基于改进多尺度乘积LoG算子的仿射不变形状匹配算法

Affine-invariant shape matching algorithm based on modified multi-scale product Laplacian of Gaussian operator

目标在成像过程中发生的几何变形多数情况下可用仿射变换来描述。据此,提出一种利用角点进行仿射不变形状匹配的算法。首先引入多尺度乘积LoG(MPLoG)算子检测轮廓角点,并根据角点间距自适应地提取轮廓特征点,从而获取形状关键特征;为解决目标的仿射变形问题,采用Grassmann流形Gr(2,n)来表征和度量两形状之间的相似度;最后通过迭代式序列移位匹配算法来克服Grassmann流形对起始点的依赖并完成形状的匹配。对形状数据进行仿真实验的结果表明,所提算法能够有效地实现形状检索和识别,并对噪声有较强的鲁棒性。

Geometric transforms of the object in the imaging process can be represented by affine transform in most situations. Therefore, a method for shape matching using comers was proposed. Firstly, the comer of contour using Multiscale Product Laplacian of Gaussian （MPLoG） operator was detected, and the feature points based on comer interval were adaptively extracted to obtain the key feature of shape. In order to cope with affine transform, the similarity of two shapes on Grassmann manifold Gr（ 2, n） were represented and measured. Finally, the iterative sequence shift matching was adopted for overcoming the dependency of Grassmann manifold on the starting point, and achieving shape matching. The proposed algorithm was tested on the database of shapes. The simulation results show that the proposed method can achieve shape recognition and retrieval effectively, and it has strong robustness against noise.