一种透视不变的图像匹配算法

针对ASIFT (Affine scale invariant feature transform) 算法存在的仿射采样策略、采样点离散设置等问题,提出了一种基于粒子群优化的图像透视不变特征PSIFT (Perspective scale invariant feature transform)算法. 该算法通过虚拟相机的透视采样来模拟景物在多视角图像中的变形. 在此基础上,将图像匹配问题转换为透视变换的优化问题,并以粒子群算法为工具,研究了虚拟相机旋转参数搜索空间、适应值函数的合理设定. 针对三组不同类型低空遥感图像的实验结果表明,该算法比ASIFT、SIFT (Scale invariant feature transform)、Harris affine和MSER (Maximally stable extremal regions)等算法获得更多的特征匹配对,有效地提高了算法对视角变化的鲁棒性.

A Perspective Invariant Image Matching Algorithm

To solve the problem of affine transform and discrete sampling in ASIFT (Affine scale invariant feature transform), the PSIFT (Perspective scale invariant feature transform), which is based on particle swarm optimization, is proposed in this paper. The proposed algorithm uses a virtual camera and homographic transform to simulate perspective distortion among multi-view images. Therefore, particle swarm optimization is employed to determine the appropriate homography, which is decomposed into three rotation matrices. Experimental results obtained on three categories of low-altitude remote sensing images show that the proposed method outperforms significantly the state-of-the-art ASIFT, SIFT, Harris-affine and MSER, especially when images suffer severe perspective distortion.