的匹配精度，在图像处理、信息安全等领域具有良好的参考价值。 |
| 英文摘要: |
| The work aims to solve such problems as poor robustness and matching accuracy induced by difficultly in adapting to the scaling of matching between affine transformed images in the current image matching algorithm. An image matching algorithm based on improved multi-scale Retinex coupled angle constraint rule was proposed. The bilateral filtering was used to replace the Gauss filter in multi-scale Retinex method. The multi-scale Retinex method was improved to reduce the influence of noise, halo and other factors in the image. Subsequently, the Harris algorithm was introduced to detect the features of the image. The Haar wavelet response vector and principal direction in the circle domain of feature points were obtained to construct the sector with the principal direction as the starting point. The grayscale feature in the sector was extracted to obtain the corresponding feature vectors, thus generating the feature descriptors. The angle constraint rule was established by the angle of feature vectors corresponding to the feature points. Finally, the error matching points were detected by normalized cross correlation function and the matching effects were optimized. The proposed algorithm had better matching accuracy and robust performance than the current image matching algorithm. When the scaling ratio reached 50%, its matching accuracy could still be maintained at around 90.08%. The proposed algorithm still has higher matching accuracy under various geometric attacks, which has good reference value in image processing, information security and other fields. |
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