基于掩模搜索的快速尺度不变特征变换图像匹配算法

Image Matching Algorithm for Fast Scale-Invariant Feature Transformation Based on Mask Search

尺度不变特征变换(SIFT)算法在图像匹配领域得到广泛应用,为降低其计算复杂度,提出了一种基于掩模(Mask)搜索的SIFT快速图像匹配算法。首先,分析图像的纹理信息,使用Harris算法的角点响应函数(CRF)对图像进行分区,将纹理复杂度较高的区域作为Mask并生成Mask金字塔,以减小特征点的搜索空间;其次,在极坐标系下建立7区域的圆形描述子,并降低其维度;最后,根据特征点极值类别进行同类匹配,以降低匹配复杂度。实验结果表明,采用Mask的特征搜索方法以损失较小匹配质量为代价,能够有效提升算法的整体速度,结合改进的描述子和极值分类算法可以进一步提升算法速度。采用Mask的特征搜索方法在对匹配效率有较高要求的领域具有潜在的应用价值。

The scale-invariant feature transform(SIFT)algorithm is widely used in image matching.To reduce its computational complexity,a fast SIFT image matching algorithm based on mask search is proposed.First,the texture information of the image is analyzed,and the corner response function(CRF)of the Harris algorithm is used to divide the image.The regions with higher texture complexity are used as a Mask to generate a Mask pyramid,therefore reducing the search space of the feature points;then,a seven-zone circular descriptor is established in the polar coordinate system and its dimension is reduced.Finally,the same kind matching is carried out according to the extreme value category of feature points to reduce the matching complexity.Experimental results show that the method using Mask feature searching can improve the overall speed of the algorithm at the cost of low matching quality,and it can further improve the speed of the algorithm combined with the improved descriptor and extreme value classification algorithm.Therefore,the proposed algorithm has the potential value in the application of high matching efficiency requirements.