Scott G L, Longuet-Higgins H C
Department of Engineering Science, University of Oxford, U.K.
Proc Biol Sci. 1991 Apr 22;244(1309):21-6. doi: 10.1098/rspb.1991.0045.
In this paper we describe an algorithm that operates on the distances between features in the two related images and delivers a set of correspondences between them. The algorithm maximizes the inner product of two matrices, one of which is the desired 'pairing matrix' and the other a 'proximity matrix' with elements exp (-rij2/2 sigma 2), where rij is the distance between two features, one in each image, and sigma is an adjustable scale parameter. The output of the algorithm may be compared with the movements that people perceive when viewing two images in quick succession, and it is found that an increase in sigma affects the computed correspondences in much the same way as an increase in interstimulus interval alters the perceived displacements. Provided that sigma is not too small the algorithm will recover the feature mappings that result from image translation, expansion or shear deformation--transformations of common occurrence in image sequences--even when the displacements of individual features depart slightly from the general trend.
在本文中，我们描述了一种算法，该算法基于两个相关图像中特征之间的距离进行操作，并给出它们之间的一组对应关系。该算法使两个矩阵的内积最大化，其中一个矩阵是期望的“配对矩阵”，另一个是“邻近矩阵”，其元素为exp(-rij2/2σ2)，其中rij是两个特征之间的距离，每个图像中有一个特征，σ是一个可调整的比例参数。该算法的输出可以与人们在快速连续查看两个图像时感知到的运动进行比较，并且发现σ的增加对计算出的对应关系的影响，与刺激间隔的增加改变感知位移的方式非常相似。只要σ不太小，该算法就能恢复由图像平移、缩放或剪切变形（这些是图像序列中常见的变换）产生的特征映射，即使单个特征的位移略微偏离总体趋势。
