一种基于特征点的卷烟商标纸配准方法

A Method of Cigarette Wrapping Paper Image Registration Based on Feature Points

【目的】利用改进的尺度不变特征变换(Scale-Invariant Feature Transform,SIFT)算法提取的匹配特征对卷烟商标纸图像进行细粒度配准,达到提升配准精度和区分真伪卷烟商标纸图像的目的。【方法】通过对图像分块处理、剔除不稳健特征点、单应性矩阵粗配准后根据匹配点距离进行约束筛选匹配对,并提出根据细粒度配准后的匹配点距离均值进行评价,最终实现并改进了基于特征点的卷烟商标纸细粒度图像配准方法。【结果】基于本文改进的特征点检测方法可以提取到更均衡的特征点,提高推定匹配率,提出的配准结果评估标准能有效评估配准质量,粗配准筛选匹配点可以提高图像细粒度配准的精度,并可以对卷烟商标纸图像进行区分。【局限】目前的改进集中在匹配对的筛选,在细粒度配准方法研究上仍有改进的空间。【结论】基于改进的SIFT算法提取的特征点,提出了先粗配准后细配准的图像细粒度配准策略,经实验证明此策略可以提升图像配准精度,并可以达到区分卷烟商标纸图像的目的。

[Objective]In order to improve the registration accuracy and authenticate the wrapping paper images,fine-grained registration is performed on the cigarette packaging images by using the matching feature points extracted from the optimized SIFT(Scale-invariant feature transform)algorithm.[Methods]After performing block processing on the images,removing unstable feature points,and using coarse registration of the homography matrix to filtrate paired points by distance constraint,an evaluation approach is proposed based on average distance betwteen fine-grained matching pairs to improve the registration performance based on SIFT features.[Results]The experimental results show that the improved feature point extraction method in this paper can extract more balanced feature points and improve the estimated matching rate.The proposed registration evaluation standard can effectively evaluate the registration quality,and the coarse registered matching points can improve the accuracy of fine-grained registration of the image and authenticate the wrapping paper images.[Limitations]The current improvement is focused on the selection of matching pairs and there is still room for improvement in the research of fine-grained registration methods.[Conclusions]Experiments prove that this strategy can improve registration accuracy and achieve the purpose of authenticating cigarette wrapping paper images.