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.
We describe a widely applicable method of grouping -or clustering -image features (such as points, lines, corners, flow vectors and the like). It takes as input a "proximity matrix" H -a square, symmetric matrix of dimension N (where N is the number of features). The element i,j of H is an initial estimate of the "proximity" between the ith and yth features. As output it delivers another square symmetric matrix S whose i-)th element is near to, or much less than unity according as features i and j are to be assigned to the same or different clusters.
To find S we first determine the eigenvalues and eigenvectors ofH and re-express the features as linear combinations of a limited number of these eigenvectors -those with the largest eigenvalues. The cosines between the resulting vectors are the elements ofS. We demonstrate the application of the method to a range of examples and briefly discuss various theoretical and computational issues.In studying various problems in computer vision we have hit upon an apparently novel method of cluster analysis [4] related to a technique widely used in molecular physics [2] [6]. The input to the method is a matrix H of pairwise proximities in, for example, a two dimensional image; the output is closely related to the molecular concept of a "bond order" matrix which indicates whether any two features do or do not belong to the same cluster.
In this paper I introduce a new way of generating and describing shapes which was largely inspired by reading D'Arcy Thompson's classic "On Growth and Form" [1]. There are suggestive parallels between my system and "coupled oscillation" models of handwriting [2] and locomotion [3].
A technique is described for acquiring and imaging faults in random logic devices such as microprocessors and other VLSI chips. Logic states for both faulty and
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.