一种基于极坐标变换的点模式匹配算法

点模式匹配是目标识别、图像配准与匹配、姿态估计等计算机视觉与模式识别应用方向的基础问题之一。提出了一种新的利用点特征进行匹配的算法,该算法根据点集的分布与点位置信息,构建了点的特征属性图,通过极坐标变换得到对数极坐标的特征图,并利用几何不变矩方法对特征图进行描述。由特征描述向量的比较,获得粗匹配结果,然后通过几何约束迭代的方法获取最终的点集匹配结果。本文贡献如下:一,构建了一种点的极坐标变换特征,并运用不变矩进行描述,使所提特征具有旋转与平移的不变性;二,提出了利用点特征与整体点集几何约束结合的匹配算法,能有效克服出格点与噪声带来的不利影响。最终实验说明了算法的有效性和鲁棒性。

Point pattern matching based on polar coordinate transform

Point pattern matching (PPM) is an important issue in computer vision and pattern recognition, such as target recognition, medical and remote image registration, and pose estimation, and so on. We propose a novel PPM algorithm based on point features. We first construct feature attribute image of the points according to the distribution of point sets and points’ position. Then polar coordinate transform is applied to the feature attribute image, and we describe the feature attribute image with the moment invariants. We get the course matching results by comparing the feature vectors, and an iterative method is introduced for the final matching. We make two contributions in this paper. Firstly, we construct a polar coordinate transform based points features, and describe the features with the moment invariants. The features are invariant to rotation and transportation. Secondly, an iterative matching process by point features and geometry constraint is proposed. The method is insensitive to outliers and noises. Experimental results demonstrate the validity and robustness of the algorithm.