基于局部聚类的改进运动网格统计算法的研究

针对常用的匹配点筛选算法效率低、对具有角度和尺度变化匹配图像稳定性差等问题，提出一种基于局部聚类的改进网格运动统计特征点筛选算法。首先，通过局部区域抑制算法筛选响应强度较高且成对出现特征点作为种子点，并以种子点为聚类中心分割图像，得到最小外接矩形作为运动网格；随后把运动网格划分为3×3邻域支持估计量网格，计算运动网格在不同方向上的梯度最大值，作为运动网格的主方向；最后，把待匹配图像邻域支持估计量网格旋转至目标图像运动网格的主方向位置，借助网格运动统计算法筛选匹配。实验表明：对具有JPEG压缩变换、光照变化、模糊变换的匹配图像，所提算法匹配正确率在90%以上；对具有旋转和尺度变换图像，所提算法匹配正确率相较运动网格统计算法提高10%左右，高达40%以上；算法耗时仅为13 min,效率较高；所提算法可稳定高效地筛选正确的匹配点。

Research on improved grid-based motion algorithm based on local clustering

To solve the problems of low efficiency and poor stability of image matching with angle and scale changes, this paper proposed an improved mesh motion statistical feature point screening algorithm based on local clustering. Firstly, as seed points by local region suppression algorithm could screen feature points with high response intensity and paired appearance, and with seed points as the clustering center to obtain the minimum enclosing rectangle as the moving grid could segment images. Then, it divided the motion grid into 3×3 neighborhood supporting estimator grids, and it could calculate the maximum gradient value of the motiongrid in eight directions as the main direction of the motion grid. Finally, it rotated the neighborhood support estimator grid of the image to be matched to the main direction of the target image motion grid, and the mesh motion statistics algorithm usually screened the matches. The experimental results show that the matching accuracy rate of the proposed algorithm is above 90% for matching images with JPEG compression transform, light change and fuzzy transform. For images with rotation and scale transformation, the matching accuracy of the proposed algorithm is about 10% higher than that of the moving grid statistic algorithm, up to more than 70%. The algorithm takes only 13 min and has high efficiency. It shows that the proposed algorithm can screen the correct matching points stably and efficiently.