Abstract

In this paper the registration mapping function is derived for images that are produced by parallel projections. This function has the form F(x, y) = A(x, y) + h(x, y)e, where A(x, y) is an affine transformation, h(x, y) is a scalar-valued function, and e is a vector that defines the epipolar lines. The main result of the paper is the formulation of a normalization constraint that guarantees the uniqueness of the parameters of this function and makes possible their least-squares estimation from a collection of matching points. This approach reduces the search for match points from a two-dimensional to a one-dimensional search along the epipolar lines, thereby increasing the accuracy and robustness of image registration. Simulation results are presented that demonstrate the validity of this approach for nonparallel as well as for parallel imaging geometries. Subpixel registration accuracy is possible for perspective projections as long as either the field of view or the separation angle between the two images is small.

© 1993 Optical Society of America

Registration of eye reflection and scene images using an aspherical eye model

Atsushi Nakazawa, Christian Nitschke, and Toyoaki Nishida
J. Opt. Soc. Am. A 33(11) 2264-2276 (2016)

Solution to the three-dimensional image reconstruction problem from two-dimensional parallel projections

M. Defrise, R. Clack, and D. Townsend
J. Opt. Soc. Am. A 10(5) 869-877 (1993)

Parallel optimization of tridimensional deformation measurement based on correlation function constraints of a multi-camera network

Guiyang Zhang, Liang Wei, Bin Zhang, Xing Zhou, and Ju Huo
Appl. Opt. 61(32) 9311-9323 (2022)

Retinal image registration via feature-guided Gaussian mixture model

Chengyin Liu, Jiayi Ma, Yong Ma, and Jun Huang
J. Opt. Soc. Am. A 33(7) 1267-1276 (2016)

Image recovery by convex projections using a least-squares constraint

Christine I. Podilchuk and Richard J. Mammone
J. Opt. Soc. Am. A 7(3) 517-521 (1990)